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.

Inter-annual Variability in Global Suspended Particulate Inorganic Carbon Inventory Using Space-based Measurements

NASA Astrophysics Data System (ADS)

Hopkins, J.; Balch, W. M.; Henson, S.; Poulton, A. J.; Drapeau, D.; Bowler, B.; Lubelczyk, L.

2016-02-01

Coccolithophores, the single celled phytoplankton that produce an outer covering of calcium carbonate coccoliths, are considered to be the greatest contributors to the global oceanic particulate inorganic carbon (PIC) pool. The reflective coccoliths scatter light back out from the ocean surface, enabling PIC concentration to be quantitatively estimated from ocean color satellites. Here we use datasets of AQUA MODIS PIC concentration from 2003-2014 (using the recently-revised PIC algorithm), as well as statistics on coccolithophore vertical distribution derived from cruises throughout the world ocean, to estimate the average global (surface and integrated) PIC standing stock and its associated inter-annual variability. In addition, we divide the global ocean into Longhurst biogeochemical provinces, update the PIC biomass statistics and identify those regions that have the greatest inter-annual variability and thus may exert the greatest influence on global PIC standing stock and the alkalinity pump.

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.

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

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.

Disentangling Global Warming, Multidecadal Variability, and El Niño in Pacific Temperatures

NASA Astrophysics Data System (ADS)

Wills, Robert C.; Schneider, Tapio; Wallace, John M.; Battisti, David S.; Hartmann, Dennis L.

2018-03-01

A key challenge in climate science is to separate observed temperature changes into components due to internal variability and responses to external forcing. Extended integrations of forced and unforced climate models are often used for this purpose. Here we demonstrate a novel method to separate modes of internal variability from global warming based on differences in time scale and spatial pattern, without relying on climate models. We identify uncorrelated components of Pacific sea surface temperature variability due to global warming, the Pacific Decadal Oscillation (PDO), and the El Niño-Southern Oscillation (ENSO). Our results give statistical representations of PDO and ENSO that are consistent with their being separate processes, operating on different time scales, but are otherwise consistent with canonical definitions. We isolate the multidecadal variability of the PDO and find that it is confined to midlatitudes; tropical sea surface temperatures and their teleconnections mix in higher-frequency variability. This implies that midlatitude PDO anomalies are more persistent than previously thought.

A global distributed basin morphometric dataset

NASA Astrophysics Data System (ADS)

Shen, Xinyi; Anagnostou, Emmanouil N.; Mei, Yiwen; Hong, Yang

2017-01-01

Basin morphometry is vital information for relating storms to hydrologic hazards, such as landslides and floods. In this paper we present the first comprehensive global dataset of distributed basin morphometry at 30 arc seconds resolution. The dataset includes nine prime morphometric variables; in addition we present formulas for generating twenty-one additional morphometric variables based on combination of the prime variables. The dataset can aid different applications including studies of land-atmosphere interaction, and modelling of floods and droughts for sustainable water management. The validity of the dataset has been consolidated by successfully repeating the Hack's law.

Regional Climate Simulation and Data Assimilation with Variable-Resolution GCMs

NASA Technical Reports Server (NTRS)

Fox-Rabinovitz, Michael S.

2002-01-01

Variable resolution GCMs using a global stretched grid (SG) with enhanced regional resolution over one or multiple areas of interest represents a viable new approach to regional climateklimate change and data assimilation studies and applications. The multiple areas of interest, at least one within each global quadrant, include the major global mountains and major global monsoonal circulations over North America, South America, India-China, and Australia. They also can include the polar domains, and the European and African regions. The SG-approach provides an efficient regional downscaling to mesoscales, and it is an ideal tool for representing consistent interactions of globaYlarge- and regionallmeso- scales while preserving the high quality of global circulation. Basically, the SG-GCM simulations are no different from those of the traditional uniform-grid GCM simulations besides using a variable-resolution grid. Several existing SG-GCMs developed by major centers and groups are briefly described. The major discussion is based on the GEOS (Goddard Earth Observing System) SG-GCM regional climate simulations.

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.

Setting the scene for SWOT: global maps of river reach hydrodynamic variables

NASA Astrophysics Data System (ADS)

Schumann, Guy J.-P.; Durand, Michael; Pavelsky, Tamlin; Lion, Christine; Allen, George

2017-04-01

Credible and reliable characterization of discharge from the Surface Water and Ocean Topography (SWOT) mission using the Manning-based algorithms needs a prior estimate constraining reach-scale channel roughness, base flow and river bathymetry. For some places, any one of those variables may exist locally or even regionally as a measurement, which is often only at a station, or sometimes as a basin-wide model estimate. However, to date none of those exist at the scale required for SWOT and thus need to be mapped at a continental scale. The prior estimates will be employed for producing initial discharge estimates, which will be used as starting-guesses for the various Manning-based algorithms, to be refined using the SWOT measurements themselves. A multitude of reach-scale variables were derived, including Landsat-based width, SRTM slope and accumulation area. As a possible starting point for building the prior database of low flow, river bathymetry and channel roughness estimates, we employed a variety of sources, including data from all GRDC records, simulations from the long-time runs of the global water balance model (WBM), and reach-based calculations from hydraulic geometry relationships as well as Manning's equation. Here, we present the first global maps of this prior database with some initial validation, caveats and prospective uses.

Increasing importance of precipitation variability on global livestock grazing lands

NASA Astrophysics Data System (ADS)

Sloat, Lindsey L.; Gerber, James S.; Samberg, Leah H.; Smith, William K.; Herrero, Mario; Ferreira, Laerte G.; Godde, Cécile M.; West, Paul C.

2018-03-01

Pastures and rangelands underpin global meat and milk production and are a critical resource for millions of people dependent on livestock for food security1,2. Forage growth, which is highly climate dependent3,4, is potentially vulnerable to climate change, although precisely where and to what extent remains relatively unexplored. In this study, we assess climate-based threats to global pastures, with a specific focus on changes in within- and between-year precipitation variability (precipitation concentration index (PCI) and coefficient of variation of precipitation (CVP), respectively). Relating global satellite measures of vegetation greenness (such as the Normalized Difference Vegetation Index; NDVI) to key climatic factors reveals that CVP is a significant, yet often overlooked, constraint on vegetation productivity across global pastures. Using independent stocking data, we found that areas with high CVP support lower livestock densities than less-variable regions. Globally, pastures experience about a 25% greater year-to-year precipitation variation (CVP = 0.27) than the average global land surface area (0.21). Over the past century, CVP has generally increased across pasture areas, although both positive (49% of pasture area) and negative (31% of pasture area) trends exist. We identify regions in which livestock grazing is important for local food access and economies, and discuss the potential for pasture intensification in the context of long-term regional trends in precipitation variability.

Improving Global Vascular Risk Prediction with Behavioral and Anthropometric Factors: The Multi-ethnic Northern Manhattan Cohort Study

PubMed Central

Sacco, Ralph L.; Khatri, Minesh; Rundek, Tatjana; Xu, Qiang; Gardener, Hannah; Boden-Albala, Bernadette; Di Tullio, Marco R.; Homma, Shunichi; Elkind, Mitchell SV; Paik, Myunghee C

2010-01-01

Objective To improve global vascular risk prediction with behavioral and anthropometric factors. Background Few cardiovascular risk models are designed to predict the global vascular risk of MI, stroke, or vascular death in multi-ethnic individuals, and existing schemes do not fully include behavioral risk factors. Methods A randomly-derived, population-based, prospective cohort of 2737 community participants free of stroke and coronary artery disease were followed annually for a median of 9.0 years in the Northern Manhattan Study (mean age 69 years; 63.2% women; 52.7% Hispanic, 24.9% African-American, 19.9% white). A global vascular risk score (GVRS) predictive of stroke, myocardial infarction, or vascular death was developed by adding variables to the traditional Framingham cardiovascular variables based on the likelihood ratio criterion. Model utility was assessed through receiver operating characteristics, calibration, and effect on reclassification of subjects. Results Variables which significantly added to the traditional Framingham profile included waist circumference, alcohol consumption, and physical activity. Continuous measures for blood pressure and fasting blood sugar were used instead of hypertension and diabetes. Ten -year event-free probabilities were 0.95 for the first quartile of GVRS, 0.89 for the second quartile, 0.79 for the third quartile, and 0.56 for the fourth quartile. The addition of behavioral factors in our model improved prediction of 10 -year event rates compared to a model restricted to the traditional variables. Conclusion A global vascular risk score that combines both traditional, behavioral, and anthropometric risk factors, uses continuous variables for physiological parameters, and is applicable to non-white subjects could improve primary prevention strategies. PMID:19958966

On the Interannual Variability and on Trends of the Temperature in the Middle Atmosphere

NASA Technical Reports Server (NTRS)

Labitzke, K.; Naujokat, B.

1985-01-01

The new Reference Atmosphere presented here is based on global satellite data and forms a very useful basis for climatological studies. When using such climatologies it is important to be aware of the well known interannual variability which n themiddle atmosphere is particularly large during the northern winters and southern springs. Variability ofthe upper and lower stratospheres is discussed in detail. Areas covered included the polar region and the middile and lower latitudes. Temperature trends, notably the alteration of the global temperature structure by a number of anthropogenically influenced tract gases or the greenhouse effect is discussed.

Analysis of global oceanic rainfall from microwave data

NASA Technical Reports Server (NTRS)

Rao, M.

1978-01-01

A Global Rainfall Atlas was prepared from Nimbus 5 ESMR data. The Atlas includes global oceanic rainfall maps based on weekly, monthly and seasonal averages, complete through the end of 1975. Similar maps for 1973 and 1974 were studied. They reveal several previously unknown areas of enhanced rainfall and preliminary data on interannual variability of oceanic rainfall.

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

Compensatory Water Effects Link Yearly Global Land CO2 Sink Changes to Temperature

NASA Technical Reports Server (NTRS)

Jung, Martin; Reichstein, Markus; Tramontana, Gianluca; Viovy, Nicolas; Schwalm, Christopher R.; Wang, Ying-Ping; Weber, Ulrich; Weber, Ulrich; Zaehle, Soenke; Zeng, Ning;

Effects of Recent Regional Soil Moisture Variability on Global Net Ecosystem CO2 Exchange

NASA Astrophysics Data System (ADS)

Jones, L. A.; Madani, N.; Kimball, J. S.; Reichle, R. H.; Colliander, A.

2017-12-01

Soil moisture exerts a major regional control on the inter-annual variability of the global land sink for atmospheric CO2. In semi-arid regions, annual biomass production is closely coupled to variability in soil moisture availability, while in cold-season-affected regions, summer drought offsets the effects of advancing spring phenology. Availability of satellite solar-induced fluorescence (SIF) observations and improvements in atmospheric inversions has led to unprecedented ability to monitor atmospheric sink strength. However, discrepancies still exist between such top-down estimates as atmospheric inversion and bottom-up process and satellite driven models, indicating that relative strength, mechanisms, and interaction of driving factors remain poorly understood. We use soil moisture fields informed by Soil Moisture Active Passive Mission (SMAP) observations to compare recent (2015-2017) and historic (2000-2014) variability in net ecosystem land-atmosphere CO2 exchange (NEE). The operational SMAP Level 4 Carbon (L4C) product relates ground-based flux tower measurements to other bottom-up and global top-down estimates to underlying soil moisture and other driving conditions using data-assimilation-based SMAP Level 4 Soil Moisture (L4SM). Droughts in coastal Brazil, South Africa, Eastern Africa, and an anomalous wet period in Eastern Australia were observed by L4C. A seasonal seesaw pattern of below-normal sink strength at high latitudes relative to slightly above-normal sink strength for mid-latitudes was also observed. Whereas SMAP-based soil moisture is relatively informative for short-term temporal variability, soil moisture biases that vary in space and with season constrain the ability of the L4C estimates to accurately resolve NEE. Such biases might be caused by irrigation and plant-accessible ground-water. Nevertheless, SMAP L4C daily NEE estimates connect top-down estimates to variability of effective driving factors for accurate estimates of regional-to-global land-atmosphere CO2 exchange.

Extremal entanglement and mixedness in continuous variable systems

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

Adesso, Gerardo; Serafini, Alessio; Illuminati, Fabrizio

2004-08-01

We investigate the relationship between mixedness and entanglement for Gaussian states of continuous variable systems. We introduce generalized entropies based on Schatten p norms to quantify the mixedness of a state and derive their explicit expressions in terms of symplectic spectra. We compare the hierarchies of mixedness provided by such measures with the one provided by the purity (defined as tr {rho}{sup 2} for the state {rho}) for generic n-mode states. We then review the analysis proving the existence of both maximally and minimally entangled states at given global and marginal purities, with the entanglement quantified by the logarithmic negativity.more » Based on these results, we extend such an analysis to generalized entropies, introducing and fully characterizing maximally and minimally entangled states for given global and local generalized entropies. We compare the different roles played by the purity and by the generalized p entropies in quantifying the entanglement and the mixedness of continuous variable systems. We introduce the concept of average logarithmic negativity, showing that it allows a reliable quantitative estimate of continuous variable entanglement by direct measurements of global and marginal generalized p entropies.« less

Spatial and Temporal Variability and Trends in 2001-2016 Global Fire Activity

NASA Astrophysics Data System (ADS)

Earl, Nick; Simmonds, Ian

2018-03-01

Fire regimes across the globe have great spatial and temporal variability, and these are influence by many factors including anthropogenic management, climate, and vegetation types. Here we utilize the satellite-based "active fire" product, from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, to statistically analyze variability and trends in fire activity from the global to regional scales. We split up the regions by economic development, region/geographical land use, clusters of fire-abundant areas, or by religious/cultural influence. Weekly cycle tests are conducted to highlight and quantify part of the anthropogenic influence on fire regime across the world. We find that there is a strong statistically significant decline in 2001-2016 active fires globally linked to an increase in net primary productivity observed in northern Africa, along with global agricultural expansion and intensification, which generally reduces fire activity. There are high levels of variability, however. The large-scale regions exhibit either little change or decreasing in fire activity except for strong increasing trends in India and China, where rapid population increase is occurring, leading to agricultural intensification and increased crop residue burning. Variability in Canada has been linked to a warming global climate leading to a longer growing season and higher fuel loads. Areas with a strong weekly cycle give a good indication of where fire management is being applied most extensively, for example, the United States, where few areas retain a natural fire regime.

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.

New and Improved GLDAS and NLDAS Data Sets and Data Services at HDISC/NASA

NASA Technical Reports Server (NTRS)

Rui, Hualan; Beaudoing, Hiroko Kato; Mocko, David M.; Rodell, Matthew; Teng, William L.; Vollmer. Bruce

2010-01-01

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. Generating global fields of these variables, however, is still a challenge. The goal of a land data assimilation system (LDAS)is to ingest satellite-and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast.

Improving Global Gross Primary Productivity Estimates by Computing Optimum Light Use Efficiencies Using Flux Tower Data

NASA Astrophysics Data System (ADS)

Madani, Nima; Kimball, John S.; Running, Steven W.

2017-11-01

In the light use efficiency (LUE) approach of estimating the gross primary productivity (GPP), plant productivity is linearly related to absorbed photosynthetically active radiation assuming that plants absorb and convert solar energy into biomass within a maximum LUE (LUEmax) rate, which is assumed to vary conservatively within a given biome type. However, it has been shown that photosynthetic efficiency can vary within biomes. In this study, we used 149 global CO2 flux towers to derive the optimum LUE (LUEopt) under prevailing climate conditions for each tower location, stratified according to model training and test sites. Unlike LUEmax, LUEopt varies according to heterogeneous landscape characteristics and species traits. The LUEopt data showed large spatial variability within and between biome types, so that a simple biome classification explained only 29% of LUEopt variability over 95 global tower training sites. The use of explanatory variables in a mixed effect regression model explained 62.2% of the spatial variability in tower LUEopt data. The resulting regression model was used for global extrapolation of the LUEopt data and GPP estimation. The GPP estimated using the new LUEopt map showed significant improvement relative to global tower data, including a 15% R2 increase and 34% root-mean-square error reduction relative to baseline GPP calculations derived from biome-specific LUEmax constants. The new global LUEopt map is expected to improve the performance of LUE-based GPP algorithms for better assessment and monitoring of global terrestrial productivity and carbon dynamics.

Process contributions of Australian ecosystems to interannual variations in the carbon cycle

NASA Astrophysics Data System (ADS)

Haverd, Vanessa; Smith, Benjamin; Trudinger, Cathy

2016-05-01

New evidence is emerging that semi-arid ecosystems dominate interannual variability (IAV) of the global carbon cycle, largely via fluctuating water availability associated with El Niño/Southern Oscillation. Recent evidence from global terrestrial biosphere modelling and satellite-based inversion of atmospheric CO2 point to a large role of Australian ecosystems in global carbon cycle variability, including a large contribution from Australia to the record land sink of 2011. However the specific mechanisms governing this variability, and their bioclimatic distribution within Australia, have not been identified. Here we provide a regional assessment, based on best available observational data, of IAV in the Australian terrestrial carbon cycle and the role of Australia in the record land sink anomaly of 2011. We find that IAV in Australian net carbon uptake is dominated by semi-arid ecosystems in the east of the continent, whereas the 2011 anomaly was more uniformly spread across most of the continent. Further, and in contrast to global modelling results suggesting that IAV in Australian net carbon uptake is amplified by lags between production and decomposition, we find that, at continental scale, annual variations in production are dampened by annual variations in decomposition, with both fluxes responding positively to precipitation anomalies.

Global Quality of Life Among WHI Women Aged 80 Years and Older

PubMed Central

Brunner, Robert L.; Hogan, Patricia E.; Danhauer, Suzanne C.; Brenes, Gretchen A.; Bowen, Deborah J.; Snively, Beverly M.; Goveas, Joseph S.; Saquib, Nazmus; Zaslavsky, Oleg; Shumaker, Sally A.

2016-01-01

Abstract Background. The number of older adults living to age 80 and older is increasing rapidly, particularly among women. Correlates of quality of life (QOL) in very advanced ages are not known. We examined the association of demographic, social-psychological, lifestyle, and physical health variables with global QOL in a Women’s Health Initiative (WHI) cohort of women aged 80 and older. Methods. 26,299 WHI participants, who had completed a recent psychosocial and medical update, were included in these analyses. Global QOL was assessed by a single item, asking the women to rate their overall QOL on a scale from 0 to 10. Characteristics of the women were examined by the level of their transformed global QOL scores (≤50, 50–70, ≥70), and multiple regression was used to examine which demographic, social-psychological, lifestyle and health variables were independently associated with higher global QOL. Results. Social-psychological and current health variables were more strongly associated with global QOL than a history of selected comorbid conditions. In particular, higher self-rated health and fewer depressive symptoms were the most strongly associated with better global QOL in WHI women ≥80 years. Conclusions. Interventions to reduce depressive symptoms and improve health may lead to better self-reported health and global QOL among older women. Physical and mental health screenings followed by evidence-based interventions are imperative in geriatric care. PMID:26858327

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.

Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease.

PubMed

Rohr, Jason R; Raffel, Thomas R

2010-05-04

The role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial, and the effect of climatic variability, in particular, has largely been ignored. For instance, it was recently revealed that the proposed link between climate change and widespread amphibian declines, putatively caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), was tenuous because it was based on a temporally confounded correlation. Here we provide temporally unconfounded evidence that global El Niño climatic events drive widespread amphibian losses in genus Atelopus via increased regional temperature variability, which can reduce amphibian defenses against pathogens. Of 26 climate variables tested, only factors associated with temperature variability could account for the spatiotemporal patterns of declines thought to be associated with Bd. Climatic predictors of declines became significant only after controlling for a pattern consistent with epidemic spread (by temporally detrending the data). This presumed spread accounted for 59% of the temporal variation in amphibian losses, whereas El Niño accounted for 59% of the remaining variation. Hence, we could account for 83% of the variation in declines with these two variables alone. Given that global climate change seems to increase temperature variability, extreme climatic events, and the strength of Central Pacific El Niño episodes, climate change might exacerbate worldwide enigmatic declines of amphibians, presumably by increasing susceptibility to disease. These results suggest that changes to temperature variability associated with climate change might be as significant to biodiversity losses and disease emergence as changes to mean temperature.

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;

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.

Cloud cover archiving on a global scale - A discussion of principles

NASA Technical Reports Server (NTRS)

Henderson-Sellers, A.; Hughes, N. A.; Wilson, M.

1981-01-01

Monitoring of climatic variability and climate modeling both require a reliable global cloud data set. Examination is made of the temporal and spatial variability of cloudiness in light of recommendations made by GARP in 1975 (and updated by JOC in 1978 and 1980) for cloud data archiving. An examination of the methods of comparing cloud cover frequency curves suggests that the use of the beta distribution not only facilitates objective comparison, but also reduces overall storage requirements. A specific study of the only current global cloud climatology (the U.S. Air Force's 3-dimensional nephanalysis) over the United Kingdom indicates that discussion of methods of validating satellite-based data sets is urgently required.

Displacement Based Multilevel Structural Optimization

NASA Technical Reports Server (NTRS)

Sobieszezanski-Sobieski, J.; Striz, A. G.

1996-01-01

In the complex environment of true multidisciplinary design optimization (MDO), efficiency is one of the most desirable attributes of any approach. In the present research, a new and highly efficient methodology for the MDO subset of structural optimization is proposed and detailed, i.e., for the weight minimization of a given structure under size, strength, and displacement constraints. Specifically, finite element based multilevel optimization of structures is performed. In the system level optimization, the design variables are the coefficients of assumed polynomially based global displacement functions, and the load unbalance resulting from the solution of the global stiffness equations is minimized. In the subsystems level optimizations, the weight of each element is minimized under the action of stress constraints, with the cross sectional dimensions as design variables. The approach is expected to prove very efficient since the design task is broken down into a large number of small and efficient subtasks, each with a small number of variables, which are amenable to parallel computing.

A segmentation approach for a delineation of terrestrial ecoregions

NASA Astrophysics Data System (ADS)

Nowosad, J.; Stepinski, T.

2017-12-01

Terrestrial ecoregions are the result of regionalization of land into homogeneous units of similar ecological and physiographic features. Terrestrial Ecoregions of the World (TEW) is a commonly used global ecoregionalization based on expert knowledge and in situ observations. Ecological Land Units (ELUs) is a global classification of 250 meters-sized cells into 4000 types on the basis of the categorical values of four environmental variables. ELUs are automatically calculated and reproducible but they are not a regionalization which makes them impractical for GIS-based spatial analysis and for comparison with TEW. We have regionalized terrestrial ecosystems on the basis of patterns of the same variables (land cover, soils, landform, and bioclimate) previously used in ELUs. Considering patterns of categorical variables makes segmentation and thus regionalization possible. Original raster datasets of the four variables are first transformed into regular grids of square-sized blocks of their cells called eco-sites. Eco-sites are elementary land units containing local patterns of physiographic characteristics and thus assumed to contain a single ecosystem. Next, eco-sites are locally aggregated using a procedure analogous to image segmentation. The procedure optimizes pattern homogeneity of all four environmental variables within each segment. The result is a regionalization of the landmass into land units characterized by uniform pattern of land cover, soils, landforms, climate, and, by inference, by uniform ecosystem. Because several disjoined segments may have very similar characteristics, we cluster the segments to obtain a smaller set of segment types which we identify with ecoregions. Our approach is automatic, reproducible, updatable, and customizable. It yields the first automatic delineation of ecoregions on the global scale. In the resulting vector database each ecoregion/segment is described by numerous attributes which make it a valuable GIS resource for global ecological and conservation studies.

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

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

McCabe, M. F.; Ershadi, A.; Jimenez, C.

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, fourmore » commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2; 0.65), followed closely by GLEAM (0.68; 64 W m –2; 0.62), with values in parentheses representing the R 2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.« less

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

DOE PAGES

McCabe, M. F.; Ershadi, A.; Jimenez, C.; ...

2016-01-26

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, fourmore » commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2; 0.65), followed closely by GLEAM (0.68; 64 W m –2; 0.62), with values in parentheses representing the R 2, RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.« less

Sensitivity of Water Scarcity Events to ENSO-Driven Climate Variability at the Global Scale

NASA Technical Reports Server (NTRS)

Veldkamp, T. I. E.; Eisner, S.; Wada, Y.; Aerts, J. C. J. H.; Ward, P. J.

2015-01-01

Globally, freshwater shortage is one of the most dangerous risks for society. Changing hydro-climatic and socioeconomic conditions have aggravated water scarcity over the past decades. A wide range of studies show that water scarcity will intensify in the future, as a result of both increased consumptive water use and, in some regions, climate change. Although it is well-known that El Niño- Southern Oscillation (ENSO) affects patterns of precipitation and drought at global and regional scales, little attention has yet been paid to the impacts of climate variability on water scarcity conditions, despite its importance for adaptation planning. Therefore, we present the first global-scale sensitivity assessment of water scarcity to ENSO, the most dominant signal of climate variability. We show that over the time period 1961-2010, both water availability and water scarcity conditions are significantly correlated with ENSO-driven climate variability over a large proportion of the global land area (> 28.1 %); an area inhabited by more than 31.4% of the global population. We also found, however, that climate variability alone is often not enough to trigger the actual incidence of water scarcity events. The sensitivity of a region to water scarcity events, expressed in terms of land area or population exposed, is determined by both hydro-climatic and socioeconomic conditions. Currently, the population actually impacted by water scarcity events consists of 39.6% (CTA: consumption-to-availability ratio) and 41.1% (WCI: water crowding index) of the global population, whilst only 11.4% (CTA) and 15.9% (WCI) of the global population is at the same time living in areas sensitive to ENSO-driven climate variability. These results are contrasted, however, by differences in growth rates found under changing socioeconomic conditions, which are relatively high in regions exposed to water scarcity events. Given the correlations found between ENSO and water availability and scarcity conditions, and the relative developments of water scarcity impacts under changing socioeconomic conditions, we suggest that there is potential for ENSO-based adaptation and risk reduction that could be facilitated by more research on this emerging topic.

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;

Interactive influence of the Atlantic and Pacific climates and their contribution to the multidecadal variations of global temperature and precipitation.

NASA Astrophysics Data System (ADS)

Barcikowska, M. J.; Knutson, T. R.; Zhang, R.

2016-12-01

This study investigates mechanisms and global-scale climate impacts of multidecadal climate variability. Here we show, using observations and CSIRO-Mk3.6.0 model control run, that multidecadal variability of the Atlantic Meridional Overturning Circulation (AMOC) may have a profound impact on the thermal- and hydro-climatic changes over the Pacific region. In our model-based analysis we propose a mechanism, which comprises a coupled ocean-atmosphere teleconnection, established through the atmospheric overturning circulation cell between the tropical North Atlantic and tropical Pacific. For example, warming SSTs over the tropical North Atlantic intensify local convection and reinforce subsidence, low-level divergence in the eastern tropical Pacific. This is also accompanied with an intensification of trade winds, cooling and drying anomalies in the tropical central-east Pacific. The derived multidecadal changes, associated with the AMOC, contribute remarkably to the global temperature and precipitation variations. This highlights its potential predictive value. Shown here results suggest a possibility that: 1) recently observed slowdown in global warming may partly originate from internal variability, 2) climate system may be undergoing a transition to a cold AMO phase which could prolong the global slowdown.

Interdisciplinary research in global biogeochemical cycling Nitrous oxide in terrestrial ecosystems

NASA Technical Reports Server (NTRS)

Norman, S. D.; Peterson, D. L.

1984-01-01

NASA has begun an interdisciplinary research program to investigate various aspects of Global Biology and Global Habitability. An important element selected for the study of global phenomena is related to biogeochemical cycling. The studies involve a collaboration with recognized scientists in the areas of plant physiology, microbiology, nutrient cycling theory, and related areas. Selected subjects of study include nitrogen cycling dynamics in terrestrial ecosystems with special attention to biosphere/atmosphere interactions, and an identification of sensitive response variables which can be used in ecosystem models based on parameters derived from remotely sensed variables. A description is provided of the progress and findings over the past two years. Attention is given to the characteristics of nitrous oxide emissions, the approach followed in the investigations, the selection of study sites, radiometric measurements, and research in Sequoia.

Global Gradients of Coral Exposure to Environmental Stresses and Implications for Local Management

PubMed Central

Maina, Joseph; McClanahan, Tim R.; Venus, Valentijn; Ateweberhan, Mebrahtu; Madin, Joshua

2011-01-01

Background The decline of coral reefs globally underscores the need for a spatial assessment of their exposure to multiple environmental stressors to estimate vulnerability and evaluate potential counter-measures. Methodology/Principal Findings This study combined global spatial gradients of coral exposure to radiation stress factors (temperature, UV light and doldrums), stress-reinforcing factors (sedimentation and eutrophication), and stress-reducing factors (temperature variability and tidal amplitude) to produce a global map of coral exposure and identify areas where exposure depends on factors that can be locally managed. A systems analytical approach was used to define interactions between radiation stress variables, stress reinforcing variables and stress reducing variables. Fuzzy logic and spatial ordinations were employed to quantify coral exposure to these stressors. Globally, corals are exposed to radiation and reinforcing stress, albeit with high spatial variability within regions. Based on ordination of exposure grades, regions group into two clusters. The first cluster was composed of severely exposed regions with high radiation and low reducing stress scores (South East Asia, Micronesia, Eastern Pacific and the central Indian Ocean) or alternatively high reinforcing stress scores (the Middle East and the Western Australia). The second cluster was composed of moderately to highly exposed regions with moderate to high scores in both radiation and reducing factors (Caribbean, Great Barrier Reef (GBR), Central Pacific, Polynesia and the western Indian Ocean) where the GBR was strongly associated with reinforcing stress. Conclusions/Significance Despite radiation stress being the most dominant stressor, the exposure of coral reefs could be reduced by locally managing chronic human impacts that act to reinforce radiation stress. Future research and management efforts should focus on incorporating the factors that mitigate the effect of coral stressors until long-term carbon reductions are achieved through global negotiations. PMID:21860667

Melanin-based coloration and host–parasite interactions under global change

PubMed Central

Côte, J.; Boniface, A.; Blanchet, S.; Hendry, A. P.; Gasparini, J.

2018-01-01

The role of parasites in shaping melanin-based colour polymorphism, and the consequences of colour polymorphism for disease resistance, remain debated. Here we review recent evidence of the links between melanin-based coloration and the behavioural and immunological defences of vertebrates against their parasites. First we propose that (1) differences between colour morphs can result in variable exposure to parasites, either directly (certain colours might be more or less attractive to parasites) or indirectly (variations in behaviour and encounter probability). Once infected, we propose that (2) immune variation between differently coloured individuals might result in different abilities to cope with parasite infection. We then discuss (3) how these different abilities could translate into variable sexual and natural selection in environments varying in parasite pressure. Finally, we address (4) the potential role of parasites in the maintenance of melanin-based colour polymorphism, especially in the context of global change and multiple stressors in human-altered environments. Because global change will probably affect both coloration and the spread of parasitic diseases in the decades to come, future studies should take into account melanin-based coloration to better predict the evolutionary responses of animals to changing disease risk in human-altered environments. PMID:29848644

A New method for identifying possible causal relationships between CO2, total solar irradiance and global temperature change

NASA Astrophysics Data System (ADS)

Seip, Knut L.; Grøn, Øyvind

2017-02-01

We apply a novel method based upon "before" and "after" relationships to investigate and quantify interconnections between global temperature anomaly (GTA), as response variable, and greenhouse gases (CO2) and total solar irradiance (TSI) as candidate causal variables for the period 1880 to 2010. The most likely interpretations of our results for the 6 to 8 years cyclic components of the variables are that during the period 1929 to 1936, CO2 significantly leads GTA. However, during the period 1960-2003, GTA apparently leads CO2, that is, the peaks (and troughs) in GTA are in front of, and close to, the peaks (and troughs) in CO2. For time windows outside these periods, we did not find significant before or after-relations. An alternative interpretation is that there is a shift between short (≈1.5 year) and long (≈5 years) durations between cause and effect. Relationships between GTA and TSI suggest that "inertia" of the global sea, land, and atmosphere system leads to delays longer than half their common cycle length of about 10 years. Based on the interaction patterns between the variables GTA, CO2, and TSI, we suggest the possibility that a new regime for how the variables interact started around 1960. From trend forms, and not considering physical mechanisms, we found that the trend in CO2 contributes ≈ 90 %, and the trend in TSI ≈ 10 %, to the trend in GTA during the last 130 years.

Multi-region statistical shape model for cochlear implantation

NASA Astrophysics Data System (ADS)

Romera, Jordi; Kjer, H. Martin; Piella, Gemma; Ceresa, Mario; González Ballester, Miguel A.

2016-03-01

Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.

Atmospheric Variability of CO2 impact on space observation Requirements

NASA Astrophysics Data System (ADS)

Swanson, A. L.; Sen, B.; Newhart, L.; Segal, G.

2009-12-01

If International governments are to reduce GHG levels by 80% by 2050, as recommended by most scientific bodies concerned with avoiding the most hazardous changes in climate, then massive investments in infrastructure and new technology will be required over the coming decades. Such an investment will be a huge commitment by governments and corporations, and while it will offer long-term dividends in lower energy costs, a healthier environment and averted additional global warming, the shear magnitude of upfront costs will drive a call for a monitoring and verification system. Such a system will be required to offer accountability to signatories of governing bodies, as well as, for the global public. Measuring the average global distribution of CO2 is straight forward, as exemplified by the long running station measurements managed by NOAA’s Global Monitoring Division that includes the longterm Keeling record. However, quantifying anthropogenic and natural source/sink distributions and atmospheric mixing have been much more difficult to constrain. And, yet, an accurate accounting of all anthropogenic source strengths is required for Global Treaty verification. The only way to accurately assess Global GHG emissions is to construct an integrated system of ground, air and space based observations with extensive chemical modeling capabilities. We look at the measurement requirements for the space based component of the solutions. To determine what space sensor performance requirements for ground resolution, coverage, and revisit, we have analyzed regional CO2 distributions and variability using NASA and NOAA aircraft flight campaigns. The results of our analysis are presented as variograms showing average spatial variability over several Northern Hemispheric regions. There are distinct regional differences with the starkest contrast between urban versus rural and Coastal Asia versus Coastal US. The results suggest specific consequences on what spatial and temporal requirements might need to be for space based observations.

Contribution of rivers and floodplains to the global terrestrial water storage variability

NASA Astrophysics Data System (ADS)

Getirana, A.; Kumar, S.; Girotto, M.; Rodell, M.

2017-12-01

Since the launch of the GRACE mission in 2002, the scientific community has gained significant insight into terrestrial water storage (TWS) variations around the world. Still, understanding of the relationship between TWS variations and changes in its individual components (groundwater, soil moisture, surface waters, snow, and vegetation water storage) has not advanced beyond small-scale studies based on in situ data. Although a few studies have demonstrated the impact that surface water storage (SWS) has on TWS in tropical basins, the vast majority of investigations on TWS decomposition systematically neglect SWS by assuming that its contribution to TWS is trivial. Even though that assumption might be a close representation of the truth in specific locations, the actual impact of SWS on the global TWS change and its spatial variability is unknown. This study aims to quantify the contribution of rivers and floodplains on the global terrestrial water storage (TWS) variability. We use state-of-the-art models to simulate land surface processes and river dynamics in order to separate TWS into its main components. Based on a proposed impact index, we show that surface water storage (SWS) contributes to 7% of TWS globally, but that contribution highly varies spatially. The primary contribution of SWS to TWS is in the tropics, and in major rivers flowing over arid regions or at high latitudes. About 20-23% of both Amazon and Nile basins' TWS changes are due to SWS. SWS has low impact in Western U.S., Northern Africa, Middle-East and central Asia. Based on comparisons against GRACE-based estimates, we conclude that using SWS significantly improves TWS simulations in most South America, Africa and Northern India, confirming the need for SWS as a key component of TWS change.

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.

Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study.

PubMed

Colston, Josh M; Ahmed, Tahmeed; Mahopo, Cloupas; Kang, Gagandeep; Kosek, Margaret; de Sousa Junior, Francisco; Shrestha, Prakash Sunder; Svensen, Erling; Turab, Ali; Zaitchik, Benjamin

2018-04-21

Longitudinal and time series analyses are needed to characterize the associations between hydrometeorological parameters and health outcomes. Earth Observation (EO) climate data products derived from satellites and global model-based reanalysis have the potential to be used as surrogates in situations and locations where weather-station based observations are inadequate or incomplete. However, these products often lack direct evaluation at specific sites of epidemiological interest. Standard evaluation metrics of correlation, agreement, bias and error were applied to a set of ten hydrometeorological variables extracted from two quasi-global, commonly used climate data products - the Global Land Data Assimilation System (GLDAS) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) - to evaluate their performance relative to weather-station derived estimates at the specific geographic locations of the eight sites in a multi-site cohort study. These metrics were calculated for both daily estimates and 7-day averages and for a rotavirus-peak-season subset. Then the variables from the two sources were each used as predictors in longitudinal regression models to test their association with rotavirus infection in the cohort after adjusting for covariates. The availability and completeness of station-based validation data varied depending on the variable and study site. The performance of the two gridded climate models varied considerably within the same location and for the same variable across locations, according to different evaluation criteria and for the peak-season compared to the full dataset in ways that showed no obvious pattern. They also differed in the statistical significance of their association with the rotavirus outcome. For some variables, the station-based records showed a strong association while the EO-derived estimates showed none, while for others, the opposite was true. Researchers wishing to utilize publicly available climate data - whether EO-derived or station based - are advised to recognize their specific limitations both in the analysis and the interpretation of the results. Epidemiologists engaged in prospective research into environmentally driven diseases should install their own weather monitoring stations at their study sites whenever possible, in order to circumvent the constraints of choosing between distant or incomplete station data or unverified EO estimates. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Evidence and mapping of extinction debts for global forest-dwelling reptiles, amphibians and mammals

NASA Astrophysics Data System (ADS)

Chen, Youhua; Peng, Shushi

2017-03-01

Evidence of extinction debts for the global distributions of forest-dwelling reptiles, mammals and amphibians was tested and the debt magnitude was estimated and mapped. By using different correlation tests and variable importance analysis, the results showed that spatial richness patterns for the three forest-dwelling terrestrial vertebrate groups had significant and stronger correlations with past forest cover area and other variables in the 1500 s, implying the evidence for extinction debts. Moreover, it was likely that the extinction debts have been partially paid, given that their global richness patterns were also significantly correlated with contemporary forest variables in the 2000 s (but the absolute magnitudes of the correlation coefficients were usually smaller than those calculated for historical forest variables). By utilizing species-area relationships, spatial extinction-debt magnitudes for the three vertebrate groups at the global scale were estimated and the hotspots of extinction debts were identified. These high-debt hotspots were generally situated in areas that did not spatially overlap with hotspots of species richness or high extinction-risk areas based on IUCN threatened status to a large extent. This spatial mismatch pattern suggested that necessary conservation efforts should be directed toward high-debt areas that are still overlooked.

Evidence and mapping of extinction debts for global forest-dwelling reptiles, amphibians and mammals.

PubMed

Chen, Youhua; Peng, Shushi

2017-03-16

Evidence of extinction debts for the global distributions of forest-dwelling reptiles, mammals and amphibians was tested and the debt magnitude was estimated and mapped. By using different correlation tests and variable importance analysis, the results showed that spatial richness patterns for the three forest-dwelling terrestrial vertebrate groups had significant and stronger correlations with past forest cover area and other variables in the 1500 s, implying the evidence for extinction debts. Moreover, it was likely that the extinction debts have been partially paid, given that their global richness patterns were also significantly correlated with contemporary forest variables in the 2000 s (but the absolute magnitudes of the correlation coefficients were usually smaller than those calculated for historical forest variables). By utilizing species-area relationships, spatial extinction-debt magnitudes for the three vertebrate groups at the global scale were estimated and the hotspots of extinction debts were identified. These high-debt hotspots were generally situated in areas that did not spatially overlap with hotspots of species richness or high extinction-risk areas based on IUCN threatened status to a large extent. This spatial mismatch pattern suggested that necessary conservation efforts should be directed toward high-debt areas that are still overlooked.

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.

Data-based estimates of the ocean carbon sink variability - results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM)

NASA Astrophysics Data System (ADS)

Rödenbeck, Christian; Bakker, Dorothee; Gruber, Nicolas; Iida, Yosuke; Jacobson, Andy; Jones, Steve; Landschützer, Peter; Metzl, Nicolas; Nakaoka, Shin-ichiro; Olsen, Are; Park, Geun-Ha; Peylin, Philippe; Rodgers, Keith; Sasse, Tristan; Schuster, Ute; Shutler, James; Valsala, Vinu; Wanninkhof, Rik; Zeng, Jiye

2016-04-01

Using measurements of the surface-ocean COtwo partial pressure (pCOtwo) from the SOCAT and LDEO data bases and 14 different pCOtwo mapping methods recently collated by the Surface Ocean pCOtwo Mapping intercomparison (SOCOM) initiative, variations in regional and global sea-air COtwo fluxes are investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCOtwo seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the Eastern equatorial Pacific. Despite considerable spread in the detailed variations, mapping methods that fit the data more closely also tend to agree more closely with each other in regional averages. Encouragingly, this includes mapping methods belonging to complementary types - taking variability either directly from the pCOtwo data or indirectly from driver data via regression. From a weighted ensemble average, we find an IAV amplitude of the global sea-air COtwo flux of IAVampl (standard deviation over AnalysisPeriod), which is larger than simulated by biogeochemical process models. On a decadal perspective, the global ocean COtwo uptake is estimated to have gradually increased since about 2000, with little decadal change prior to that. The weighted mean net global ocean COtwo sink estimated by the SOCOM ensemble is -1.75 UPgCyr (AnalysisPeriod), consistent within uncertainties with estimates from ocean-interior carbon data or atmospheric oxygen trends. Using data-based sea-air COtwo fluxes in atmospheric COtwo inversions also helps to better constrain land-atmosphere COtwo fluxes.

Determination of continuous variable entanglement by purity measurements.

PubMed

Adesso, Gerardo; Serafini, Alessio; Illuminati, Fabrizio

2004-02-27

We classify the entanglement of two-mode Gaussian states according to their degree of total and partial mixedness. We derive exact bounds that determine maximally and minimally entangled states for fixed global and marginal purities. This characterization allows for an experimentally reliable estimate of continuous variable entanglement based on measurements of purity.

Trends and variability of daily temperature extremes during 1960-2012 in the Yangtze River Basin, China

USDA-ARS?s Scientific Manuscript database

The variability of temperature extremes has been the focus of attention during the past few decades, and may exert a great influence on the global hydrologic cycle and energy balance through thermal forcing. Based on daily minimum and maximum temperature observed by the China Meteorological Administ...

Geostatistical Analysis of Mesoscale Spatial Variability and Error in SeaWiFS and MODIS/Aqua Global Ocean Color Data

NASA Astrophysics Data System (ADS)

Glover, David M.; Doney, Scott C.; Oestreich, William K.; Tullo, Alisdair W.

2018-01-01

Mesoscale (10-300 km, weeks to months) physical variability strongly modulates the structure and dynamics of planktonic marine ecosystems via both turbulent advection and environmental impacts upon biological rates. Using structure function analysis (geostatistics), we quantify the mesoscale biological signals within global 13 year SeaWiFS (1998-2010) and 8 year MODIS/Aqua (2003-2010) chlorophyll a ocean color data (Level-3, 9 km resolution). We present geographical distributions, seasonality, and interannual variability of key geostatistical parameters: unresolved variability or noise, resolved variability, and spatial range. Resolved variability is nearly identical for both instruments, indicating that geostatistical techniques isolate a robust measure of biophysical mesoscale variability largely independent of measurement platform. In contrast, unresolved variability in MODIS/Aqua is substantially lower than in SeaWiFS, especially in oligotrophic waters where previous analysis identified a problem for the SeaWiFS instrument likely due to sensor noise characteristics. Both records exhibit a statistically significant relationship between resolved mesoscale variability and the low-pass filtered chlorophyll field horizontal gradient magnitude, consistent with physical stirring acting on large-scale gradient as an important factor supporting observed mesoscale variability. Comparable horizontal length scales for variability are found from tracer-based scaling arguments and geostatistical decorrelation. Regional variations between these length scales may reflect scale dependence of biological mechanisms that also create variability directly at the mesoscale, for example, enhanced net phytoplankton growth in coastal and frontal upwelling and convective mixing regions. Global estimates of mesoscale biophysical variability provide an improved basis for evaluating higher resolution, coupled ecosystem-ocean general circulation models, and data assimilation.

Global Design Optimization for Fluid Machinery Applications

NASA Technical Reports Server (NTRS)

Shyy, Wei; Papila, Nilay; Tucker, Kevin; Vaidyanathan, Raj; Griffin, Lisa

2000-01-01

Recent experiences in utilizing the global optimization methodology, based on polynomial and neural network techniques for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. Another advantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables and methods for predicting the model performance. Examples of applications selected from rocket propulsion components including a supersonic turbine and an injector element and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.

On nonstationarity and antipersistency in global temperature series

NASA Astrophysics Data System (ADS)

KäRner, O.

2002-10-01

Statistical analysis is carried out for satellite-based global daily tropospheric and stratospheric temperature anomaly and solar irradiance data sets. Behavior of the series appears to be nonstationary with stationary daily increments. Estimating long-range dependence between the increments reveals a remarkable difference between the two temperature series. Global average tropospheric temperature anomaly behaves similarly to the solar irradiance anomaly. Their daily increments show antipersistency for scales longer than 2 months. The property points at a cumulative negative feedback in the Earth climate system governing the tropospheric variability during the last 22 years. The result emphasizes a dominating role of the solar irradiance variability in variations of the tropospheric temperature and gives no support to the theory of anthropogenic climate change. The global average stratospheric temperature anomaly proceeds like a 1-dim random walk at least up to 11 years, allowing good presentation by means of the autoregressive integrated moving average (ARIMA) models for monthly series.

Spatial and temporal variation in plant hydraulic traits and their relevance for climate change impacts on vegetation.

PubMed

Anderegg, William R L

2015-02-01

Plant hydraulics mediate terrestrial woody plant productivity, influencing global water, carbon, and biogeochemical cycles, as well as ecosystem vulnerability to drought and climate change. While inter-specific differences in hydraulic traits are widely documented, intra-specific hydraulic variability is less well known and is important for predicting climate change impacts. Here, I present a conceptual framework for this intra-specific hydraulic trait variability, reviewing the mechanisms that drive variability and the consequences for vegetation response to climate change. I performed a meta-analysis on published studies (n = 33) of intra-specific variation in a prominent hydraulic trait - water potential at which 50% stem conductivity is lost (P50) - and compared this variation to inter-specific variability within genera and plant functional types used by a dynamic global vegetation model. I found that intra-specific variability is of ecologically relevant magnitudes, equivalent to c. 33% of the inter-specific variability within a genus, and is larger in angiosperms than gymnosperms, although the limited number of studies highlights that more research is greatly needed. Furthermore, plant functional types were poorly situated to capture key differences in hydraulic traits across species, indicating a need to approach prediction of drought impacts from a trait-based, rather than functional type-based perspective.

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.

Harmonize input selection for sediment transport prediction

NASA Astrophysics Data System (ADS)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

An evaluation of the effect of recent temperature variability on the prediction of coral bleaching events.

PubMed

Donner, Simon D

2011-07-01

Over the past 30 years, warm thermal disturbances have become commonplace on coral reefs worldwide. These periods of anomalous sea surface temperature (SST) can lead to coral bleaching, a breakdown of the symbiosis between the host coral and symbiotic dinoflagellates which reside in coral tissue. The onset of bleaching is typically predicted to occur when the SST exceeds a local climatological maximum by 1 degrees C for a month or more. However, recent evidence suggests that the threshold at which bleaching occurs may depend on thermal history. This study uses global SST data sets (HadISST and NOAA AVHRR) and mass coral bleaching reports (from Reefbase) to examine the effect of historical SST variability on the accuracy of bleaching prediction. Two variability-based bleaching prediction methods are developed from global analysis of seasonal and interannual SST variability. The first method employs a local bleaching threshold derived from the historical variability in maximum annual SST to account for spatial variability in past thermal disturbance frequency. The second method uses a different formula to estimate the local climatological maximum to account for the low seasonality of SST in the tropics. The new prediction methods are tested against the common globally fixed threshold method using the observed bleaching reports. The results find that estimating the bleaching threshold from local historical SST variability delivers the highest predictive power, but also a higher rate of Type I errors. The second method has the lowest predictive power globally, though regional analysis suggests that it may be applicable in equatorial regions. The historical data analysis suggests that the bleaching threshold may have appeared to be constant globally because the magnitude of interannual variability in maximum SST is similar for many of the world's coral reef ecosystems. For example, the results show that a SST anomaly of 1 degrees C is equivalent to 1.73-2.94 standard deviations of the maximum monthly SST for two-thirds of the world's coral reefs. Coral reefs in the few regions that experience anomalously high interannual SST variability like the equatorial Pacific could prove critical to understanding how coral communities acclimate or adapt to frequent and/or severe thermal disturbances.

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

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501

Solar variability: Implications for global change

NASA Technical Reports Server (NTRS)

Lean, Judith; Rind, David

1994-01-01

Solar variability is examined in search of implications for global change. The topics covered include the following: solar variation modification of global surface temperature; the significance of solar variability with respect to future climate change; and methods of reducing the uncertainty of the potential amplitude of solar variability on longer time scales.

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

2017-12-01

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. Focusingmore » 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 (N m) and phosphorus (P m), we characterize how traits vary within and among over 50,000 ~50×50-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 further reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.« less

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 variable resolution nonhydrostatic global atmospheric semi-implicit semi-Lagrangian model

NASA Astrophysics Data System (ADS)

Pouliot, George Antoine

2000-10-01

The objective of this project is to develop a variable-resolution finite difference adiabatic global nonhydrostatic semi-implicit semi-Lagrangian (SISL) model based on the fully compressible nonhydrostatic atmospheric equations. To achieve this goal, a three-dimensional variable resolution dynamical core was developed and tested. The main characteristics of the dynamical core can be summarized as follows: Spherical coordinates were used in a global domain. A hydrostatic/nonhydrostatic switch was incorporated into the dynamical equations to use the fully compressible atmospheric equations. A generalized horizontal variable resolution grid was developed and incorporated into the model. For a variable resolution grid, in contrast to a uniform resolution grid, the order of accuracy of finite difference approximations is formally lost but remains close to the order of accuracy associated with the uniform resolution grid provided the grid stretching is not too significant. The SISL numerical scheme was implemented for the fully compressible set of equations. In addition, the generalized minimum residual (GMRES) method with restart and preconditioner was used to solve the three-dimensional elliptic equation derived from the discretized system of equations. The three-dimensional momentum equation was integrated in vector-form to incorporate the metric terms in the calculations of the trajectories. Using global re-analysis data for a specific test case, the model was compared to similar SISL models previously developed. Reasonable agreement between the model and the other independently developed models was obtained. The Held-Suarez test for dynamical cores was used for a long integration and the model was successfully integrated for up to 1200 days. Idealized topography was used to test the variable resolution component of the model. Nonhydrostatic effects were simulated at grid spacings of 400 meters with idealized topography and uniform flow. Using a high-resolution topographic data set and the variable resolution grid, sets of experiments with increasing resolution were performed over specific regions of interest. Using realistic initial conditions derived from re-analysis fields, nonhydrostatic effects were significant for grid spacings on the order of 0.1 degrees with orographic forcing. If the model code was adapted for use in a message passing interface (MPI) on a parallel supercomputer today, it was estimated that a global grid spacing of 0.1 degrees would be achievable for a global model. In this case, nonhydrostatic effects would be significant for most areas. A variable resolution grid in a global model provides a unified and flexible approach to many climate and numerical weather prediction problems. The ability to configure the model from very fine to very coarse resolutions allows for the simulation of atmospheric phenomena at different scales using the same code. We have developed a dynamical core illustrating the feasibility of using a variable resolution in a global model.

Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

NASA Astrophysics Data System (ADS)

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

2018-03-01

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

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

Analysis of the global ISCCP TOVS water vapor climatology

NASA Technical Reports Server (NTRS)

Wittmeyer, Ian L.; Vonder Haar, Thomas H.

1994-01-01

A climatological examination of the global water vapor field based on a multiyear period of successfull satellite-based observations is presented. Results from the multiyear global ISCCP TIROS Operational Vertical Sounder (TOVS) water vapor dataset as operationally produced by NESDIS and ISCCP are shown. The methods employed for the retrieval of precipitable water content (PWC) utilize infrared measurements collected by the TOVS instrument package flown aboard the NOAA series of operational polar-orbiting satellites. Strengths of this dataset include the nearly global daily coverage, availability for a multiyear period, operational internal quality checks, and its description of important features in the mean state of the atmosphere. Weaknesses of this PWC dataset include that the infrared sensors are unable to collect data in cloudy regions, the retrievals are strongly biased toward a land-based radiosonde first-guess dataset, and the description of high spatial and temporal variability is inadequate. Primary consequences of these factors are seen in the underestimation of ITCZ water vapor maxima, and underestimation of midlatitude water vapor mean and standard deviation values where transient atmospheric phenomena contribute significantly toward time means. A comparison of TOVS analyses to SSM/I data over ocean for the month of July 1988 shows fair agreement in the magnitude and distribution of the monthly mean values, but the TOVS fields exhibit much less temporal and spatial variability on a daily basis in comparison to the SSM/I analyses. The emphasis of this paper is on the presentation and documentation of an early satellite-based water vapor climatology, and description of factors that prevent a more accurate representation of the global water vapor field.

Consistency across missions of long time series of global biophysical variables: challenges and lessons learnt

NASA Astrophysics Data System (ADS)

Lacaze, Roselyne; Smets, Bruno; Calvet, Jean-Christophe; Camacho, Fernando; Swinnen, Else; Verger, Aleixandre

2017-04-01

The Global component of the Copernicus Land Monitoring Service (CGLS) provides continuously a set of bio-geophysical variables describing the dynamics of vegetation, the energy budget at the continental surface, the water cycle and the cryosphere. Products are generated on a reliable and automatic basis from Earth Observation satellite data, at a frequency ranging from one hour to 10 days. They are accessible free of charge through the GCLS website (http://land.copernicus.eu/global/), associated with documentation describing the physical methodologies, the technical properties of products, and the quality of variables based on the results of validation exercises. The portfolio of the CGLS contains some Essential Climate Variables (ECV) like the Leaf Area Index (LAI), the Fraction of PAR absorbed by the vegetation (FAPAR), the surface albedo, and additional vegetation indices. These products were derived from SPOT/VEGETATION sensor data till December 2013, are currently derived from PROBA-V sensor data, and will be derived in the future from Sentinel-3 data. This talk will show how challenging is the transition between sensors to ensure the sustainability of the production while keeping the consistency of the time series. We will discuss the various sources of differences from input data, the impact of these differences on the biophysical variables and, in turn, on some final users' applications as such those based upon anomalies or assimilation of time series. We will present the mitigation measures taken to reduce as much as possible this impact. We will conclude with the lessons learnt and how this experience will be exploited to manage the transition towards Sentinel-3.

Middle Pliocene sea surface temperature variability

USGS Publications Warehouse

Dowsett, H.J.; Chandler, M.A.; Cronin, T. M.; Dwyer, Gary S.

2005-01-01

Estimates of sea surface temperature (SST) based upon foraminifer, diatom, and ostracod assemblages from ocean cores reveal a warm phase of the Pliocene between about 3.3 and 3.0 Ma. Pollen records and plant megafossils, although not as well dated, show evidence for a warmer climate at about the same time. Increased greenhouse forcing and altered ocean heat transport are the leading candidates for the underlying cause of Pliocene global warmth. Despite being a period of global warmth, this interval encompasses considerable variability. Two new SST reconstructions are presented that are designed to provide a climatological error bar for warm peak phases of the Pliocene and to document the spatial distribution and magnitude of SST variability within the mid-Pliocene warm period. These data suggest long-term stability of low-latitude SST and document greater variability in regions of maximum warming. Copyright 2005 by the American Geophysical Union.

EMG-based speech recognition using hidden markov models with global control variables.

PubMed

Lee, Ki-Seung

2008-03-01

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

Satellite-based global-ocean mass balance estimates of interannual variability and emerging trends in continental freshwater discharge

PubMed Central

Syed, Tajdarul H.; Famiglietti, James S.; Chambers, Don P.; Willis, Josh K.; Hilburn, Kyle

2010-01-01

Freshwater discharge from the continents is a key component of Earth’s water cycle that sustains human life and ecosystem health. Surprisingly, owing to a number of socioeconomic and political obstacles, a comprehensive global river discharge observing system does not yet exist. Here we use 13 years (1994–2006) of satellite precipitation, evaporation, and sea level data in an ocean mass balance to estimate freshwater discharge into the global ocean. Results indicate that global freshwater discharge averaged 36,055 km3/y for the study period while exhibiting significant interannual variability driven primarily by El Niño Southern Oscillation cycles. The method described here can ultimately be used to estimate long-term global discharge trends as the records of sea level rise and ocean temperature lengthen. For the relatively short 13-year period studied here, global discharge increased by 540 km3/y2, which was largely attributed to an increase of global-ocean evaporation (768 km3/y2). Sustained growth of these flux rates into long-term trends would provide evidence for increasing intensity of the hydrologic cycle. PMID:20921364

An approach for the long-term 30-m land surface snow-free albedo retrieval from historic Landsat surface reflectance and MODIS-based a priori anisotropy knowledge

USDA-ARS?s Scientific Manuscript database

Land surface albedo has been recognized by the Global Terrestrial Observing System (GTOS) as an essential climate variable crucial for accurate modeling and monitoring of the Earth’s radiative budget. While global climate studies can leverage albedo datasets from MODIS, VIIRS, and other coarse-reso...

Mechanisms Controlling Global Mean Sea Surface Temperature Determined From a State Estimate

NASA Astrophysics Data System (ADS)

Ponte, R. M.; Piecuch, C. G.

2018-04-01

Global mean sea surface temperature (T¯) is a variable of primary interest in studies of climate variability and change. The temporal evolution of T¯ can be influenced by surface heat fluxes (F¯) and by diffusion (D¯) and advection (A¯) processes internal to the ocean, but quantifying the contribution of these different factors from data alone is prone to substantial uncertainties. Here we derive a closed T¯ budget for the period 1993-2015 based on a global ocean state estimate, which is an exact solution of a general circulation model constrained to most extant ocean observations through advanced optimization methods. The estimated average temperature of the top (10-m thick) level in the model, taken to represent T¯, shows relatively small variability at most time scales compared to F¯, D¯, or A¯, reflecting the tendency for largely balancing effects from all the latter terms. The seasonal cycle in T¯ is mostly determined by small imbalances between F¯ and D¯, with negligible contributions from A¯. While D¯ seems to simply damp F¯ at the annual period, a different dynamical role for D¯ at semiannual period is suggested by it being larger than F¯. At periods longer than annual, A¯ contributes importantly to T¯ variability, pointing to the direct influence of the variable ocean circulation on T¯ and mean surface climate.

Uncertainty in Indian Ocean Dipole response to global warming: the role of internal variability

NASA Astrophysics Data System (ADS)

Hui, Chang; Zheng, Xiao-Tong

2018-01-01

The Indian Ocean Dipole (IOD) is one of the leading modes of interannual sea surface temperature (SST) variability in the tropical Indian Ocean (TIO). The response of IOD to global warming is quite uncertain in climate model projections. In this study, the uncertainty in IOD change under global warming, especially that resulting from internal variability, is investigated based on the community earth system model large ensemble (CESM-LE). For the IOD amplitude change, the inter-member uncertainty in CESM-LE is about 50% of the intermodel uncertainty in the phase 5 of the coupled model intercomparison project (CMIP5) multimodel ensemble, indicating the important role of internal variability in IOD future projection. In CESM-LE, both the ensemble mean and spread in mean SST warming show a zonal positive IOD-like (pIOD-like) pattern in the TIO. This pIOD-like mean warming regulates ocean-atmospheric feedbacks of the interannual IOD mode, and weakens the skewness of the interannual variability. However, as the changes in oceanic and atmospheric feedbacks counteract each other, the inter-member variability in IOD amplitude change is not correlated with that of the mean state change. Instead, the ensemble spread in IOD amplitude change is correlated with that in ENSO amplitude change in CESM-LE, reflecting the close inter-basin relationship between the tropical Pacific and Indian Ocean in this model.

Visualization of Global Sensitivity Analysis Results Based on a Combination of Linearly Dependent and Independent Directions

NASA Technical Reports Server (NTRS)

Davies, Misty D.; Gundy-Burlet, Karen

2010-01-01

A useful technique for the validation and verification of complex flight systems is Monte Carlo Filtering -- a global sensitivity analysis that tries to find the inputs and ranges that are most likely to lead to a subset of the outputs. A thorough exploration of the parameter space for complex integrated systems may require thousands of experiments and hundreds of controlled and measured variables. Tools for analyzing this space often have limitations caused by the numerical problems associated with high dimensionality and caused by the assumption of independence of all of the dimensions. To combat both of these limitations, we propose a technique that uses a combination of the original variables with the derived variables obtained during a principal component analysis.

Reconciling Land-Ocean Moisture Transport Variability in Reanalyses with P-ET in Observationally-Driven Land Surface Models

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Bosilovich, Michael G.; Roberts, Jason B.

2016-01-01

Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC equals P minus ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern. Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P minus ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979-2012 ranging from 0.07 to minus 0.03 millimeters per day per decade are reduced by the adjustments to 0.016 millimeters per day per decade, much closer to the LSM P minus ET estimate (0.007 millimeters per day per decade). Neither is significant at the 90 percent level. ENSO (El Nino-Southern Oscillation)-related modulation of VMFC and P minus ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86.

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

NASA Technical Reports Server (NTRS)

Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.; Reichle, R. H.; Adler, R.; Ricciardulli, L.; Berg, W.; Huffman, G. J.

2013-01-01

Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal Oscillation, Atlantic Meridional Overturning), and efforts to distinguish these modes from longer-term trends.

Centennial-scale Holocene climate variations amplified by Antarctic Ice Sheet discharge

NASA Astrophysics Data System (ADS)

Bakker, Pepijn; Clark, Peter U.; Golledge, Nicholas R.; Schmittner, Andreas; Weber, Michael E.

2017-01-01

Proxy-based indicators of past climate change show that current global climate models systematically underestimate Holocene-epoch climate variability on centennial to multi-millennial timescales, with the mismatch increasing for longer periods. Proposed explanations for the discrepancy include ocean-atmosphere coupling that is too weak in models, insufficient energy cascades from smaller to larger spatial and temporal scales, or that global climate models do not consider slow climate feedbacks related to the carbon cycle or interactions between ice sheets and climate. Such interactions, however, are known to have strongly affected centennial- to orbital-scale climate variability during past glaciations, and are likely to be important in future climate change. Here we show that fluctuations in Antarctic Ice Sheet discharge caused by relatively small changes in subsurface ocean temperature can amplify multi-centennial climate variability regionally and globally, suggesting that a dynamic Antarctic Ice Sheet may have driven climate fluctuations during the Holocene. We analysed high-temporal-resolution records of iceberg-rafted debris derived from the Antarctic Ice Sheet, and performed both high-spatial-resolution ice-sheet modelling of the Antarctic Ice Sheet and multi-millennial global climate model simulations. Ice-sheet responses to decadal-scale ocean forcing appear to be less important, possibly indicating that the future response of the Antarctic Ice Sheet will be governed more by long-term anthropogenic warming combined with multi-centennial natural variability than by annual or decadal climate oscillations.

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

NASA Astrophysics Data System (ADS)

Sun, Qiaohong; Miao, Chiyuan; Duan, Qingyun; Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin

2018-03-01

In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

A new approach to correct for absorbing aerosols in OMI UV

NASA Astrophysics Data System (ADS)

Arola, A.; Kazadzis, S.; Lindfors, A.; Krotkov, N.; Kujanpää, J.; Tamminen, J.; Bais, A.; di Sarra, A.; Villaplana, J. M.; Brogniez, C.; Siani, A. M.; Janouch, M.; Weihs, P.; Webb, A.; Koskela, T.; Kouremeti, N.; Meloni, D.; Buchard, V.; Auriol, F.; Ialongo, I.; Staneck, M.; Simic, S.; Smedley, A.; Kinne, S.

2009-11-01

Several validation studies of surface UV irradiance based on the Ozone Monitoring Instrument (OMI) satellite data have shown a high correlation with ground-based measurements but a positive bias in many locations. The main part of the bias can be attributed to the boundary layer aerosol absorption that is not accounted for in the current satellite UV algorithms. To correct for this shortfall, a post-correction procedure was applied, based on global climatological fields of aerosol absorption optical depth. These fields were obtained by using global aerosol optical depth and aerosol single scattering albedo data assembled by combining global aerosol model data and ground-based aerosol measurements from AERONET. The resulting improvements in the satellite-based surface UV irradiance were evaluated by comparing satellite and ground-based spectral irradiances at various European UV monitoring sites. The results generally showed a significantly reduced bias by 5-20%, a lower variability, and an unchanged, high correlation coefficient.

A Global Drought and Flood Catalogue for the past 100 years

NASA Astrophysics Data System (ADS)

Sheffield, J.; He, X.; Peng, L.; Pan, M.; Fisher, C. K.; Wood, E. F.

2017-12-01

Extreme hydrological events cause the most impacts of natural hazards globally, impacting on a wide range of sectors including, most prominently, agriculture, food security and water availability and quality, but also on energy production, forestry, health, transportation and fisheries. Understanding how floods and droughts intersect, and have changed in the past provides the basis for understanding current risk and how it may change in the future. To do this requires an understanding of the mechanisms associated with events and therefore their predictability, attribution of long-term changes in risk, and quantification of projections of changes in the future. Of key importance are long-term records of relevant variables so that risk can be quantified more accurately, given the growing acknowledgement that risk is not stationary under long-term climate variability and climate change. To address this, we develop a catalogue of drought and flood events based on land surface and hydrodynamic modeling, forced by a hybrid meteorological dataset that draws from the continuity and coverage of reanalysis, and satellite datasets, merged with global gauge databases. The meteorological dataset is corrected for temporal inhomogeneities, spurious trends and variable inter-dependencies to ensure long-term consistency, as well as realistic representation of short-term variability and extremes. The VIC land surface model is run for the past 100 years at 0.25-degree resolution for global land areas. The VIC runoff is then used to drive the CaMa-Flood hydrodynamic model to obtain information on flood inundation risk. The model outputs are compared to satellite based estimates of flood and drought conditions and the observational flood record. The data are analyzed in terms of the spatio-temporal characteristics of large-scale flood and drought events with a particular focus on characterizing the long-term variability in risk. Significant changes in risk occur on multi-decadal time scales and are mostly associated with variability in the North Atlantic and Pacific. The catalogue can be used for analysis of extreme events, risk assessment, and as a benchmark for model evaluation.

Recent changes in county-level corn yield variability in the United States from observations and crop models

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

Leng, Guoyong

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota,more » Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.« less

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.

Resilience in the global food system

NASA Astrophysics Data System (ADS)

Seekell, David; Carr, Joel; Dell'Angelo, Jampel; D'Odorico, Paolo; Fader, Marianela; Gephart, Jessica; Kummu, Matti; Magliocca, Nicholas; Porkka, Miina; Puma, Michael; Ratajczak, Zak; Rulli, Maria Cristina; Suweis, Samir; Tavoni, Alessandro

2017-02-01

Ensuring food security requires food production and distribution systems function throughout disruptions. Understanding the factors that contribute to the global food system’s ability to respond and adapt to such disruptions (i.e. resilience) is critical for understanding the long-term sustainability of human populations. Variable impacts of production shocks on food supply between countries indicate a need for national-scale resilience indicators that can provide global comparisons. However, methods for tracking changes in resilience have had limited application to food systems. We developed an indicator-based analysis of food systems resilience for the years 1992-2011. Our approach is based on three dimensions of resilience: socio-economic access to food in terms of income of the poorest quintile relative to food prices, biophysical capacity to intensify or extensify food production, and the magnitude and diversity of current domestic food production. The socio-economic indicator has a large variability, but with low values concentrated in Africa and Asia. The biophysical capacity indicator is highest in Africa and Eastern Europe, in part because of a high potential for extensification of cropland and for yield gap closure in cultivated areas. However, the biophysical capacity indicator has declined globally in recent years. The production diversity indicator has increased slightly, with a relatively even geographic distribution. Few countries had exclusively high or low values for all indicators. Collectively, these results are the basis for global comparisons of resilience between countries, and provide necessary context for developing generalizations about resilience in the global food system.

Global Change and Human Consumption of Freshwater Driven by Flow Regulation and Irrigation

NASA Astrophysics Data System (ADS)

Jaramillo, F.; Destouni, G.

2015-12-01

Recent studies show major uncertainties about the magnitude and key drivers of global freshwater change, historically and projected for the future. The tackling of these uncertainties should be a societal priority to understand: 1) the role of human change drivers for freshwater availability changes, 2) the global water footprint of humanity and 3) the relation of human freshwater consumption to a proposed planetary boundary. This study analyses worldwide hydroclimatic changes, as observed during 1900-2009 in 99 large hydrological basins across all continents. We test whether global freshwater change may be driven by major developments of flow regulation and irrigation (FRI) occurring over this period. Independent categorization of the variability of FRI-impact strength among the studied basins is used to identify statistical basin differences in occurrence and strength of characteristic hydroclimatic signals of FRI. Our results show dominant signals of increasing relative evapotranspiration in basins affected by flow regulation and/or irrigation, in conjunction with decreasing relative intra-annual variability of runoff in basins affected by flow regulation. The FRI-related increase in relative evapotranspiration implies an increase of 4,688 km3/yr in global annual average water flow from land to the atmosphere. This observation-based estimate extends considerably the upper quantification limits of both FRI-driven and total global human consumption of freshwater, as well as the global water footprint of humanity. Our worldwide analysis shows clear FRI-related change signals emerging directly from observations, in spite of large change variability among basins and many other coexisting change drivers in both the atmosphere and the landscape. These results highlight the importance of considering local water use as a key change driver in Earth system studies and modelling, of relevance for global change and human consumption of freshwater.

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.

Global Lightning Activity

NASA Technical Reports Server (NTRS)

Christian, Hugh J.

2004-01-01

Our knowledge of the global distribution of lightning has improved dramatically since the advent of spacebased lightning observations. Of major importance was the 1995 launch of the Optical Transient Detector (OTD), followed in 1997 by the launch of the Lightning Imaging Sensor (LIS). Together, these instruments have generated a continuous eight-year record of global lightning activity. These lightning observations have provided a new global perspective on total lightning activity. For the first time, total lightning activity (cloud-to-ground and intra-cloud) has been observed over large regions with high detection efficiency and accurate geographic location. This has produced new insights into lightning distributions, times of occurrence and variability. It has produced a revised global flash rate estimate (44 flashes per second) and has lead to a new realization of the significance of total lightning activity in severe weather. Accurate flash rate estimates are now available over large areas of the earth (+/- 72 deg. latitude). Ocean-land contrasts as a function of season are clearly reveled, as are orographic effects and seasonal and interannual variability. The space-based observations indicate that air mass thunderstorms, not large storm system dominate global activity. The ability of LIS and OTD to detect total lightning has lead to improved insight into the correlation between lightning and storm development. The relationship between updraft development and lightning activity is now well established and presents an opportunity for providing a new mechanism for remotely monitoring storm development. In this concept, lightning would serve as a surrogate for updraft velocity. It is anticipated that this capability could lead to significantly improved severe weather warning times and reduced false warning rates. This talk will summarize our space-based lightning measurements, will discuss how lightning observations can be used to monitor severe weather, and present a concept for continuous geostationary-based lightning observations.

Task planning with uncertainty for robotic systems. Thesis

NASA Technical Reports Server (NTRS)

Cao, Tiehua

1993-01-01

In a practical robotic system, it is important to represent and plan sequences of operations and to be able to choose an efficient sequence from them for a specific task. During the generation and execution of task plans, different kinds of uncertainty may occur and erroneous states need to be handled to ensure the efficiency and reliability of the system. An approach to task representation, planning, and error recovery for robotic systems is demonstrated. Our approach to task planning is based on an AND/OR net representation, which is then mapped to a Petri net representation of all feasible geometric states and associated feasibility criteria for net transitions. Task decomposition of robotic assembly plans based on this representation is performed on the Petri net for robotic assembly tasks, and the inheritance of properties of liveness, safeness, and reversibility at all levels of decomposition are explored. This approach provides a framework for robust execution of tasks through the properties of traceability and viability. Uncertainty in robotic systems are modeled by local fuzzy variables, fuzzy marking variables, and global fuzzy variables which are incorporated in fuzzy Petri nets. Analysis of properties and reasoning about uncertainty are investigated using fuzzy reasoning structures built into the net. Two applications of fuzzy Petri nets, robot task sequence planning and sensor-based error recovery, are explored. In the first application, the search space for feasible and complete task sequences with correct precedence relationships is reduced via the use of global fuzzy variables in reasoning about subgoals. In the second application, sensory verification operations are modeled by mutually exclusive transitions to reason about local and global fuzzy variables on-line and automatically select a retry or an alternative error recovery sequence when errors occur. Task sequencing and task execution with error recovery capability for one and multiple soft components in robotic systems are investigated.

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.

Climate and soil attributes determine plant species turnover in global drylands.

PubMed

Ulrich, Werner; Soliveres, Santiago; Maestre, Fernando T; Gotelli, Nicholas J; Quero, José L; Delgado-Baquerizo, Manuel; Bowker, Matthew A; Eldridge, David J; Ochoa, Victoria; Gozalo, Beatriz; Valencia, Enrique; Berdugo, Miguel; Escolar, Cristina; García-Gómez, Miguel; Escudero, Adrián; Prina, Aníbal; Alfonso, Graciela; Arredondo, Tulio; Bran, Donaldo; Cabrera, Omar; Cea, Alex; Chaieb, Mohamed; Contreras, Jorge; Derak, Mchich; Espinosa, Carlos I; Florentino, Adriana; Gaitán, Juan; Muro, Victoria García; Ghiloufi, Wahida; Gómez-González, Susana; Gutiérrez, Julio R; Hernández, Rosa M; Huber-Sannwald, Elisabeth; Jankju, Mohammad; Mau, Rebecca L; Hughes, Frederic Mendes; Miriti, Maria; Monerris, Jorge; Muchane, Muchai; Naseri, Kamal; Pucheta, Eduardo; Ramírez-Collantes, David A; Raveh, Eran; Romão, Roberto L; Torres-Díaz, Cristian; Val, James; Veiga, José Pablo; Wang, Deli; Yuan, Xia; Zaady, Eli

2014-12-01

Geographic, climatic, and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. This study aims to: i) characterize patterns of beta diversity in global drylands, ii) detect common environmental drivers of beta diversity, and iii) test for thresholds in environmental conditions driving potential shifts in plant species composition. 224 sites in diverse dryland plant communities from 22 geographical regions in six continents. Beta diversity was quantified with four complementary measures: the percentage of singletons (species occurring at only one site), Whittake's beta diversity (β(W)), a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites (β(R 2 )), and a multivariate abundance-based metric (β(MV)). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographic, climatic, and soil variables. Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity (percentage of singletons and β(W)) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance ((β(R 2 )) and β(MV)) were more associated with climate variability. Interactions among soil variables, climatic factors, and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving ~ 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Nelkin, Eric; Ardizzone, Joe; Atlas, Robert M.

2003-01-01

To improve our understanding of global energy and water cycle variability, and to improve model simulations of climate variations, it is vital to have accurate latent heat fluxes (LHF) over global oceans. Monthly LHF, 10-m wind speed (U10m), 10-m specific humidity (Q10h), and sea-air humidity difference (Qs-Q10m) of GSSTF2 (version 2 Goddard Satellite-based Surface Turbulent Fluxes) over global Oceans during 1992-93 are compared with those of HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP (NCEP/NCAR reanalysis). The mean differences, standard deviations of differences, and temporal correlation of these monthly variables over global Oceans during 1992-93 between GSSTF2 and each of the three datasets are analyzed. The large-scale patterns of the 2yr-mean fields for these variables are similar among these four datasets, but significant quantitative differences are found. The temporal correlation is higher in the northern extratropics than in the south for all variables, with the contrast being especially large for da Silva as a result of more missing ship data in the south. The da Silva has extremely low temporal correlation and large differences with GSSTF2 for all variables in the southern extratropics, indicating that da Silva hardly produces a realistic variability in these variables. The NCEP has extremely low temporal correlation (0.27) and large spatial variations of differences with GSSTF2 for Qs-Q10m in the tropics, which causes the low correlation for LHF. Over the tropics, the HOAPS LHF is significantly smaller than GSSTF2 by approx. 31% (37 W/sq m), whereas the other two datasets are comparable to GSSTF2. This is because the HOAPS has systematically smaller LHF than GSSTF2 in space, while the other two datasets have very large spatial variations of large positive and negative LHF differences with GSSTF2 to cancel and to produce smaller regional-mean differences. Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other three flux datasets examined, although those of GSSTF2 are still subject to regional biases.

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

Methane emission from animals: A Global High-Resolution Data Base

NASA Astrophysics Data System (ADS)

Lerner, Jean; Matthews, Elaine; Fung, Inez

1988-06-01

We present a high-resolution global data base of animal population densities and associated methane emission. Statistics on animal populations from the Food and Agriculture Organization and other sources have been compiled. Animals were distributed using a 1° resolution data base of countries of the world and a 1° resolution data base of land use. The animals included are cattle and dairy cows, water buffalo, sheep, goats, camels, pigs, horses and caribou. Published estimates of methane production from each type of animal have been applied to the animal populations to yield a global distribution of annual methane emission by animals. There is large spatial variability in the distribution of animal populations and their methane emissions. Emission rates greater than 5000 kg CH4 km-2 yr-1 are found in small regions such as Bangladesh, the Benelux countries, parts of northern India, and New Zealand. Of the global annual emission of 75.8 Tg CH4 for 1984, about 55% is concentrated between 25°N and 55°N, a significant contribution to the observed north-south gradient of atmospheric methane concentration. A magnetic tape of the global data bases is available from the authors.

Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.

Comparing UK, USA and Australian values for EQ-5D as a health utility measure of oral health.

PubMed

Brennan, D S; Teusner, D N

2015-09-01

Using generic measures to examine outcomes of oral disorders can add additional information relating to health utility. However, different algorithms are available to generate health states. The aim was to assess UK-, US- and Australian-based algorithms for the EuroQol (EQ-5D) in relation to their discriminative and convergent validity. Methods: Data were collected from adults in Australia aged 30-61 years by mailed survey in 2009-10, including the EQ-5D and a range of self-reported oral health variables, and self-rated oral and general health. Responses were collected from n=1,093 persons (response rate 39.1%). UK-based EQ-5D estimates were lower (0.85) than the USA and Australian estimates (0.91). EQ-5D was associated (p<0.01) with all seven oral health variables, with differences in utility scores ranging from 0.03 to 0.06 for the UK, from 0.04 to 0.07 for the USA, and from 0.05 to 0.08 for the Australian-based estimates. The effect sizes (ESs) of the associations with all seven oral health variables were similar for the UK (ES=0.26 to 0.49), USA (ES=0.31 to 0.48) and Australian-based (ES=0.31 to 0.46) estimates. EQ-5D was correlated with global dental health for the UK (rho=0.29), USA (rho=0.30) and Australian-based estimates (rho=0.30), and correlations with global general health were the same (rho=0.42) for the UK, USA and Australian-based estimates. EQ-5D exhibited equivalent discriminative validity and convergent validity in relation to oral health variables for the UK, USA and Australian-based estimates.

Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

NASA Astrophysics Data System (ADS)

Quesada-Montano, Beatriz; Westerberg, Ida K.; Fuentes-Andino, Diana; Hidalgo-Leon, Hugo; Halldin, Sven

2017-04-01

Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information - to locally observed discharge - can be used to constrain model parameter uncertainty for ungauged catchments. Climate variability exerts a strong influence on streamflow variability on long and short time scales, in particular in the Central-American region. We therefore explored the use of climate variability knowledge to constrain the simulated discharge uncertainty of a conceptual hydrological model applied to a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty we first rejected parameter relationships that disagreed with our understanding of the system. We then assessed how well climate-based constraints applied at long-term, inter-annual and intra-annual time scales could constrain model uncertainty. Finally, we compared the climate-based constraints to a constraint on low-flow statistics based on information obtained from global maps. We evaluated our method in terms of the ability of the model to reproduce the observed hydrograph and the active catchment processes in terms of two efficiency measures, a statistical consistency measure, a spread measure and 17 hydrological signatures. We found that climate variability knowledge was useful for reducing model uncertainty, in particular, unrealistic representation of deep groundwater processes. The constraints based on global maps of low-flow statistics provided more constraining information than those based on climate variability, but the latter rejected slow rainfall-runoff representations that the low flow statistics did not reject. The use of such knowledge, together with information on low-flow statistics and constraints on parameter relationships showed to be useful to constrain model uncertainty for an - assumed to be - ungauged basin. This shows that our method is promising for reconstructing long-term flow data for ungauged catchments on the Pacific side of Central America, and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.

PubMed

Brown, Patrick T; Li, Wenhong; Cordero, Eugene C; Mauget, Steven A

2015-04-21

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise

PubMed Central

Brown, Patrick T.; Li, Wenhong; Cordero, Eugene C.; Mauget, Steven A.

2015-01-01

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal. PMID:25898351

Variability and Extremes of Precipitation in the Global Climate as Determined by the 25-Year GEWEX/GPCP Data Set

NASA Technical Reports Server (NTRS)

Adler, R. F.; Gu, G.; Curtis, S.; Huffman, G. J.; Bolvin, D. T.; Nelkin, E. J.

2005-01-01

The Global Precipitation Climatology Project (GPCP) 25-year precipitation data set is used to evaluate the variability and extremes on global and regional scales. The variability of precipitation year-to-year is evaluated in relation to the overall lack of a significant global trend and to climate events such as ENSO and volcanic eruptions. The validity of conclusions and limitations of the data set are checked by comparison with independent data sets (e.g., TRMM). The GPCP data set necessarily has a heterogeneous time series of input data sources, so part of the assessment described above is to test the initial results for potential influence by major data boundaries in the record. Regional trends, or inter-decadal changes, are also analyzed to determine validity and correlation with other long-term data sets related to the hydrological cycle (e.g., clouds and ocean surface fluxes). Statistics of extremes (both wet and dry) are analyzed at the monthly time scale for the 25 years. A preliminary result of increasing frequency of extreme monthly values will be a focus to determine validity. Daily values for an eight-year are also examined for variation in extremes and compared to the longer monthly-based study.

Shape Optimization of Supersonic Turbines Using Response Surface and Neural Network Methods

NASA Technical Reports Server (NTRS)

Papila, Nilay; Shyy, Wei; Griffin, Lisa W.; Dorney, Daniel J.

2001-01-01

Turbine performance directly affects engine specific impulse, thrust-to-weight ratio, and cost in a rocket propulsion system. A global optimization framework combining the radial basis neural network (RBNN) and the polynomial-based response surface method (RSM) is constructed for shape optimization of a supersonic turbine. Based on the optimized preliminary design, shape optimization is performed for the first vane and blade of a 2-stage supersonic turbine, involving O(10) design variables. The design of experiment approach is adopted to reduce the data size needed by the optimization task. It is demonstrated that a major merit of the global optimization approach is that it enables one to adaptively revise the design space to perform multiple optimization cycles. This benefit is realized when an optimal design approaches the boundary of a pre-defined design space. Furthermore, by inspecting the influence of each design variable, one can also gain insight into the existence of multiple design choices and select the optimum design based on other factors such as stress and materials considerations.

Towards an integrated set of surface meterological observations for climate science and applications

NASA Astrophysics Data System (ADS)

Dunn, Robert; Thorne, Peter

2017-04-01

We cannot predict what is not observed, and we cannot analyse what is not archived. To meet current scientific and societal demands, as well as future requirements for climate services, it is vital that the management and curation of land-based meteorological data holdings is improved. A comprehensive global set of data holdings, of known provenance, integrated across both climate variable and timescale are required to meet the wide range of user needs. Presently, the land-based holdings are highly fractured into global, region and national holdings for different variables and timescales, from a variety of sources, and in a mixture of formats. We present a high level overview, based on broad community input, of the steps that are required to bring about this integration and progress towards such a database. Any long-term, international, program creating such an integrated database will transform the our collective ability to provide societally relevant research, analysis and predictions across the globe.

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.

Human impact on sediment fluxes within the Blue Nile and Atbara River basins

NASA Astrophysics Data System (ADS)

Balthazar, Vincent; Vanacker, Veerle; Girma, Atkilt; Poesen, Jean; Golla, Semunesh

2013-01-01

A regional assessment of the spatial variability in sediment yields allows filling the gap between detailed, process-based understanding of erosion at field scale and empirical sediment flux models at global scale. In this paper, we focus on the intrabasin variability in sediment yield within the Blue Nile and Atbara basins as biophysical and anthropogenic factors are presumably acting together to accelerate soil erosion. The Blue Nile and Atbara River systems are characterized by an important spatial variability in sediment fluxes, with area-specific sediment yield (SSY) values ranging between 4 and 4935 t/km2/y. Statistical analyses show that 41% of the observed variation in SSY can be explained by remote sensing proxy data of surface vegetation cover, rainfall intensity, mean annual temperature, and human impact. The comparison of a locally adapted regression model with global predictive sediment flux models indicates that global flux models such as the ART and BQART models are less suited to capture the spatial variability in area-specific sediment yields (SSY), but they are very efficient to predict absolute sediment yields (SY). We developed a modified version of the BQART model that estimates the human influence on sediment yield based on a high resolution composite measure of local human impact (human footprint index) instead of countrywide estimates of GNP/capita. Our modified version of the BQART is able to explain 80% of the observed variation in SY for the Blue Nile and Atbara basins and thereby performs only slightly less than locally adapted regression models.

Analysis and modeling of wafer-level process variability in 28 nm FD-SOI using split C-V measurements

NASA Astrophysics Data System (ADS)

Pradeep, Krishna; Poiroux, Thierry; Scheer, Patrick; Juge, André; Gouget, Gilles; Ghibaudo, Gérard

2018-07-01

This work details the analysis of wafer level global process variability in 28 nm FD-SOI using split C-V measurements. The proposed approach initially evaluates the native on wafer process variability using efficient extraction methods on split C-V measurements. The on-wafer threshold voltage (VT) variability is first studied and modeled using a simple analytical model. Then, a statistical model based on the Leti-UTSOI compact model is proposed to describe the total C-V variability in different bias conditions. This statistical model is finally used to study the contribution of each process parameter to the total C-V variability.

Understanding the Longitudinal Variability of Equatorial Electrodynamics using integrated Ground- and Space-based Observations

NASA Astrophysics Data System (ADS)

Yizengaw, E.; Moldwin, M.; Zesta, E.

2015-12-01

The currently funded African Meridian B-Field Education and Research (AMBER) magnetometer array comprises more than thirteen magnetometers stationed globally in the vicinity of geomagnetic equator. One of the main objectives of AMBER network is to understand the longitudinal variability of equatorial electrodynamics as function of local time, magnetic activity, and season. While providing complete meridian observation in the region and filling the largest land-based gap in global magnetometer coverage, the AMBER array addresses two fundamental areas of space physics: first, the processes governing electrodynamics of the equatorial ionosphere as a function of latitude (or L-shell), local time, longitude, magnetic activity, and season, and second, ULF pulsation strength at low/mid-latitude regions and its connection with equatorial electrojet and density fluctuation. The global AMBER network can also be used to augment observations from space-based instruments, such us the triplet SWARM mission and the upcoming ICON missions. Thus, in coordination with space-based and other ground-based observations, the AMBER magnetometer network provides a great opportunity to understand the electrodynamics that governs equatorial ionosphere motions. In this paper we present the longitudinal variability of the equatorial electrodynamics using the combination of instruments onboard SWARM and C/NOFS satellites and ground-based AMBER network. Both ground- and pace-based observations show stronger dayside and evening sector equatorial electrodynamics in the American and Asian sectors compared to the African sector. On the other hand, the African sector is home to stronger and year-round ionospheric bubbles/irregularities compared to the American and Asian sectors. This raises the question if the evening sector equatorial electrodynamics (vertical drift), which is believed to be the main cause for the enhancement of Rayleigh-Taylor (RT) instability growth rate, is stronger in the American sector and weaker in the African sector - why are the occurrence and amplitude of equatorial irregularities stronger in the African sector?

Mid-Pliocene to Early Pleistocene land and sea surface temperature history of NW Australia based on organic geochemical proxies

NASA Astrophysics Data System (ADS)

Smith, R. A.; Castañeda, I. S.; Henderiks, J.; Christensen, B. A.; De Vleeschouwer, D.; Renema, W.; Groeneveld, J.; Bogus, K.; Gallagher, S. J.; Fulthorpe, C.; Expedition 356 Scientists, I.

2017-12-01

IODP Expedition 356 Site U1463 is located off the coast of NW Australia, and is sensitive to Indonesian Throughflow (ITF) variability. The ITF is a critical ocean gateway that affects global thermohaline circulation, and regulates the movement of water from the Pacific Ocean into the Indian Ocean. However, despite its importance to the global climate system, few SST reconstructions exist for this region that span the Plio-Pleistocene. Here we investigate both the land and sea-surface temperature (SST) history of NW Australia to constrain ITF variability across the Plio-Pleistocene interval. We apply multiple organic geochemical proxies to this site from 3.4-2.6 Ma, which includes the mid-Pliocene warm period, characterized by slightly higher (2-3°C) global temperatures and similar CO2 concentrations to modern values (e.g. Badger et al. 2013; Bartoli et al., 2011; Dowsett et al., 2009; Hönisch et al., 2009; Pagani et al. 2009; Raymo et al., 1996). SST was reconstructed using TEX86, based on isoprenoid glycerol dialkyl glycerol tetraethers (iGDGTs), and the long-chain diol index (LDI), based on the ratio of diols produced by marine diatoms (Rampen et al., 2012). The Uk'37 index, based on long-chain ketones, was analyzed but cannot be applied as a SST proxy at this site due to the influence of coastal alkenone producers. Additionally, a continental air temperature record was developed using the MBT'5ME proxy, based on branched GDGTs (De Jonge et al., 2014; Weijers et al., 2007). We find that TEX86, LDI and MBT'5Me exhibit similar trends and show relatively warm and stable temperatures from 3.5-2.4 Ma followed by a gradual cooling of 3-4°C from 2.4-1.5 Ma. This cooling corresponds with an arid interval previously identified on the same core by Christensen et al. (2017). Furthermore, we find that the TEX86 record agrees closely with the LR04 global benthic δ18O stack (Lisiecki and Raymo, 2005) and captures glacial/interglacial periods including Marine Isotope Stage M2. Our results help to constrain climatic changes across the mid-Pliocene warm period and aim to improve future climate models and elucidate the role of the ITF in driving global climate variability.

Mass spectrometry-based biomarker discovery: toward a global proteome index of individuality.

PubMed

Hawkridge, Adam M; Muddiman, David C

2009-01-01

Biomarker discovery and proteomics have become synonymous with mass spectrometry in recent years. Although this conflation is an injustice to the many essential biomolecular techniques widely used in biomarker-discovery platforms, it underscores the power and potential of contemporary mass spectrometry. Numerous novel and powerful technologies have been developed around mass spectrometry, proteomics, and biomarker discovery over the past 20 years to globally study complex proteomes (e.g., plasma). However, very few large-scale longitudinal studies have been carried out using these platforms to establish the analytical variability relative to true biological variability. The purpose of this review is not to cover exhaustively the applications of mass spectrometry to biomarker discovery, but rather to discuss the analytical methods and strategies that have been developed for mass spectrometry-based biomarker-discovery platforms and to place them in the context of the many challenges and opportunities yet to be addressed.

Orbit-related sea level errors for TOPEX altimetry at seasonal to decadal timescales

NASA Astrophysics Data System (ADS)

Esselborn, Saskia; Rudenko, Sergei; Schöne, Tilo

2018-03-01

Interannual to decadal sea level trends are indicators of climate variability and change. A major source of global and regional sea level data is satellite radar altimetry, which relies on precise knowledge of the satellite's orbit. Here, we assess the error budget of the radial orbit component for the TOPEX/Poseidon mission for the period 1993 to 2004 from a set of different orbit solutions. The errors for seasonal, interannual (5-year), and decadal periods are estimated on global and regional scales based on radial orbit differences from three state-of-the-art orbit solutions provided by different research teams: the German Research Centre for Geosciences (GFZ), the Groupe de Recherche de Géodésie Spatiale (GRGS), and the Goddard Space Flight Center (GSFC). The global mean sea level error related to orbit uncertainties is of the order of 1 mm (8 % of the global mean sea level variability) with negligible contributions on the annual and decadal timescales. In contrast, the orbit-related error of the interannual trend is 0.1 mm yr-1 (27 % of the corresponding sea level variability) and might hamper the estimation of an acceleration of the global mean sea level rise. For regional scales, the gridded orbit-related error is up to 11 mm, and for about half the ocean the orbit error accounts for at least 10 % of the observed sea level variability. The seasonal orbit error amounts to 10 % of the observed seasonal sea level signal in the Southern Ocean. At interannual and decadal timescales, the orbit-related trend uncertainties reach regionally more than 1 mm yr-1. The interannual trend errors account for 10 % of the observed sea level signal in the tropical Atlantic and the south-eastern Pacific. For decadal scales, the orbit-related trend errors are prominent in a several regions including the South Atlantic, western North Atlantic, central Pacific, South Australian Basin, and the Mediterranean Sea. Based on a set of test orbits calculated at GFZ, the sources of the observed orbit-related errors are further investigated. The main contributors on all timescales are uncertainties in Earth's time-variable gravity field models and on annual to interannual timescales discrepancies of the tracking station subnetworks, i.e. satellite laser ranging (SLR) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS).

Climate Change of 4°C GlobalWarming above Pre-industrial Levels

NASA Astrophysics Data System (ADS)

Wang, Xiaoxin; Jiang, Dabang; Lang, Xianmei

2018-07-01

Using a set of numerical experiments from 39 CMIP5 climate models, we project the emergence time for 4°C global warming with respect to pre-industrial levels and associated climate changes under the RCP8.5 greenhouse gas concentration scenario. Results show that, according to the 39 models, the median year in which 4°C global warming will occur is 2084. Based on the median results of models that project a 4°C global warming by 2100, land areas will generally exhibit stronger warming than the oceans annually and seasonally, and the strongest enhancement occurs in the Arctic, with the exception of the summer season. Change signals for temperature go outside its natural internal variabilities globally, and the signal-tonoise ratio averages 9.6 for the annual mean and ranges from 6.3 to 7.2 for the seasonal mean over the globe, with the greatest values appearing at low latitudes because of low noise. Decreased precipitation generally occurs in the subtropics, whilst increased precipitation mainly appears at high latitudes. The precipitation changes in most of the high latitudes are greater than the background variability, and the global mean signal-to-noise ratio is 0.5 and ranges from 0.2 to 0.4 for the annual and seasonal means, respectively. Attention should be paid to limiting global warming to 1.5°C, in which case temperature and precipitation will experience a far more moderate change than the natural internal variability. Large inter-model disagreement appears at high latitudes for temperature changes and at mid and low latitudes for precipitation changes. Overall, the intermodel consistency is better for temperature than for precipitation.

Land surface temperature over global deserts: Means, variability, and trends

NASA Astrophysics Data System (ADS)

Zhou, Chunlüe; Wang, Kaicun

2016-12-01

Land surface air temperature (LSAT) has been a widely used metric to study climate change. Weather observations of LSAT are the fundamental data for climate change studies and provide key evidence of global warming. However, there are very few meteorological observations over deserts due to their uninhabitable environment. This study fills this gap and provides independent evidence using satellite-derived land surface temperatures (LSTs), benefiting from their global coverage. The frequency of clear sky from MODerate Resolution Imaging Spectroradiometer (MODIS) LST data over global deserts was found to be greater than 94% for the 2002-2015 period. Our results show that MODIS LST has a bias of 1.36°C compared to ground-based observations collected at 31 U.S. Climate Reference Network (USCRN) stations, with a standard deviation of 1.83°C. After bias correction, MODIS LST was used to evaluate existing reanalyses, including ERA-Interim, Japanese 55-year Reanalysis (JRA-55), Modern-Era Retrospective Analysis for Research and Applications (MERRA), MERRA-land, National Centers for Environmental Prediction (NCEP)-R1, and NCEP-R2. The reanalyses accurately reproduce the seasonal cycle and interannual variability of the LSTs, but their multiyear means and trends of LSTs exhibit large uncertainties. The multiyear averaged LST over global deserts is 23.5°C from MODIS and varies from 20.8°C to 24.5°C in different reanalyses. The MODIS LST over global deserts increased by 0.25°C/decade from 2002 to 2015, whereas the reanalyses estimated a trend varying from -0.14 to 0.10°C/decade. The underestimation of the LST trend by the reanalyses occurs for approximately 70% of the global deserts, likely due to the imperfect performance of the reanalyses in reproducing natural climate variability.

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS)

NASA Astrophysics Data System (ADS)

Chen, Zhuoqi; Chen, Jing M.; Zhang, Shupeng; Zheng, Xiaogu; Ju, Weiming; Mo, Gang; Lu, Xiaoliang

2017-12-01

The Global Carbon Assimilation System that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C (Vmax25), the temperature sensitivity of ecosystem respiration (Q10), and the soil carbon pool size. The optimization is performed at the global scale at 1° resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/midlatitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at middle-high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during nongrowing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Data Set for Earth System. The results also suggest that atmospheric CO2 data are a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

Adaptive Synchronization of Fractional Order Complex-Variable Dynamical Networks via Pinning Control

NASA Astrophysics Data System (ADS)

Ding, Da-Wei; Yan, Jie; Wang, Nian; Liang, Dong

2017-09-01

In this paper, the synchronization of fractional order complex-variable dynamical networks is studied using an adaptive pinning control strategy based on close center degree. Some effective criteria for global synchronization of fractional order complex-variable dynamical networks are derived based on the Lyapunov stability theory. From the theoretical analysis, one concludes that under appropriate conditions, the complex-variable dynamical networks can realize the global synchronization by using the proper adaptive pinning control method. Meanwhile, we succeed in solving the problem about how much coupling strength should be applied to ensure the synchronization of the fractional order complex networks. Therefore, compared with the existing results, the synchronization method in this paper is more general and convenient. This result extends the synchronization condition of the real-variable dynamical networks to the complex-valued field, which makes our research more practical. Finally, two simulation examples show that the derived theoretical results are valid and the proposed adaptive pinning method is effective. Supported by National Natural Science Foundation of China under Grant No. 61201227, National Natural Science Foundation of China Guangdong Joint Fund under Grant No. U1201255, the Natural Science Foundation of Anhui Province under Grant No. 1208085MF93, 211 Innovation Team of Anhui University under Grant Nos. KJTD007A and KJTD001B, and also supported by Chinese Scholarship Council

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

PubMed

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

Cooperative Coevolution with Formula-Based Variable Grouping for Large-Scale Global Optimization.

PubMed

Wang, Yuping; Liu, Haiyan; Wei, Fei; Zong, Tingting; Li, Xiaodong

2017-08-09

For a large-scale global optimization (LSGO) problem, divide-and-conquer is usually considered an effective strategy to decompose the problem into smaller subproblems, each of which can then be solved individually. Among these decomposition methods, variable grouping is shown to be promising in recent years. Existing variable grouping methods usually assume the problem to be black-box (i.e., assuming that an analytical model of the objective function is unknown), and they attempt to learn appropriate variable grouping that would allow for a better decomposition of the problem. In such cases, these variable grouping methods do not make a direct use of the formula of the objective function. However, it can be argued that many real-world problems are white-box problems, that is, the formulas of objective functions are often known a priori. These formulas of the objective functions provide rich information which can then be used to design an effective variable group method. In this article, a formula-based grouping strategy (FBG) for white-box problems is first proposed. It groups variables directly via the formula of an objective function which usually consists of a finite number of operations (i.e., four arithmetic operations "[Formula: see text]", "[Formula: see text]", "[Formula: see text]", "[Formula: see text]" and composite operations of basic elementary functions). In FBG, the operations are classified into two classes: one resulting in nonseparable variables, and the other resulting in separable variables. In FBG, variables can be automatically grouped into a suitable number of non-interacting subcomponents, with variables in each subcomponent being interdependent. FBG can easily be applied to any white-box problem and can be integrated into a cooperative coevolution framework. Based on FBG, a novel cooperative coevolution algorithm with formula-based variable grouping (so-called CCF) is proposed in this article for decomposing a large-scale white-box problem into several smaller subproblems and optimizing them respectively. To further enhance the efficiency of CCF, a new local search scheme is designed to improve the solution quality. To verify the efficiency of CCF, experiments are conducted on the standard LSGO benchmark suites of CEC'2008, CEC'2010, CEC'2013, and a real-world problem. Our results suggest that the performance of CCF is very competitive when compared with those of the state-of-the-art LSGO algorithms.

Uncertainties in discharge projections in consequence of climate change

NASA Astrophysics Data System (ADS)

Liebert, J.; Düthmann, D.; Berg, P.; Feldmann, H.; Ihringer, J.; Kunstmann, H.; Merz, B.; Ott, I.; Schädler, G.; Wagner, S.

2012-04-01

The fourth assessment report of the IPCC summarizes possible effects of the global climate change. For Europe an increasing variability of temperature and precipitation is expected. While the increasing temperature is projected almost uniformly for Europe, for precipitation the models indicate partly heterogeneous tendencies. In order to maintain current safety-standards in the infrastructure of our various water management systems, the possible future floods discharges are very often a central question. In the planning and operation of water infrastructure systems uncertainties considerations have an important function. In times of climate change the analyses of measured historical gauge data (normally 30 - 80 years) are not sufficient enough, because even significant trends are only valid in the analyzed time period and extrapolations are exceedingly difficult. Therefore combined climate and hydrological modeling for scenario based projections become more and more popular. Regarding that adaptation measures in water infrastructure are in general very time-consuming and cost intensive qualified questions to the variability and uncertainty of model based results are important as well. The CEDIM-Project "Flood hazards in a changing climate" is focusing on both: future changes in flood discharge and assess the uncertainties that are involved in such model based future predictions. In detail the study bases on an ensemble of hydrological model (HM) simulations in 3 representative small to medium sized German river catchments (Ammer, Mulde and Ruhr). The meteorological Input bases on 2 high resolution (7 km) regional climate models (RCM) driven by 2 global climate models (GCM) for the near future (2021 - 2050) following the A1B emission scenario (SRES). Two of the catchments (Ruhr and Mulde) have sub-mountainous and one (Ammer) has alpine character. Besides analyzing the future changes in discharge in the catchments, the describing and potential quantification of the variability of the results, based on the different driving data, regionalization methods, spatial resolutions and model types, is one main goal of the study and should stay in the focus of the poster. The general result is a large variability in the discharge projection. The identified variabilities are in the annual regime mainly attributable to different causes in the used model chain (GCM-RCM-HM). In winter the global climate models (GCM) bring the main uncertainties in the future projection. In summer the main variability refers to the meteorological downscaling to the regional scale (RCM) in combination with the hydrological modeling (HM). But with an appropriate ensemble statistic are despite the large variabilities mean future tendencies detectable. The Ruhr catchment shows tendencies to future higher flood discharges and in the Ammer and Mulde catchments are no significant changes expected.

Separating decadal global water cycle variability from sea level rise.

PubMed

Hamlington, B D; Reager, J T; Lo, M-H; Karnauskas, K B; Leben, R R

2017-04-20

Under a warming climate, amplification of the water cycle and changes in precipitation patterns over land are expected to occur, subsequently impacting the terrestrial water balance. On global scales, such changes in terrestrial water storage (TWS) will be reflected in the water contained in the ocean and can manifest as global sea level variations. Naturally occurring climate-driven TWS variability can temporarily obscure the long-term trend in sea level rise, in addition to modulating the impacts of sea level rise through natural periodic undulation in regional and global sea level. The internal variability of the global water cycle, therefore, confounds both the detection and attribution of sea level rise. Here, we use a suite of observations to quantify and map the contribution of TWS variability to sea level variability on decadal timescales. In particular, we find that decadal sea level variability centered in the Pacific Ocean is closely tied to low frequency variability of TWS in key areas across the globe. The unambiguous identification and clean separation of this component of variability is the missing step in uncovering the anthropogenic trend in sea level and understanding the potential for low-frequency modulation of future TWS impacts including flooding and drought.

Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability

NASA Astrophysics Data System (ADS)

Wada, Y.; van Beek, L. P. H.; Bierkens, M. F. P.

2011-08-01

During the past decades, human water use more than doubled, yet available freshwater resources are finite. As a result, water scarcity has been prevalent in various regions of the world. Here, we present the first global assessment of past development of water scarcity considering not only climate variability but also growing water demand, desalinated water use and non-renewable groundwater abstraction over the period 1960-2001 at a spatial resolution of 0.5°. Agricultural water demand is estimated based on past extents of irrigated areas and livestock densities. We approximate past economic development based on GDP, energy and household consumption and electricity production, which is subsequently used together with population numbers to estimate industrial and domestic water demand. Climate variability is expressed by simulated blue water availability defined by freshwater in rivers, lakes and reservoirs by means of the global hydrological model PCR-GLOBWB. The results show a drastic increase in the global population living under water-stressed conditions (i.e., moderate to high water stress) due to the growing water demand, primarily for irrigation, which more than doubled from 1708/818 to 3708/1832 km3 yr-1 (gross/net) over the period 1960-2000. We estimate that 800 million people or 27 % of the global population were under water-stressed conditions for 1960. This number increased to 2.6 billion or 43 % for 2000. Our results indicate that increased water demand is the decisive factor for the heightened water stress, enhancing the intensity of water stress up to 200 %, while climate variability is often the main determinant of onsets for extreme events, i.e. major droughts. However, our results also suggest that in several emerging and developing economies (e.g., India, Turkey, Romania and Cuba) some of the past observed droughts were anthropogenically driven due to increased water demand rather than being climate-induced. In those countries, it can be seen that human water consumption is a major factor contributing to the high intensity of major drought events.

Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability

NASA Astrophysics Data System (ADS)

Wada, Y.; van Beek, L. P. H.; Bierkens, M. F. P.

2011-12-01

During the past decades, human water use has more than doubled, yet available freshwater resources are finite. As a result, water scarcity has been prevalent in various regions of the world. Here, we present the first global assessment of past development of water stress considering not only climate variability but also growing water demand, desalinated water use and non-renewable groundwater abstraction over the period 1960-2001 at a spatial resolution of 0.5°. Agricultural water demand is estimated based on past extents of irrigated areas and livestock densities. We approximate past economic development based on GDP, energy and household consumption and electricity production, which are subsequently used together with population numbers to estimate industrial and domestic water demand. Climate variability is expressed by simulated blue water availability defined by freshwater in rivers, lakes, wetlands and reservoirs by means of the global hydrological model PCR-GLOBWB. We thus define blue water stress by comparing blue water availability with corresponding net total blue water demand by means of the commonly used, Water Scarcity Index. The results show a drastic increase in the global population living under water-stressed conditions (i.e. moderate to high water stress) due to growing water demand, primarily for irrigation, which has more than doubled from 1708/818 to 3708/1832 km3 yr-1 (gross/net) over the period 1960-2000. We estimate that 800 million people or 27% of the global population were living under water-stressed conditions for 1960. This number is eventually increased to 2.6 billion or 43% for 2000. Our results indicate that increased water demand is a decisive factor for heightened water stress in various regions such as India and North China, enhancing the intensity of water stress up to 200%, while climate variability is often a main determinant of extreme events. However, our results also suggest that in several emerging and developing economies (e.g. India, Turkey, Romania and Cuba) some of past extreme events were anthropogenically driven due to increased water demand rather than being climate-induced.

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

Benchmarking the mesoscale variability in global ocean eddy-permitting numerical systems

NASA Astrophysics Data System (ADS)

Cipollone, Andrea; Masina, Simona; Storto, Andrea; Iovino, Doroteaciro

2017-10-01

The role of data assimilation procedures on representing ocean mesoscale variability is assessed by applying eddy statistics to a state-of-the-art global ocean reanalysis (C-GLORS), a free global ocean simulation (performed with the NEMO system) and an observation-based dataset (ARMOR3D) used as an independent benchmark. Numerical results are computed on a 1/4 ∘ horizontal grid (ORCA025) and share the same resolution with ARMOR3D dataset. This "eddy-permitting" resolution is sufficient to allow ocean eddies to form. Further to assessing the eddy statistics from three different datasets, a global three-dimensional eddy detection system is implemented in order to bypass the need of regional-dependent definition of thresholds, typical of commonly adopted eddy detection algorithms. It thus provides full three-dimensional eddy statistics segmenting vertical profiles from local rotational velocities. This criterion is crucial for discerning real eddies from transient surface noise that inevitably affects any two-dimensional algorithm. Data assimilation enhances and corrects mesoscale variability on a wide range of features that cannot be well reproduced otherwise. The free simulation fairly reproduces eddies emerging from western boundary currents and deep baroclinic instabilities, while underestimates shallower vortexes that populate the full basin. The ocean reanalysis recovers most of the missing turbulence, shown by satellite products , that is not generated by the model itself and consistently projects surface variability deep into the water column. The comparison with the statistically reconstructed vertical profiles from ARMOR3D show that ocean data assimilation is able to embed variability into the model dynamics, constraining eddies with in situ and altimetry observation and generating them consistently with local environment.

Detection and Attribution of Temperature Trends in the Presence of Natural Variability

NASA Astrophysics Data System (ADS)

Wallace, J. M.

2014-12-01

The fingerprint of human-induced global warming stands out clearly above the noise In the time series of global-mean temperature, but not local temperature. At extratropical latitudes over land the standard error of 50-year linear temperature trends at a fixed point is as large as the cumulative rise in global-mean temperature over the past century. Much of the samping variability in local temperature trends is "dynamically-induced", i.e., attributable to the fact that the seasonally-varying mean circulation varies substantially from one year to the next and anomalous circulation patterns are generally accompanied by anomalous temperature patterns. In the presence of such large sampling variability it is virtually impossible to identify the spatial signature of greenhouse warming based on observational data or to partition observed local temperature trends into natural and human-induced components. It follows that previous IPCC assessments, which have focused on the deterministic signature of human-induced climate change, are inherently limited as to what they can tell us about the attribution of the past record of local temperature change or about how much the temperature at a particular place is likely to rise in the next few decades in response to global warming. To obtain more informative assessments of regional and local climate variability and change it will be necessary to take a probabilistic approach. Just as the use of the ensembles has contributed to more informative extended range weather predictions, large ensembles of climate model simulations can provide a statistical context for interpreting observed climate change and for framing projections of future climate. For some purposes, statistics relating to the interannual variability in the historical record can serve as a surrogate for statistics relating to the diversity of climate change scenarios in large ensembles.

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.

A Global Geographic Information System Data Base of Storm Occurrences and Other Climatic Phenomena Affecting Coastal Zones (1991) (NDP-035)

DOE Data Explorer

Birdwell, Kevub R. [Murray State University, Kentucky; Daniels, Richard C.

2012-01-01

This NDP is unique in that it represents CDIAC's first offering of ARC/INFOTM export data files and equivalent flat ASCII data files that may be used by raster or vector geographic information systems (GISs). The data set contains 61 variables, including information on tropical storms, hurricanes, super typhoons, extratropical cyclogeneses, polar lows, cyclonicity, influence of winds in monsoon regions, and sea-ice concentrations. Increased availability of source data has made it possible to extend the area of these data variables to regional or global coverages. All data variables except five are referenced to 1° × 1° or 5° × 5° grid cells of latitude and longitude. These data help meet the demand for new and improved climatologies of storm events and may be used in climate research studies, including the verification of general circulation models and the calculation of storm-recurrence intervals.

CMIP5 land surface models systematically underestimate inter-annual variability of net ecosystem exchange in semi-arid southwestern North America.

NASA Astrophysics Data System (ADS)

MacBean, N.; Scott, R. L.; Biederman, J. A.; Vuichard, N.; Hudson, A.; Barnes, M.; Fox, A. M.; Smith, W. K.; Peylin, P. P.; Maignan, F.; Moore, D. J.

2017-12-01

Recent studies based on analysis of atmospheric CO2 inversions, satellite data and terrestrial biosphere model simulations have suggested that semi-arid ecosystems play a dominant role in the interannual variability and long-term trend in the global carbon sink. These studies have largely cited the response of vegetation activity to changing moisture availability as the primary mechanism of variability. However, some land surface models (LSMs) used in these studies have performed poorly in comparison to satellite-based observations of vegetation dynamics in semi-arid regions. Further analysis is therefore needed to ensure semi-arid carbon cycle processes are well represented in global scale LSMs before we can fully establish their contribution to the global carbon cycle. In this study, we evaluated annual net ecosystem exchange (NEE) simulated by CMIP5 land surface models using observations from 20 Ameriflux sites across semi-arid southwestern North America. We found that CMIP5 models systematically underestimate the magnitude and sign of NEE inter-annual variability; therefore, the true role of semi-arid regions in the global carbon cycle may be even more important than previously thought. To diagnose the factors responsible for this bias, we used the ORCHIDEE LSM to test different climate forcing data, prescribed vegetation fractions and model structures. Climate and prescribed vegetation do contribute to uncertainty in annual NEE simulations, but the bias is primarily caused by incorrect timing and magnitude of peak gross carbon fluxes. Modifications to the hydrology scheme improved simulations of soil moisture in comparison to data. This in turn improved the seasonal cycle of carbon uptake due to a more realistic limitation on photosynthesis during water stress. However, the peak fluxes are still too low, and phenology is poorly represented for desert shrubs and grasses. We provide suggestions on model developments needed to tackle these issues in the future.

Fire and deforestation dynamics in Amazonia (1973-2014).

PubMed

van Marle, Margreet J E; Field, Robert D; van der Werf, Guido R; Estrada de Wagt, Ivan A; Houghton, Richard A; Rizzo, Luciana V; Artaxo, Paulo; Tsigaridis, Kostas

2017-01-01

Consistent long-term estimates of fire emissions are important to understand the changing role of fire in the global carbon cycle and to assess the relative importance of humans and climate in shaping fire regimes. However, there is limited information on fire emissions from before the satellite era. We show that in the Amazon region, including the Arc of Deforestation and Bolivia, visibility observations derived from weather stations could explain 61% of the variability in satellite-based estimates of bottom-up fire emissions since 1997 and 42% of the variability in satellite-based estimates of total column carbon monoxide concentrations since 2001. This enabled us to reconstruct the fire history of this region since 1973 when visibility information became available. Our estimates indicate that until 1987 relatively few fires occurred in this region and that fire emissions increased rapidly over the 1990s. We found that this pattern agreed reasonably well with forest loss data sets, indicating that although natural fires may occur here, deforestation and degradation were the main cause of fires. Compared to fire emissions estimates based on Food and Agricultural Organization's Global Forest and Resources Assessment data, our estimates were substantially lower up to the 1990s, after which they were more in line. These visibility-based fire emissions data set can help constrain dynamic global vegetation models and atmospheric models with a better representation of the complex fire regime in this region.

The Little Ice Age was 1.0-1.5 °C cooler than current warm period according to LOD and NAO

NASA Astrophysics Data System (ADS)

Mazzarella, Adriano; Scafetta, Nicola

2018-02-01

We study the yearly values of the length of day (LOD, 1623-2016) and its link to the zonal index (ZI, 1873-2003), the Northern Atlantic oscillation index (NAO, 1659-2000) and the global sea surface temperature (SST, 1850-2016). LOD is herein assumed to be mostly the result of the overall circulations occurring within the ocean-atmospheric system. We find that LOD is negatively correlated with the global SST and with both the integral function of ZI and NAO, which are labeled as IZI and INAO. A first result is that LOD must be driven by a climatic change induced by an external (e.g. solar/astronomical) forcing since internal variability alone would have likely induced a positive correlation among the same variables because of the conservation of the Earth's angular momentum. A second result is that the high correlation among the variables implies that the LOD and INAO records can be adopted as global proxies to reconstruct past climate change. Tentative global SST reconstructions since the seventeenth century suggest that around 1700, that is during the coolest period of the Little Ice Age (LIA), SST could have been about 1.0-1.5 °C cooler than the 1950-1980 period. This estimated LIA cooling is greater than what some multiproxy global climate reconstructions suggested, but it is in good agreement with other more recent climate reconstructions including those based on borehole temperature data.

Towards a Global Water Scarcity Risk Assessment Framework: Incorporation of Probability Distributions and Hydro-Climatic Variability

NASA Technical Reports Server (NTRS)

Veldkamp, T. I. E.; Wada, Y.; Aerts, J. C. J. H.; Ward, P. J.

2016-01-01

Changing hydro-climatic and socioeconomic conditions increasingly put pressure on fresh water resources and are expected to aggravate water scarcity conditions towards the future. Despite numerous calls for risk-based water scarcity assessments, a global-scale framework that includes UNISDR's definition of risk does not yet exist. This study provides a first step towards such a risk based assessment, applying a Gamma distribution to estimate water scarcity conditions at the global scale under historic and future conditions, using multiple climate change and population growth scenarios. Our study highlights that water scarcity risk, expressed in terms of expected annual exposed population, increases given all future scenarios, up to greater than 56.2% of the global population in 2080. Looking at the drivers of risk, we find that population growth outweigh the impacts of climate change at global and regional scales. Using a risk-based method to assess water scarcity, we show the results to be less sensitive than traditional water scarcity assessments to the use of fixed threshold to represent different levels of water scarcity. This becomes especially important when moving from global to local scales, whereby deviations increase up to 50% of estimated risk levels.

GLEAM version 3: Global Land Evaporation Datasets and Model

NASA Astrophysics Data System (ADS)

Martens, B.; Miralles, D. G.; Lievens, H.; van der Schalie, R.; de Jeu, R.; Fernandez-Prieto, D.; Verhoest, N.

2015-12-01

Terrestrial evaporation links energy, water and carbon cycles over land and is therefore a key variable of the climate system. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to limitations in in situ measurements. As a result, several methods have risen to estimate global patterns of land evaporation from satellite observations. However, these algorithms generally differ in their approach to model evaporation, resulting in large differences in their estimates. One of these methods is GLEAM, the Global Land Evaporation: the Amsterdam Methodology. GLEAM estimates terrestrial evaporation based on daily satellite observations of meteorological variables, vegetation characteristics and soil moisture. Since the publication of the first version of the algorithm (2011), the model has been widely applied to analyse trends in the water cycle and land-atmospheric feedbacks during extreme hydrometeorological events. A third version of the GLEAM global datasets is foreseen by the end of 2015. Given the relevance of having a continuous and reliable record of global-scale evaporation estimates for climate and hydrological research, the establishment of an online data portal to host these data to the public is also foreseen. In this new release of the GLEAM datasets, different components of the model have been updated, with the most significant change being the revision of the data assimilation algorithm. In this presentation, we will highlight the most important changes of the methodology and present three new GLEAM datasets and their validation against in situ observations and an alternative dataset of terrestrial evaporation (ERA-Land). Results of the validation exercise indicate that the magnitude and the spatiotemporal variability of the modelled evaporation agree reasonably well with the estimates of ERA-Land and the in situ observations. It is also shown that the performance of the revised model is higher compared to the original one.

Precipitation response to the current ENSO variability in a warming world

NASA Astrophysics Data System (ADS)

Bonfils, C.; Santer, B. D.; Phillips, T. J.; Marvel, K.; Leung, L.

2013-12-01

The major triggers of past and recent droughts include large modes of variability, such as ENSO, as well as specific and persistent patterns of sea surface temperature anomalies (SSTAs; Hoerling and Kumar, 2003, Shin et al. 2010, Schubert et al. 2009). However, alternative drought initiators are also anticipated in response to increasing greenhouse gases, potentially changing the relative contribution of ocean variability as drought initiator. They include the intensification of the current zonal wet-dry patterns (the thermodynamic mechanism, Held and Soden, 2006), a latitudinal redistribution of global precipitation (the dynamical mechanism, Seager et al. 2007, Seidel et al. 2008, Scheff and Frierson 2008) and a reduction of local soil moisture and precipitation recycling (the land-atmosphere argument). Our ultimate goal is to investigate whether the relative contribution of those mechanisms change over time in response to global warming. In this study, we first perform an EOF analysis of the 1900-1999 time series of observed global SST field and identify a simple ENSO-like (ENSOL) mode of SST variability. We show that this mode is well spatially and temporally correlated with observed worldwide regional precipitation and drought variability. We then develop concise metrics to examine the fidelity with which the CMIP5 coupled global climate models (CGCMs) capture this particular ENSO-like mode in the current climate, and their ability to replicate the observed teleconnections with precipitation. Based on the CMIP5 model projections of future climate change, we finally analyze the potential temporal variations in ENSOL to be anticipated under further global warming, as well as their associated teleconnections with precipitation (pattern, amplitude, and total response). Overall, our approach allows us to determine what will be the effect of the current ENSO-like variability (i.e., as measured with instrumental observations) on precipitation in a warming world. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and is supported, among others, by C.B. Early Career Research Program award.

Seven-Year SSM/I-Derived Global Ocean Surface Turbulent Fluxes

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Ardizzone, Joe

2000-01-01

A 7.5-year (July 1987-December 1994) dataset of daily surface specific humidity and turbulent fluxes (momentum, latent heat, and sensible heat) over global oceans has been retrieved from the Special Sensor Microwave/Imager (SSM/I) data and other data. It has a spatial resolution of 2.0 deg.x 2.5 deg. latitude-longitude. The retrieved surface specific humidity is generally accurate over global oceans as validated against the collocated radiosonde observations. The retrieved daily wind stresses and latent heat fluxes show useful accuracy as verified by those measured by the RV Moana Wave and IMET buoy in the western equatorial Pacific. The derived turbulent fluxes and input variables are also found to agree generally with the global distributions of annual-and seasonal-means of those based on 4-year (1990-93) comprehensive ocean-atmosphere data set (COADS) with adjustment in wind speeds and other climatological studies. The COADS has collected the most complete surface marine observations, mainly from merchant ships. However, ship measurements generally have poor accuracy, and variable spatial coverages. Significant differences between the retrieved and COADS-based are found in some areas of the tropical and southern extratropical oceans, reflecting the paucity of ship observations outside the northern extratropical oceans. Averaged over the global oceans, the retrieved wind stress is smaller but the latent heat flux is larger than those based on COADS. The former is suggested to be mainly due to overestimation of the adjusted ship-estimated wind speeds (depending on sea states), while the latter is suggested to be mainly due to overestimation of ship-measured dew point temperatures. The study suggests that the SSM/I-derived turbulent fluxes can be used for climate studies and coupled model validations.

Appraisal of Weather Research and Forecasting Model Downscaling of Hydro-meteorological Variables and their Applicability for Discharge Prediction: Prognostic Approach for Ungauged Basin

NASA Astrophysics Data System (ADS)

Srivastava, P. K.; Han, D.; Rico-Ramirez, M. A.; Bray, M.; Islam, T.; Petropoulos, G.; Gupta, M.

2015-12-01

Hydro-meteorological variables such as Precipitation and Reference Evapotranspiration (ETo) are the most important variables for discharge prediction. However, it is not always possible to get access to them from ground based measurements, particularly in ungauged catchments. The mesoscale model WRF (Weather Research & Forecasting model) can be used for prediction of hydro-meteorological variables. However, hydro-meteorologists would like to know how well the downscaled global data products are as compared to ground based measurements and whether it is possible to use the downscaled data for ungauged catchments. Even with gauged catchments, most of the stations have only rain and flow gauges installed. Measurements of other weather hydro-meteorological variables such as solar radiation, wind speed, air temperature, and dew point are usually missing and thus complicate the problems. In this study, for downscaling the global datasets, the WRF model is setup over the Brue catchment with three nested domains (D1, D2 and D3) of horizontal grid spacing of 81 km, 27 km and 9 km are used. The hydro-meteorological variables are downscaled using the WRF model from the National Centers for Enviromental Prediction (NCEP) reanalysis datasets and subsequently used for the ETo estimation using the Penman Monteith equation. The analysis of weather variables and precipitation are compared against the ground based datasets, which indicate that the datasets are in agreement with the observed datasets for complete monitoring period as well as during the seasons except precipitation whose performance is poorer in comparison to the measured rainfall. After a comparison, the WRF estimated precipitation and ETo are then used as a input parameter in the Probability Distributed Model (PDM) for discharge prediction. The input data and model parameter sensitivity analysis and uncertainty estimation are also taken into account for the PDM calibration and prediction following the Generalised Likelihood Uncertainty Estimation (GLUE) approach. The overall analysis suggests that the uncertainty estimates in predicted discharge using WRF downscaled ETo have comparable performance to ground based observed datasets and hence is promising for discharge prediction in the absence of ground based measurements.

HPC Aspects of Variable-Resolution Global Climate Modeling using a Multi-scale Convection Parameterization

EPA Science Inventory

High performance computing (HPC) requirements for the new generation variable grid resolution (VGR) global climate models differ from that of traditional global models. A VGR global model with 15 km grids over the CONUS stretching to 60 km grids elsewhere will have about ~2.5 tim...

Changes in hydro-meteorological conditions over tropical West Africa (1980-2015) and links to global climate

NASA Astrophysics Data System (ADS)

Ndehedehe, Christopher E.; Awange, Joseph L.; Agutu, Nathan O.; Okwuashi, Onuwa

2018-03-01

The role of global sea surface temperature (SST) anomalies in modulating rainfall in the African region has been widely studied and is now less debated. However, their impacts and links to terrestrial water storage (TWS) in general, have not been studied. This study presents the pioneer results of canonical correlation analysis (CCA) of TWS derived from both global reanalysis data (1980-2015) and GRACE (Gravity Recovery and Climate Experiment) (2002-2014) with SST fields. The main issues discussed include, (i) oceanic hot spots that impact on TWS over tropical West Africa (TWA) based on CCA, (ii) long term changes in model and global reanalysis data (soil moisture, TWS, and groundwater) and the influence of climate variability on these hydrological indicators, and (iii) the hydrological characteristics of the Equatorial region of Africa (i.e., the Congo basin) based on GRACE-derived TWS, river discharge, and precipitation. Results of the CCA diagnostics show that El-Niño Southern Oscillation related equatorial Pacific SST fluctuations is a major index of climate variability identified in the main portion of the CCA procedure that indicates a significant association with long term TWS reanalysis data over TWA (r = 0.50, ρ < 0.05). Based on Mann-Kendall's statistics, the study found fairly large long term declines (ρ < 0.05) in TWS and soil moisture (1982 - 2015), mostly over the Congo basin, which coincided with warming of the land surface and the surrounding oceans. Meanwhile, some parts of the Sahel show significant wetting (rainfall, soil moisture, groundwater, and TWS) trends during the same period (1982-2015) and aligns with the ongoing narratives of rainfall recovery in the region. Results of singular spectral analysis and regression confirm that multi-annual changes in the Congo River discharge explained a considerable proportion of variability in GRACE-hydrological signal over the Congo basin (r = 0.86 and R2 = 0.70, ρ < 0.05). Finally, leading orthogonal modes of MERRA and GRACE-TWS over TWA show significant association with global SST anomalies.

Comparison of the New LEAF Area INDEX (LAI 3G) with the Kazahstan-Wide LEAF Area INDEX DATA SET (GGRS-LAI) over Central ASIA

NASA Astrophysics Data System (ADS)

Kappas, M.; Propastin, P.; Degener, J.; Renchin, T.

2014-12-01

Long-term global data sets of Leaf Area Index (LAI) are important for monitoring global vegetation dynamics. LAI indicating phenological development of vegetation is an important state variable for modeling land surface processes. The comparison of long-term data sets is based on two recently available data sets both derived from AVHRR time series. The LAI 3g data set introduced by Zaichun Zhu et al. (2013) is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality MODIS LAI data. The second long-term data set is based on the 8 km spatial resolution GIMMS-AVHRR data (GGRS-data set by Propastin et al. 2012). The GGRS-LAI product uses a three-dimensional physical radiative transfer model which establishes relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site/region specific parameters, including the vegetation architecture variables such as leaf angle distribution, clumping index, and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. The comparison of both long-term data sets will be used to interpret their quality for scientific research in other disciplines. References:Propastin, P., Kappas, M. (2012). Retrieval of coarse-resolution leaf area index over the Republic of Kazakhstan using NOAA AVHRR satellite data and ground measurements," Remote Sensing, vol. 4, no. 1, pp. 220-246. Zaichun Zhu, Jian Bi, Yaozhong Pan, Sangram Ganguly, Alessandro Anav, Liang Xu, Arindam Samanta, Shilong Piao, Ramakrishna R. Nemani and Ranga B. Myneni (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sens. 2013, 5, 927-948; doi:10.3390/rs5020927

A Framework for Effective Assessment of Model-based Projections of Biodiversity to Inform the Next Generation of Global Conservation Targets

NASA Astrophysics Data System (ADS)

Myers, B.; Beard, T. D.; Weiskopf, S. R.; Jackson, S. T.; Tittensor, D.; Harfoot, M.; Senay, G. B.; Casey, K.; Lenton, T. M.; Leidner, A. K.; Ruane, A. C.; Ferrier, S.; Serbin, S.; Matsuda, H.; Shiklomanov, A. N.; Rosa, I.

2017-12-01

Biodiversity and ecosystems services underpin political targets for the conservation of biodiversity; however, previous incarnations of these biodiversity-related targets have not relied on integrated model based projections of possible outcomes based on climate and land use change. Although a few global biodiversity models are available, most biodiversity models lie along a continuum of geography and components of biodiversity. Model-based projections of the future of global biodiversity are critical to support policymakers in the development of informed global conservation targets, but the scientific community lacks a clear strategy for integrating diverse data streams in developing, and evaluating the performance of, such biodiversity models. Therefore, in this paper, we propose a framework for ongoing testing and refinement of model-based projections of biodiversity trends and change, by linking a broad variety of biodiversity models with data streams generated by advances in remote sensing, coupled with new and emerging in-situ observation technologies to inform development of essential biodiversity variables, future global biodiversity targets, and indicators. Our two main objectives are to (1) develop a framework for model testing and refining projections of a broad range of biodiversity models, focusing on global models, through the integration of diverse data streams and (2) identify the realistic outputs that can be developed and determine coupled approaches using remote sensing and new and emerging in-situ observations (e.g., metagenomics) to better inform the next generation of global biodiversity targets.

Evaluation of variability in high-resolution protein structures by global distance scoring.

PubMed

Anzai, Risa; Asami, Yoshiki; Inoue, Waka; Ueno, Hina; Yamada, Koya; Okada, Tetsuji

2018-01-01

Systematic analysis of the statistical and dynamical properties of proteins is critical to understanding cellular events. Extraction of biologically relevant information from a set of high-resolution structures is important because it can provide mechanistic details behind the functional properties of protein families, enabling rational comparison between families. Most of the current structural comparisons are pairwise-based, which hampers the global analysis of increasing contents in the Protein Data Bank. Additionally, pairing of protein structures introduces uncertainty with respect to reproducibility because it frequently accompanies other settings for superimposition. This study introduces intramolecular distance scoring for the global analysis of proteins, for each of which at least several high-resolution structures are available. As a pilot study, we have tested 300 human proteins and showed that the method is comprehensively used to overview advances in each protein and protein family at the atomic level. This method, together with the interpretation of the model calculations, provide new criteria for understanding specific structural variation in a protein, enabling global comparison of the variability in proteins from different species.

Traditional neuropsychological correlates and reliability of the automated neuropsychological assessment metrics-4 battery for Parkinson's disease.

PubMed

Hawkins, Keith A; Jennings, Danna; Vincent, Andrea S; Gilliland, Kirby; West, Adrienne; Marek, Kenneth

2012-08-01

The automated neuropsychological assessment metrics battery-4 for PD offers the promise of a computerized approach to cognitive assessment. To assess its utility, the ANAM4-PD was administered to 72 PD patients and 24 controls along with a traditional battery. Reliability was assessed by retesting 26 patients. The cognitive efficiency score (CES; a global score) exhibited high reliability (r = 0.86). Constituent variables exhibited lower reliability. The CES correlated strongly with the traditional battery global score, but displayed weaker relationships to UPDRS scores than the traditional score. Multivariate analysis of variance revealed a significant difference between the patient and control groups in ANAM4-PD performance, with three ANAM4-PD tests, math, tower, and pursuit tracking, displaying sizeable differences. In discriminant analyses these variables were as effective as the total ANAM4-PD in classifying cases designated as impaired based on traditional variables. Principal components analyses uncovered fewer factors in the ANAM4-PD relative to the traditional battery. ANAM4-PD variables correlated at higher levels with traditional motor and processing speed variables than with untimed executive, intellectual or memory variables. The ANAM4-PD displays high global reliability, but variable subtest reliability. The battery assesses a narrower range of cognitive functions than traditional tests, and discriminates between patients and controls less effectively. Three ANAM4-PD tests, pursuit tracking, math, and tower performed as well as the total ANAM4-PD in classifying patients as cognitively impaired. These findings could guide the refinement of the ANAM4-PD as an efficient method of screening for mild to moderate cognitive deficits in PD patients. Copyright © 2012 Elsevier Ltd. All rights reserved.

The volume and mean depth of Earth's lakes

NASA Astrophysics Data System (ADS)

Cael, B. B.; Heathcote, A. J.; Seekell, D. A.

2017-01-01

Global lake volume estimates are scarce, highly variable, and poorly documented. We developed a rigorous method for estimating global lake depth and volume based on the Hurst coefficient of Earth's surface, which provides a mechanistic connection between lake area and volume. Volume-area scaling based on the Hurst coefficient is accurate and consistent when applied to lake data sets spanning diverse regions. We applied these relationships to a global lake area census to estimate global lake volume and depth. The volume of Earth's lakes is 199,000 km3 (95% confidence interval 196,000-202,000 km3). This volume is in the range of historical estimates (166,000-280,000 km3), but the overall mean depth of 41.8 m (95% CI 41.2-42.4 m) is significantly lower than previous estimates (62-151 m). These results highlight and constrain the relative scarcity of lake waters in the hydrosphere and have implications for the role of lakes in global biogeochemical cycles.

Genetic and environmental influences on sleep quality in middle-aged men: a twin study.

PubMed

Genderson, Margo R; Rana, Brinda K; Panizzon, Matthew S; Grant, Michael D; Toomey, Rosemary; Jacobson, Kristen C; Xian, Hong; Cronin-Golomb, Alice; Franz, Carol E; Kremen, William S; Lyons, Michael J

2013-10-01

Poor sleep quality is a risk factor for a number of cognitive and physiological age-related disorders. Identifying factors underlying sleep quality are important in understanding the etiology of these age-related health disorders. We investigated the extent to which genes and the environment contribute to subjective sleep quality in middle-aged male twins using the classical twin design. We used the Pittsburgh Sleep Quality Index to measure sleep quality in 1218 middle-aged twin men from the Vietnam Era Twin Study of Aging (mean age = 55.4 years; range 51-60; 339 monozygotic twin pairs, 257 dizygotic twin pairs, 26 unpaired twins). The mean PSQI global score was 5.6 [SD = 3.6; range 0-20]. Based on univariate twin models, 34% of variability in the global PSQI score was due to additive genetic effects (heritability) and 66% was attributed to individual-specific environmental factors. Common environment did not contribute to the variability. Similarly, the heritability of poor sleep-a dichotomous measure based on the cut-off of global PSQI>5-was 31%, with no contribution of the common environment. Heritability of six of the seven PSQI component scores (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, and daytime dysfunction) ranged from 0.15 to 0.31, whereas no genetic influences contributed to the use of sleeping medication. Additive genetic influences contribute to approximately one-third of the variability of global subjective sleep quality. Our results in middle-aged men constitute a first step towards examination of the genetic relationship between sleep and other facets of aging. © 2013 European Sleep Research Society.

Genetic and Environmental Influences on Sleep Quality in Middle-Aged Men: A Twin Study

PubMed Central

Genderson, Margo R.; Rana, Brinda K.; Panizzon, Matthew S.; Grant, Michael D.; Toomey, Rosemary; Jacobson, Kristen C.; Xian, Hong; Cronin-Golomb, Alice; Franz, Carol E.; Kremen, William S.; Lyons, Michael J.

2013-01-01

SUMMARY Poor sleep quality is a risk factor for a number of cognitive and physiological age-related disorders. Identifying factors underlying sleep quality are important in understanding the etiology of these age-related health disorders. We investigated the extent to which genes and the environment contribute to subjective sleep quality in middle-aged male twins using the classical twin design. We used the Pittsburgh Sleep Quality Index (PSQI) to measure sleep quality in 1218 middle-aged twin men from the Vietnam Era Twin Study of Aging (VETSA)(mean age=55.4 years; range 51–60; 339 monozygotic twin pairs, 257 dizygotic twin pairs, 26 unpaired twins). The mean PSQI global score was 5.6 (SD=3.6; range 0–20). Based on univariate twin models, 34% of variability in the global PSQI score was due to additive genetic effects (heritability) and 66% was attributed to individual-specific environmental factors. Common environment did not contribute to the variability. Similarly, the heritability of poor sleep—a dichotomous measure based on the cut-off of global PSQI>5--was 31% with no contribution of the common environment. Heritability of six of the seven PSQI component scores (Subjective Sleep Quality, Sleep Latency, Sleep Duration, Habitual Sleep Efficiency, Sleep Disturbances, and Daytime Dysfunction) ranged from .15 to .31, where as no genetic influences contributed to Use of Sleeping Medication. Additive genetic influences contribute to approximately one-third of the variability of global subjective sleep quality. Our results in middle-aged men constitute a first step toward examination of the genetic relationship between sleep and other facets of aging. PMID:23509903

Regional variability of sea level change using a global ocean model.

NASA Astrophysics Data System (ADS)

Lombard, A.; Garric, G.; Cazenave, A.; Penduff, T.; Molines, J.

2007-12-01

We analyse different runs of a global eddy-permitting (1/4 degree) ocean model driven by atmospheric forcing to evaluate regional variability of sea level change over 1993-2001, 1998-2006 and over the long period 1958-2004. No data assimilation is performed in the model, contrarily to previous similar studies (Carton et al., 2005; Wunsch et al., 2007; Koehl and Stammer, 2007). We compare the model-based regional sea level trend patterns with the one deduced from satellite altimetry data. We examine respective contributions of steric and bottom pressure changes to total regional sea level changes. For the steric component, we analyze separately the contributions of temperature and salinity changes as well as upper and lower ocean contributions.

Exploring the control of land-atmospheric oscillations over terrestrial vegetation productivity

NASA Astrophysics Data System (ADS)

Depoorter, Mathieu; Green, Julia; Gentine, Pierre; Liu, Yi; van Eck, Christel; Regnier, Pierre; Dorigo, Wouter; Verhoest, Niko; Miralles, Diego

2015-04-01

Vegetation dynamics play an important role in the climate system due to their control on the carbon, energy and water cycles. The spatiotemporal variability of vegetation is regulated by internal climate variability as well as natural and anthropogenic forcing mechanisms, including fires, land use, volcano eruptions or greenhouse gas emissions. Ocean-atmospheric oscillations, affect the fluxes of heat and water over continents, leading to anomalies in radiation, precipitation or temperature at widely separated locations (i.e. teleconnections); an effect of ocean-atmospheric oscillations on terrestrial primary productivity can therefore be expected. While different studies have shown the general importance of internal climate variability for global vegetation dynamics, the control by particular teleconnections over the regional growth and decay of vegetation is still poorly understood. At continental to global scales, satellite remote sensing offers a feasible approach to enhance our understanding of the main drivers of vegetation variability. Traditional studies of the multi-decadal variability of global vegetation have been usually based on the normalized difference vegetation index (NDVI) derived from the Advanced Very High Resolution Radiometer (AVHRR), which extends back to the early '80s. There are, however, some limitations to NDVI observations; arguably the most important of these limitations is that from the plant physiology perspective the index does not have a well-defined meaning, appearing poorly correlated to vegetation productivity. On the other hand, recently developed records from other remotely-sensed properties of vegetation, like fluorescence or microwave vegetation optical depth, have proven a significantly better correspondence to above-ground biomass. To enhance our understanding of the controls of ocean-atmosphere oscillations over vegetation, we propose to explore the link between climate oscillation extremes and net primary productivity over the last two decades. The co-variability of a range of climate oscillation indices and newly-derived records of fluorescence and vegetation optical depth is analyzed using a statistical framework based on correlations, bootstrapping and Empirical Orthogonal Functions (EOFs). Results will enable us to characterize regional hotspots where particular climatic oscillations control vegetation productivity, as well as allowing us to underpin the climatic variables behind this control.

Climate Variability and Wildfires: Insights from Global Earth System Models

NASA Astrophysics Data System (ADS)

Ward, D. S.; Shevliakova, E.; Malyshev, S.; Lamarque, J. F.; Wittenberg, A. T.

2016-12-01

Better understanding of the relationship between variability in global climate and emissions from wildfires is needed for predictions of fire activity on interannual to multi-decadal timescales. Here we investigate this relationship using the long, preindustrial control simulations and historical ensembles of two Earth System models; CESM1 and the NOAA/GFDL ESM2Mb. There is smaller interannual variability of global fires in both models than in present day inventories, especially in boreal regions where observed fires vary substantially from year to year. Patterns of fire response to climate oscillation indices, including the El Niño / Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Meridional Oscillation (AMO) are explored with the model results and compared to the response derived from satellite measurements and proxy observations. Increases in fire emissions in southeast Asia and boreal North America are associated with positive ENSO and PDO, while United States fires and Sahel fires decrease for the same climate conditions. Boreal fire emissions decrease in CESM1 for the warm phase of the AMO, while ESM2Mb did not produce a reliable AMO. CESM1 produces a weak negative trend in global fire emissions for the period 1920 to 2005, while ESM2Mb produces a positive trend over the same period. Both trends are statistically significant at a confidence level of 95% or greater given the variability derived from the respective preindustrial controls. In addition to climate variability impacts on fires, we also explore the impacts of fire emissions on climate variability and atmospheric chemistry. We analyze three long, free-evolving ESM2Mb simulations; one without fire emissions, one with constant year-over-year fire emissions based on a present day inventory, and one with interannually varying fire emissions coupled between the terrestrial and atmospheric components of the model, to gain a better understanding of the role of fire emissions in climate over long timescales.

Decoding the spatial signatures of multi-scale climate variability - a climate network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Jajcay, N.; Wiedermann, M.; Ekhtiari, N.; Palus, M.

2017-12-01

During the last years, the application of complex networks as a versatile tool for analyzing complex spatio-temporal data has gained increasing interest. Establishing this approach as a new paradigm in climatology has already provided valuable insights into key spatio-temporal climate variability patterns across scales, including novel perspectives on the dynamics of the El Nino Southern Oscillation or the emergence of extreme precipitation patterns in monsoonal regions. In this work, we report first attempts to employ network analysis for disentangling multi-scale climate variability. Specifically, we introduce the concept of scale-specific climate networks, which comprises a sequence of networks representing the statistical association structure between variations at distinct time scales. For this purpose, we consider global surface air temperature reanalysis data and subject the corresponding time series at each grid point to a complex-valued continuous wavelet transform. From this time-scale decomposition, we obtain three types of signals per grid point and scale - amplitude, phase and reconstructed signal, the statistical similarity of which is then represented by three complex networks associated with each scale. We provide a detailed analysis of the resulting connectivity patterns reflecting the spatial organization of climate variability at each chosen time-scale. Global network characteristics like transitivity or network entropy are shown to provide a new view on the (global average) relevance of different time scales in climate dynamics. Beyond expected trends originating from the increasing smoothness of fluctuations at longer scales, network-based statistics reveal different degrees of fragmentation of spatial co-variability patterns at different scales and zonal shifts among the key players of climate variability from tropically to extra-tropically dominated patterns when moving from inter-annual to decadal scales and beyond. The obtained results demonstrate the potential usefulness of systematically exploiting scale-specific climate networks, whose general patterns are in line with existing climatological knowledge, but provide vast opportunities for further quantifications at local, regional and global scales that are yet to be explored.

Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model.

PubMed

Sakschewski, Boris; von Bloh, Werner; Boit, Alice; Rammig, Anja; Kattge, Jens; Poorter, Lourens; Peñuelas, Josep; Thonicke, Kirsten

2015-01-22

Functional diversity is critical for ecosystem dynamics, stability and productivity. However, dynamic global vegetation models (DGVMs) which are increasingly used to simulate ecosystem functions under global change, condense functional diversity to plant functional types (PFTs) with constant parameters. Here, we develop an individual- and trait-based version of the DGVM LPJmL (Lund-Potsdam-Jena managed Land) called LPJmL- flexible individual traits (LPJmL-FIT) with flexible individual traits) which we apply to generate plant trait maps for the Amazon basin. LPJmL-FIT incorporates empirical ranges of five traits of tropical trees extracted from the TRY global plant trait database, namely specific leaf area (SLA), leaf longevity (LL), leaf nitrogen content (N area ), the maximum carboxylation rate of Rubisco per leaf area (vcmaxarea), and wood density (WD). To scale the individual growth performance of trees, the leaf traits are linked by trade-offs based on the leaf economics spectrum, whereas wood density is linked to tree mortality. No preselection of growth strategies is taking place, because individuals with unique trait combinations are uniformly distributed at tree establishment. We validate the modeled trait distributions by empirical trait data and the modeled biomass by a remote sensing product along a climatic gradient. Including trait variability and trade-offs successfully predicts natural trait distributions and achieves a more realistic representation of functional diversity at the local to regional scale. As sites of high climatic variability, the fringes of the Amazon promote trait divergence and the coexistence of multiple tree growth strategies, while lower plant trait diversity is found in the species-rich center of the region with relatively low climatic variability. LPJmL-FIT enables to test hypotheses on the effects of functional biodiversity on ecosystem functioning and to apply the DGVM to current challenges in ecosystem management from local to global scales, that is, deforestation and climate change effects. © 2015 John Wiley & Sons Ltd.

Estimating moisture transport over oceans using space-based observations

NASA Technical Reports Server (NTRS)

Liu, W. Timothy; Wenqing, Tang

2005-01-01

The moisture transport integrated over the depth of the atmosphere (0) is estimated over oceans using satellite data. The transport is the product of the precipitable water and an equivalent velocity (ue), which, by definition, is the depth-averaged wind velocity weighted by humidity. An artificial neural network is employed to construct a relation between the surface wind velocity measured by the spaceborne scatterometer and coincident ue derived using humidity and wind profiles measured by rawinsondes and produced by reanalysis of operational numerical weather prediction (NWP). On the basis of this relation, 0 fields are produced over global tropical and subtropical oceans (40_N- 40_S) at 0.25_ latitude-longitude and twice daily resolutions from August 1999 to December 2003 using surface wind vector from QuikSCAT and precipitable water from the Tropical Rain Measuring Mission. The derived ue were found to capture the major temporal variability when compared with radiosonde measurements. The average error over global oceans, when compared with NWP data, was comparable with the instrument accuracy specification of space-based scatterometers. The global distribution exhibits the known characteristics of, and reveals more detailed variability than in, previous data.

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

Global Soil Respiration: Interaction with Environmental Variables and Response to Climate Change

NASA Astrophysics Data System (ADS)

Jian, J.; Steele, M.

2016-12-01

Background, methods, objectivesTerrestrial ecosystems take up around 1.7 Pg C per year; however, the role of terrestrial ecosystems as a carbon sink may change to carbon source by 2050, as a result of positive feedback of soil respiration response to global warming. Nevertheless, limited evidence shows that soil carbon is decreasing and the role of terrestrial ecosystems is changing under warming. One possibility is the positive feedback may slow due to the acclimation of soil respiration as a result of decreasing temperature sensitivity (Q10) with warming. To verify and quantify the uncertainty in soil carbon cycling and feedbacks to climate change, we assembled soil respiration observations from 1961 to 2014 from 724 publications into a monthly global soil respiration database (MSRDB), which included 13482 soil respiration measurements together with 38 other ancillary measurements from 538 sites. Using this database we examined macroscale variation in the relationship between soil respiration and air temperature, precipitation, leaf area index and soil properties. We also quantified global soil respiration, the sources of uncertainty, and its feedback to warming based on climate region-oriented models with variant Q10function. Results and ConclusionsOur results showed substantial heterogeneity in the relationship between soil respiration and environmental factors across different climate regions. For example, soil respiration was strongly related to vegetation (via leaf area index) in colder regions, but not in tropical region. Only in tropical and arid regions did soil properties explain any variation in soil respiration. Global annual mean soil respiration from 1961 to 2014 was estimated to be 72.41 Pg C yr-1 based on monthly global soil respiration database, 25 Pg lower than estimated based on yearly soil respiration database. By using the variable Q10 models, we estimated that global soil respiration increased at a rate of 0.03 Pg C yr-1 from 1961 to 2014, smaller than previous studies ( 0.1 Pg C yr-1). The substantial variations in these relationships suggest that regional scales is important for understanding and prediction of global carbon cycling and how it response to climate change.

Tolerance adaptation and precipitation changes complicate latitudinal patterns of climate change impacts.

PubMed

Bonebrake, Timothy C; Mastrandrea, Michael D

2010-07-13

Global patterns of biodiversity and comparisons between tropical and temperate ecosystems have pervaded ecology from its inception. However, the urgency in understanding these global patterns has been accentuated by the threat of rapid climate change. We apply an adaptive model of environmental tolerance evolution to global climate data and climate change model projections to examine the relative impacts of climate change on different regions of the globe. Our results project more adverse impacts of warming on tropical populations due to environmental tolerance adaptation to conditions of low interannual variability in temperature. When applied to present variability and future forecasts of precipitation data, the tolerance adaptation model found large reductions in fitness predicted for populations in high-latitude northern hemisphere regions, although some tropical regions had comparable reductions in fitness. We formulated an evolutionary regional climate change index (ERCCI) to additionally incorporate the predicted changes in the interannual variability of temperature and precipitation. Based on this index, we suggest that the magnitude of climate change impacts could be much more heterogeneous across latitude than previously thought. Specifically, tropical regions are likely to be just as affected as temperate regions and, in some regions under some circumstances, possibly more so.

A First Approach to Global Runoff Simulation using Satellite Rainfall Estimation

NASA Technical Reports Server (NTRS)

Hong, Yang; Adler, Robert F.; Hossain, Faisal; Curtis, Scott; Huffman, George J.

2007-01-01

Many hydrological models have been introduced in the hydrological literature to predict runoff but few of these have become common planning or decision-making tools, either because the data requirements are substantial or because the modeling processes are too complicated for operational application. On the other hand, progress in regional or global rainfall-runoff simulation has been constrained by the difficulty of measuring spatiotemporal variability of the primary causative factor, i.e. rainfall fluxes, continuously over space and time. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and space-borne radar sensors. Motivated by the recent increasing availability of global remote sensing data for estimating precipitation and describing land surface characteristics, this note reports a ballpark assessment of quasi-global runoff computed by incorporating satellite rainfall data and other remote sensing products in a relatively simple rainfall-runoff simulation approach: the Natural Resources Conservation Service (NRCS) runoff Curve Number (CN) method. Using an Antecedent Precipitation Index (API) as a proxy of antecedent moisture conditions, this note estimates time-varying NRCS-CN values determined by the 5-day normalized API. Driven by multi-year (1998-2006) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis, quasi-global runoff was retrospectively simulated with the NRCS-CN method and compared to Global Runoff Data Centre data at global and catchment scales. Results demonstrated the potential for using this simple method when diagnosing runoff values from satellite rainfall for the globe and for medium to large river basins. This work was done with the simple NRCS-CN method as a first-cut approach to understanding the challenges that lie ahead in advancing the satellite-based inference of global runoff. We expect that the successes and limitations revealed in this study will lay the basis for applying more advanced methods to capture the dynamic variability of the global hydrologic process for global runoff monltongin real time. The essential ingredient in this work is the use of global satellite-based rainfall estimation.

Analysis of meteorological variables in the Australasian region using ground- and space-based GPS techniques

NASA Astrophysics Data System (ADS)

Kuleshov, Yuriy; Choy, Suelynn; Fu, Erjiang Frank; Chane-Ming, Fabrice; Liou, Yuei-An; Pavelyev, Alexander G.

2016-07-01

Results of analysis of meteorological variables (temperature and moisture) in the Australasian region using the global positioning system (GPS) radio occultation (RO) and GPS ground-based observations verified with in situ radiosonde (RS) data are presented. The potential of using ground-based GPS observations for retrieving column integrated precipitable water vapour (PWV) over the Australian continent has been demonstrated using the Australian ground-based GPS reference stations network. Using data from the 15 ground-based GPS stations, the state of the atmosphere over Victoria during a significant weather event, the March 2010 Melbourne storm, has been investigated, and it has been shown that the GPS observations has potential for monitoring the movement of a weather front that has sharp moisture contrast. Temperature and moisture variability in the atmosphere over various climatic regions (the Indian and the Pacific Oceans, the Antarctic and Australia) has been examined using satellite-based GPS RO and in situ RS observations. Investigating recent atmospheric temperature trends over Antarctica, the time series of the collocated GPS RO and RS data were examined, and strong cooling in the lower stratosphere and warming through the troposphere over Antarctica has been identified, in agreement with outputs of climate models. With further expansion of the Global Navigation Satellite Systems (GNSS) system, it is expected that GNSS satellite- and ground-based measurements would be able to provide an order of magnitude larger amount of data which in turn could significantly advance weather forecasting services, climate monitoring and analysis in the Australasian region.

The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times

PubMed Central

Bloom, A. Anthony; Exbrayat, Jean-François; van der Velde, Ivar R.; Feng, Liang; Williams, Mathew

2016-01-01

The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85–88%) in contrast to higher latitudes (73–82%), where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42–0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64–0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types. PMID:26787856

Temporal variability of green and blue water footprint worldwide

NASA Astrophysics Data System (ADS)

Tamea, Stefania; Lomurno, Marianna; Tuninetti, Marta; Laio, Francesco; Ridolfi, Luca

2016-04-01

Water footprint assessment is becoming widely used in the scientific literature and it is proving useful in a number of multidisciplinary contexts. Given this increasing popularity, measures of green and blue water footprint (or virtual water content, VWC) require evaluations of uncertainty and variability to quantify the reliability of proposed analyses. As of today, no studies are known to assess the temporal variability of crop VWC at the global scale; the present contribution aims at filling this gap. We use a global high-resolution distributed model to compute the VWC of staple crops (wheat and maize), basing on the soil water balance, forced by hydroclimatic imputs, and on the total crop evapotranspiration in multiple growing seasons. Crop actual yield is estimated using country-based yield data, adjusted to account for spatial variability, allowing for the analysis of the different role played by climatic and management factors in the definition of crop yield. The model is then run using hydroclimatic data, i.e., precipitation and potential evapotranspiration, for the period 1961-2013 as taken from the CRU database (CRU TS v. 3.23) and using the corresponding country-based yield data from FAOSTAT. Results provide the time series of total evapotranspiration, actual yield and VWC, with separation between green and blue VWC, and the overall volume of water used for crop production, both at the cell scale (5x5 arc-min) and aggregated at the country scale. Preliminary results indicate that total (green+blue) VWC is, in general, weekly dependent on hydroclimatic forcings if water for irrigation is unlimited, because irrigated agriculture allows to compensate temporary water shortage. Conversely, most part of the VWC variability is found to be determined by the temporal evolution of crop yield. At the country scale, the total water used by countries for agricultural production has seen a limited change in time, but the marked increase in the water-use efficiency expressed by VWC has determined an increase of production. Such increase has helped to meet the increasing global food demand in the past 50 years.

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.

ENSO elicits opposing responses of semi-arid vegetation between Hemispheres

NASA Astrophysics Data System (ADS)

Zhang, Anzhi; Jia, Gensuo; Epstein, Howard E.; Xia, Jiangjiang

2017-02-01

Semi-arid ecosystems are key contributors to the global carbon cycle and may even dominate the inter-annual variability (IAV) and trends of the land carbon sink, driven largely by the El Niño-Southern Oscillation (ENSO). The linkages between dynamics of semi-arid ecosystems and climate at the hemispheric scale however are not well known. Here, we use satellite data and climate observations from 2000 to 2014 to explore the impacts of ENSO on variability of semi-arid ecosystems, using the Ensemble Empirical Mode Decomposition method. We show that the responses of semi-arid vegetation to ENSO occur in opposite directions, resulting from opposing controls of ENSO on precipitation between the Northern Hemisphere (positively correlated to ENSO) and the Southern Hemisphere (negatively correlated to ENSO). Also, the Southern Hemisphere, with a robust negative coupling of temperature and precipitation anomalies, exhibits stronger and faster responses of semi-arid ecosystems to ENSO than the Northern Hemisphere. Our findings suggest that natural coherent variability in semi-arid ecosystem productivity responded to ENSO in opposite ways between two hemispheres, which may imply potential prediction of global semi-arid ecosystem variability, particularly based on variability in tropical Pacific Sea Surface Temperatures.

Change in the magnitude and mechanisms of global temperature variability with warming.

PubMed

Brown, Patrick T; Ming, Yi; Li, Wenhong; Hill, Spencer A

2017-01-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

Change in the Magnitude and Mechanisms of Global Temperature Variability with Warming

NASA Astrophysics Data System (ADS)

Brown, P. T.; Ming, Y.; Li, W.; Hill, S. A.

2017-12-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

A strategic outlook for coordination of ground-based measurement networks of atmospheric state variables and atmospheric composition

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Thorne, P.; Braathen, G.; De Maziere, M.; Thompson, A. M.; Kurylo, M. J., III

2016-12-01

There are a number of ground-based global observing networks that collectively aim to make key measurements of atmospheric state variables and atmospheric chemical composition. These networks include, but are not limited to:NDACC: Network for the Detection of Atmospheric Composition Change GUAN: GCOS Upper Air Network GRUAN: GCOS Reference Upper Air Network EARLINET: the European Aerosol Research Lidar Network GAW: Global Atmosphere Watch SHADOZ: Southern Hemisphere ADditional OZonesondes TCCON: Total Carbon Column Observing Network BSRN: Baseline Surface Radiation Network While each network brings unique capabilities to the global observing system, there are many instances where the activities and capabilities of the networks overlap. These commonalities across multiple networks can confound funding agencies when allocating scarce financial resources. Overlaps between networks may also result in some duplication of effort and a resultant sub-optimal use of funding resource for the global observing system. While some degree of overlap is useful for quality assurance, it is essential to identify the degree to which one network can take on a specific responsibility on behalf of all other networks to avoid unnecessary duplication, to identify where expertise in any one network may serve other networks, and to develop a long-term strategy for the evolution of these networks that clarifies to funding agencies where new investment is required. This presentation will briefly summarise the key characteristics of each network listed above, adopt a matrix approach to identify commonalities and, in particular, where there may be a danger of duplication of effort, and where gaps between the networks may be compromising the services that these networks are expected to collectively deliver to the global atmospheric and climate science research communities. The presentation will also examine where sharing of data and tools between networks may result in a more efficient delivery of records of essential climate variables to the global research community. There are aspects of underpinning research that are needed across all of these networks, such as laboratory spectroscopy, that often do not receive the attention they deserve. The presentation will also seek to identify where that underpinning research is lacking.

Patterns of Spatial Variation of Assemblages Associated with Intertidal Rocky Shores: A Global Perspective

PubMed Central

Cruz-Motta, Juan José; Miloslavich, Patricia; Palomo, Gabriela; Iken, Katrin; Konar, Brenda; Pohle, Gerhard; Trott, Tom; Benedetti-Cecchi, Lisandro; Herrera, César; Hernández, Alejandra; Sardi, Adriana; Bueno, Andrea; Castillo, Julio; Klein, Eduardo; Guerra-Castro, Edlin; Gobin, Judith; Gómez, Diana Isabel; Riosmena-Rodríguez, Rafael; Mead, Angela; Bigatti, Gregorio; Knowlton, Ann; Shirayama, Yoshihisa

2010-01-01

Assemblages associated with intertidal rocky shores were examined for large scale distribution patterns with specific emphasis on identifying latitudinal trends of species richness and taxonomic distinctiveness. Seventy-two sites distributed around the globe were evaluated following the standardized sampling protocol of the Census of Marine Life NaGISA project (www.nagisa.coml.org). There were no clear patterns of standardized estimators of species richness along latitudinal gradients or among Large Marine Ecosystems (LMEs); however, a strong latitudinal gradient in taxonomic composition (i.e., proportion of different taxonomic groups in a given sample) was observed. Environmental variables related to natural influences were strongly related to the distribution patterns of the assemblages on the LME scale, particularly photoperiod, sea surface temperature (SST) and rainfall. In contrast, no environmental variables directly associated with human influences (with the exception of the inorganic pollution index) were related to assemblage patterns among LMEs. Correlations of the natural assemblages with either latitudinal gradients or environmental variables were equally strong suggesting that neither neutral models nor models based solely on environmental variables sufficiently explain spatial variation of these assemblages at a global scale. Despite the data shortcomings in this study (e.g., unbalanced sample distribution), we show the importance of generating biological global databases for the use in large-scale diversity comparisons of rocky intertidal assemblages to stimulate continued sampling and analyses. PMID:21179546

A QC approach to the determination of day-to-day reproducibility and robustness of LC-MS methods for global metabolite profiling in metabonomics/metabolomics.

PubMed

Gika, Helen G; Theodoridis, Georgios A; Earll, Mark; Wilson, Ian D

2012-09-01

An approach to the determination of day-to-day analytical robustness of LC-MS-based methods for global metabolic profiling using a pooled QC sample is presented for the evaluation of metabonomic/metabolomic data. A set of 60 urine samples were repeatedly analyzed on five different days and the day-to-day reproducibility of the data obtained was determined. Multivariate statistical analysis was performed with the aim of evaluating variability and selected peaks were assessed and validated in terms of retention time stability, mass accuracy and intensity. The methodology enables the repeatability/reproducibility of extended analytical runs in large-scale studies to be determined, allowing the elimination of analytical (as opposed to biological) variability, in order to discover true patterns and correlations within the data. The day-to-day variability of the data revealed by this process suggested that, for this particular system, 3 days continuous operation was possible without the need for maintenance and cleaning. Variation was generally based on signal intensity changes over the 7-day period of the study, and was mainly a result of source contamination.

Modelling spatial and temporal vegetation variability with the Climate Constrained Vegetation Index: evidence of CO2 fertilisation and of water stress in continental interiors

NASA Astrophysics Data System (ADS)

Los, S. O.

2015-06-01

A model was developed to simulate spatial, seasonal and interannual variations in vegetation in response to temperature, precipitation and atmospheric CO2 concentrations; the model addresses shortcomings in current implementations. The model uses the minimum of 12 temperature and precipitation constraint functions to simulate NDVI. Functions vary based on the Köppen-Trewartha climate classification to take adaptations of vegetation to climate into account. The simulated NDVI, referred to as the climate constrained vegetation index (CCVI), captured the spatial variability (0.82 < r <0.87), seasonal variability (median r = 0.83) and interannual variability (median global r = 0.24) in NDVI. The CCVI simulated the effects of adverse climate on vegetation during the 1984 drought in the Sahel and during dust bowls of the 1930s and 1950s in the Great Plains in North America. A global CO2 fertilisation effect was found in NDVI data, similar in magnitude to that of earlier estimates (8 % for the 20th century). This effect increased linearly with simple ratio, a transformation of the NDVI. Three CCVI scenarios, based on climate simulations using the representative concentration pathway RCP4.5, showed a greater sensitivity of vegetation towards precipitation in Northern Hemisphere mid latitudes than is currently implemented in climate models. This higher sensitivity is of importance to assess the impact of climate variability on vegetation, in particular on agricultural productivity.

Recent changes in stratospheric aerosol budget from ground-based and satellite observations

NASA Astrophysics Data System (ADS)

Khaykin, Sergey; Godin-Beekmann, Sophie; Keckhut, Philippe; Hauchecorne, Alain; Portafaix, Thierry; Begue, Nelson; Vernier, Jean-Paul; DeLand, Matthew; Bhartia, Pawan K.; Leblanc, Thierry

2017-04-01

Stratospheric aerosol budget plays an important role in climate variability and ozone chemistry. Observations of stratospheric aerosol by ground-based lidars represent a particular value as they ensure the continuity and coherence of stratospheric aerosol record. Ground-based lidars remain indispensable for complementing and validating satellite instruments and for filling gaps between satellite missions. On the other hand, geophysical interpretation of local observations is complicated without the knowledge of global distribution of stratospheric aerosol, which calls for a combined analysis of ground-based and space-borne observations. The present study aims at characterizing global and regional variability of stratospheric aerosol over the last 5 years using various sets of observations. We use the data provided by three lidars operated within NDACC (Network for Detection of Atmospheric Composition Change) at Haute-Provence, (44° N), Mauna Loa (21° N) and Maido (21° S) sites together with quasi-global-coverage aerosol measurements by CALIOP and OMPS satellite instruments. The local and space-borne measurements are shown to be in good agreement allowing for their synergetic use. Since the late 2012 stratospheric aerosol remained at background levels throughout the globe. Eruptions of Kelud volcano at 4° S in February 2014 and Calbuco volcano at 41° S in April 2015 resulted in a remarkable enhancement of stratospheric AOD at a wide latitude range. We explore meridional dispersion and lifetime of volcanic plumes in consideration of global atmospheric circulation. A focus is made on the poleward transport of volcanic aerosol and its detection at the mid-latitude Haute-Provence observatory. We show that the moderate eruptions in the Southern hemisphere leave a measurable imprint on the Northern mid-latitude aerosol loading. Having identified the volcanically-perturbed periods from local and global observations we examine the evolution of non-volcanic (background) aerosol by comparing the recent observations with historical data available from 23-yr observations at Haute-Provence and Mauna-Loa.

A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability

NASA Astrophysics Data System (ADS)

Wang, Kaicun; Dickinson, Robert E.

2012-06-01

This review surveys the basic theories, observational methods, satellite algorithms, and land surface models for terrestrial evapotranspiration, E (or λE, i.e., latent heat flux), including a long-term variability and trends perspective. The basic theories used to estimate E are the Monin-Obukhov similarity theory (MOST), the Bowen ratio method, and the Penman-Monteith equation. The latter two theoretical expressions combine MOST with surface energy balance. Estimates of E can differ substantially between these three approaches because of their use of different input data. Surface and satellite-based measurement systems can provide accurate estimates of diurnal, daily, and annual variability of E. But their estimation of longer time variability is largely not established. A reasonable estimate of E as a global mean can be obtained from a surface water budget method, but its regional distribution is still rather uncertain. Current land surface models provide widely different ratios of the transpiration by vegetation to total E. This source of uncertainty therefore limits the capability of models to provide the sensitivities of E to precipitation deficits and land cover change.

Comparison of High-Frequency Solar Irradiance: Ground Measured vs. Satellite-Derived

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

Lave, Matthew; Weekley, Andrew

2016-11-21

High-frequency solar variability is an important to grid integration studies, but ground measurements are scarce. The high resolution irradiance algorithm (HRIA) has the ability to produce 4-sceond resolution global horizontal irradiance (GHI) samples, at locations across North America. However, the HRIA has not been extensively validated. In this work, we evaluate the HRIA against a database of 10 high-frequency ground-based measurements of irradiance. The evaluation focuses on variability-based metrics. This results in a greater understanding of the errors in the HRIA as well as suggestions for improvement to the HRIA.

Assessing the Effects of Climate on Global Fluvial Discharge Variability

NASA Astrophysics Data System (ADS)

Hansford, M. R.; Plink-Bjorklund, P.

2017-12-01

Plink-Bjorklund (2015) established the link between precipitation seasonality and river discharge variability in the monsoon domain and subtropical rivers (see also Leier et al, 2005; Fielding et al., 2009), resulting in distinct morphodynamic processes and a sedimentary record distinct from perennial precipitation zone in tropical rainforest zone and mid latitudes. This study further develops our understanding of discharge variability using a modern global river database created with data from the Global Runoff Data Centre (GRDC). The database consists of daily discharge for 595 river stations and examines them using a series of discharge variability indexes (DVI) on different temporal scales to examine how discharge variability occurs in river systems around the globe. These indexes examine discharge of individual days and monthly averages that allows for comparison of river systems against each other, regardless of size of the river. Comparing river discharge patterns in seven climate zones (arid, cold, humid subtropics, monsoonal, polar, rainforest, and temperate) based off the Koppen-Geiger climate classifications reveals a first order climatic control on discharge patterns and correspondingly sediment transport. Four groupings of discharge patterns emerge when coming climate zones and DVI: persistent, moderate, seasonal, and erratic. This dataset has incredible predictive power about the nature of discharge in fluvial systems around the world. These seasonal effects on surface water supply affects river morphodynamics and sedimentation on a wide timeframe, ranging from large single events to an inter-annual or even decadal timeframe. The resulting sedimentary deposits lead to differences in fluvial architecture on a range of depositional scales from sedimentary structures and bedforms to channel complex systems. These differences are important to accurately model for several reasons, ranging from stratigraphic and paleoenviromental reconstructions to more economic reasons, such as predicting reservoir presence, distribution, and connectivity in continental basins. The ultimate objective of this research is to develop differentiated fluvial facies and architecture based on the observed discharge patterns in the different climate zones.

The sensitivity of the atmospheric branch of the global water cycle to temperature fluctuations at synoptic to decadal time-scales in different satellite- and model-based products

NASA Astrophysics Data System (ADS)

Nogueira, Miguel

2018-02-01

Spectral analysis of global-mean precipitation, P, evaporation, E, precipitable water, W, and surface temperature, Ts, revealed significant variability from sub-daily to multi-decadal time-scales, superposed on high-amplitude diurnal and yearly peaks. Two distinct regimes emerged from a transition in the spectral exponents, β. The weather regime covering time-scales < 10 days with β ≥ 1; and the macroweather regime extending from a few months to a few decades with 0 <β <1. Additionally, the spectra showed a generally good statistical agreement amongst several different model- and satellite-based datasets. Detrended cross-correlation analysis (DCCA) revealed three important results which are robust across all datasets: (1) Clausius-Clapeyron (C-C) relationship is the dominant mechanism of W non-periodic variability at multi-year time-scales; (2) C-C is not the dominant control of W, P or E non-periodic variability at time-scales below about 6 months, where the weather regime is approached and other mechanisms become important; (3) C-C is not a dominant control for P or E over land throughout the entire time-scale range considered. Furthermore, it is suggested that the atmosphere and oceans start to act as a single coupled system at time-scales > 1-2 years, while at time-scales < 6 months they are not the dominant drivers of each other. For global-ocean and full-globe averages, ρDCCA showed large spread of the C-C importance for P and E variability amongst different datasets at multi-year time-scales, ranging from negligible (< 0.3) to high ( 0.6-0.8) values. Hence, state-of-the-art climate datasets have significant uncertainties in the representation of macroweather precipitation and evaporation variability and its governing mechanisms.

Climate and soil attributes determine plant species turnover in global drylands

PubMed Central

Maestre, Fernando T.; Gotelli, Nicholas J.; Quero, José L.; Delgado-Baquerizo, Manuel; Bowker, Matthew A.; Eldridge, David J.; Ochoa, Victoria; Gozalo, Beatriz; Valencia, Enrique; Berdugo, Miguel; Escolar, Cristina; García-Gómez, Miguel; Escudero, Adrián; Prina, Aníbal; Alfonso, Graciela; Arredondo, Tulio; Bran, Donaldo; Cabrera, Omar; Cea, Alex; Chaieb, Mohamed; Contreras, Jorge; Derak, Mchich; Espinosa, Carlos I.; Florentino, Adriana; Gaitán, Juan; Muro, Victoria García; Ghiloufi, Wahida; Gómez-González, Susana; Gutiérrez, Julio R.; Hernández, Rosa M.; Huber-Sannwald, Elisabeth; Jankju, Mohammad; Mau, Rebecca L.; Hughes, Frederic Mendes; Miriti, Maria; Monerris, Jorge; Muchane, Muchai; Naseri, Kamal; Pucheta, Eduardo; Ramírez-Collantes, David A.; Raveh, Eran; Romão, Roberto L.; Torres-Díaz, Cristian; Val, James; Veiga, José Pablo; Wang, Deli; Yuan, Xia; Zaady, Eli

2015-01-01

Aim Geographic, climatic, and soil factors are major drivers of plant beta diversity, but their importance for dryland plant communities is poorly known. This study aims to: i) characterize patterns of beta diversity in global drylands, ii) detect common environmental drivers of beta diversity, and iii) test for thresholds in environmental conditions driving potential shifts in plant species composition. Location 224 sites in diverse dryland plant communities from 22 geographical regions in six continents. Methods Beta diversity was quantified with four complementary measures: the percentage of singletons (species occurring at only one site), Whittake’s beta diversity (β(W)), a directional beta diversity metric based on the correlation in species occurrences among spatially contiguous sites (β(R2)), and a multivariate abundance-based metric (β(MV)). We used linear modelling to quantify the relationships between these metrics of beta diversity and geographic, climatic, and soil variables. Results Soil fertility and variability in temperature and rainfall, and to a lesser extent latitude, were the most important environmental predictors of beta diversity. Metrics related to species identity (percentage of singletons and β(W)) were most sensitive to soil fertility, whereas those metrics related to environmental gradients and abundance ((β(R2)) and β(MV)) were more associated with climate variability. Interactions among soil variables, climatic factors, and plant cover were not important determinants of beta diversity. Sites receiving less than 178 mm of annual rainfall differed sharply in species composition from more mesic sites (> 200 mm). Main conclusions Soil fertility and variability in temperature and rainfall are the most important environmental predictors of variation in plant beta diversity in global drylands. Our results suggest that those sites annually receiving ~ 178 mm of rainfall will be especially sensitive to future climate changes. These findings may help to define appropriate conservation strategies for mitigating effects of climate change on dryland vegetation. PMID:25914437

Terrestrial essential climate variables (ECVs) at a glance

USGS Publications Warehouse

Stitt, Susan; Dwyer, John; Dye, Dennis; Josberger, Edward

2011-01-01

The Global Terrestrial Observing System, Global Climate Observing System, World Meteorological Organization, and Committee on Earth Observation Satellites all support consistent global land observations and measurements. To accomplish this goal, the Global Terrestrial Observing System defined 'essential climate variables' as measurements of atmosphere, oceans, and land that are technically and economically feasible for systematic observation and that are needed to meet the United Nations Framework Convention on Climate Change and requirements of the Intergovernmental Panel on Climate Change. The following are the climate variables defined by the Global Terrestrial Observing System that relate to terrestrial measurements. Several of them are currently measured most appropriately by in-place observations, whereas others are suitable for measurement by remote sensing technologies. The U.S. Geological Survey is the steward of the Landsat archive, satellite imagery collected from 1972 to the present, that provides a potential basis for deriving long-term, global-scale, accurate, timely and consistent measurements of many of these essential climate variables.

Climate variation explains a third of global crop yield variability

PubMed Central

Ray, Deepak K.; Gerber, James S.; MacDonald, Graham K.; West, Paul C.

2015-01-01

Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (~32–39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability. PMID:25609225

Interannual variability of global dust storms on Mars.

PubMed

Haberle, R M

1986-10-24

Global dust storms on Mars occur in some years but not in others. If the four Mars years of Viking data are representative, some distinguishing characteristics can be inferred. In years with global dust storms, dust is raised in the southern hemisphere and spread over much of the planet by an intensified Hadley circulation. In years without global dust storms, dust is raised in the northern hemisphere by relatively active mid-latitude storm systems but does not spread globally. In both cases the dusty season is winter in the north. Assuming that the cross-equatorial Hadley circulation plays a key role in the onset of global dust storms, it is shown from numerical simulations that a northen hemisphere dust haze weakens its intensity and, hence, its contribution to the surface stress in the southern hemisphere. This, in turn, reduces the possibility of global dust storm development. The interannual variability is therefore the result either of a competition between circulations in opposite hemispheres, in which case the variability has a random component, or it is the result of the cycling of dust between hemispheres, in which case the variability is related to the characteristics of global dust storms themselves.

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Global volcanic aerosol properties derived from emissions, 1990-2014, using CESM1(WACCM): VOLCANIC AEROSOLS DERIVED FROM EMISSIONS

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

Mills, Michael J.; Schmidt, Anja; Easter, Richard

Accurate representation of global stratospheric aerosol properties from volcanic and non-volcanic sulfur emissions is key to understanding the cooling effects and ozone-loss enhancements of recent volcanic activity. Attribution of climate and ozone variability to volcanic activity is of particular interest in relation to the post-2000 slowing in the apparent rate of global average temperature increases, and variable recovery of the Antarctic ozone hole. We have developed a climatology of global aerosol properties from 1990 to 2014 calculated based on volcanic and non-volcanic emissions of sulfur sources. We have complied a database of volcanic SO2 emissions and plume altitudes for eruptionsmore » between 1990 and 2014, and a new prognostic capability for simulating stratospheric sulfate aerosols in version 5 of the Whole Atmosphere Community Climate Model, a component of the Community Earth System Model. Our climatology shows remarkable agreement with ground-based lidar observations of stratospheric aerosol optical depth (SAOD), and with in situ measurements of aerosol surface area density (SAD). These properties are key parameters in calculating the radiative and chemical effects of stratospheric aerosols. Our SAOD climatology represents a significant improvement over satellite-based analyses, which ignore aerosol extinction below 15 km, a region that can contain the vast majority of stratospheric aerosol extinction at mid- and high-latitudes. Our SAD climatology significantly improves on that provided for the Chemistry-Climate Model Initiative, which misses 60% of the SAD measured in situ. Our climatology of aerosol properties is publicly available on the Earth System Grid.« less

The rationale/design of the Guimarães/Vizela study: a multimodal population-based cohort study to determine global cardiovascular risk and disease.

PubMed

Cunha, Pedro Guimarães; Cotter, Jorge; Oliveira, Pedro; Vila, Isabel; Sousa, Nuno

2014-06-01

Cardiovascular disease and dementia are growing medical and social problems in aging societies. Appropriate knowledge of cardiovascular disease and cognitive decline risk factors (RFs) are critical for global CVR health preventive intervention. Many epidemiological studies use case definition based on data collected/measured in a single visit, a fact that can overestimate prevalence rates and distant from clinical practice demanding criteria. Portugal displays an elevated stroke mortality rate. However, population's global CV risk characterization is limited, namely, considering traditional/nontraditional RF and new intermediate phenotypes of CV and renal disease. Association of hemodynamic variables (pulse wave velocity and central blood pressure) with global CVR stratification, cognitive performance, and kidney disease are practically inexistent at a dwelling population level. After reviewing published data, we designed a population-based cohort study to analyze the prevalence of these cardiovascular RFs and intermediate phenotypes, using random sampling of adult dwellers living in 2 adjacent cities. Strict definition of phenotypes was planned: subjects were observed twice, and several hemodynamic and other biological variables measured at least 3 months apart. Three thousand thirty-eight subjects were enrolled, and extensive data collection (including central and peripheral blood pressure, pulse wave velocity), sample processing, and biobank edification were carried out. One thousand forty-seven cognitive evaluations were performed. Seeking for CV risk reclassification, early identification of subjects at risk, and evidence of early vascular aging and cognitive and renal function decline, using the strict daily clinical practice criteria, will lead to better resource allocation in preventive measures at a population level.

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.

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

NASA Astrophysics Data System (ADS)

Raleigh, M. S.; Lundquist, J. D.; Clark, M. P.

2015-07-01

Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snow-affected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

Ocean carbon and heat variability in an Earth System Model

NASA Astrophysics Data System (ADS)

Thomas, J. L.; Waugh, D.; Gnanadesikan, A.

2016-12-01

Ocean carbon and heat content are very important for regulating global climate. Furthermore, due to lack of observations and dependence on parameterizations, there has been little consensus in the modeling community on the magnitude of realistic ocean carbon and heat content variability, particularly in the Southern Ocean. We assess the differences between global oceanic heat and carbon content variability in GFDL ESM2Mc using a 500-year, pre-industrial control simulation. The global carbon and heat content are directly out of phase with each other; however, in the Southern Ocean the heat and carbon content are in phase. The global heat mutli-decadal variability is primarily explained by variability in the tropics and mid-latitudes, while the variability in global carbon content is primarily explained by Southern Ocean variability. In order to test the robustness of this relationship, we use three additional pre-industrial control simulations using different mesoscale mixing parameterizations. Three pre-industrial control simulations are conducted with the along-isopycnal diffusion coefficient (Aredi) set to constant values of 400, 800 (control) and 2400 m2 s-1. These values for Aredi are within the range of parameter settings commonly used in modeling groups. Finally, one pre-industrial control simulation is conducted where the minimum in the Gent-McWilliams parameterization closure scheme (AGM) increased to 600 m2 s-1. We find that the different simulations have very different multi-decadal variability, especially in the Weddell Sea where the characteristics of deep convection are drastically changed. While the temporal frequency and amplitude global heat and carbon content changes significantly, the overall spatial pattern of variability remains unchanged between the simulations.

Application of solar max ACRIM data to analyze solar-driven climatic variability on Earth

NASA Technical Reports Server (NTRS)

Hoffert, M. I.

1986-01-01

Terrestrial climatic effects associated with solar variability have been proposed for at least a century, but could not be assessed quantitatively owing to observational uncertainities in solar flux variations. Measurements from 1980 to 1984 by the Active Cavity Radiometer Irradiance Monitor (ACRIM), capable of resolving fluctuations above the sensible atmosphere less than 0.1% of the solar constant, permit direct albeit preliminary assessments of solar forcing effects on global temperatures during this period. The global temperature response to ACRIM-measured fluctuations was computed from 1980 to 1985 using the NYU transient climate model including thermal inertia effects of the world ocean; and compared the results with observations of recent temperature trends. Monthly mean ACRIM-driven global surface temperature fluctuations computed with the climate model are an order of magnitude smaller, of order 0.01 C. In constrast, global mean surface temperature observations indicate an approx. 0.1 C increase during this period. Solar variability is therefore likely to have been a minor factor in global climate change during this period compared with variations in atmospheric albedo, greenhouse gases and internal self-inducedoscillations. It was not possible to extend the applicability of the measured flux variations to longer periods since a possible correlation of luminosity with solar annual activity is not supported by statistical analysis. The continuous monitoring of solar flux by satellite-based instruments over timescales of 20 years or more comparable to timescales for thermal relaxation of the oceans and of the solar cycle itself is needed to resolve the question of long-term solar variation effects on climate.

Biodiversity Mapping via Natura 2000 Conservation Status and Ebv Assessment Using Airborne Laser Scanning in Alkali Grasslands

NASA Astrophysics Data System (ADS)

Zlinszky, A.; Deák, B.; Kania, A.; Schroiff, A.; Pfeifer, N.

2016-06-01

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.

A global reconstruction of climate-driven subdecadal water storage variability

NASA Astrophysics Data System (ADS)

Humphrey, V.; Gudmundsson, L.; Seneviratne, S. I.

2017-03-01

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of global mass redistribution caused by hydrological processes. However, there are still few sources on pre-2002 global terrestrial water storage (TWS). Classical approaches to retrieve past TWS rely on either land surface models (LSMs) or basin-scale water balance calculations. Here we propose a new approach which statistically relates anomalies in atmospheric drivers to monthly GRACE anomalies. Gridded subdecadal TWS changes and time-dependent uncertainty intervals are reconstructed for the period 1985-2015. Comparisons with model results demonstrate the performance and robustness of the derived data set, which represents a new and valuable source for studying subdecadal TWS variability, closing the ocean/land water budgets and assessing GRACE uncertainties. At midpoint between GRACE observations and LSM simulations, the statistical approach provides TWS estimates (doi:10.5905/ethz-1007-85) that are essentially derived from observations and are based on a limited number of transparent model assumptions.

Paleodust variability since the Last Glacial Maximum and implications for iron inputs to the ocean

NASA Astrophysics Data System (ADS)

Albani, S.; Mahowald, N. M.; Murphy, L. N.; Raiswell, R.; Moore, J. K.; Anderson, R. F.; McGee, D.; Bradtmiller, L. I.; Delmonte, B.; Hesse, P. P.; Mayewski, P. A.

2016-04-01

Changing climate conditions affect dust emissions and the global dust cycle, which in turn affects climate and biogeochemistry. In this study we use observationally constrained model reconstructions of the global dust cycle since the Last Glacial Maximum, combined with different simplified assumptions of atmospheric and sea ice processing of dust-borne iron, to provide estimates of soluble iron deposition to the oceans. For different climate conditions, we discuss uncertainties in model-based estimates of atmospheric processing and dust deposition to key oceanic regions, highlighting the large degree of uncertainty of this important variable for ocean biogeochemistry and the global carbon cycle. We also show the role of sea ice acting as a time buffer and processing agent, which results in a delayed and pulse-like soluble iron release into the ocean during the melting season, with monthly peaks up to ~17 Gg/month released into the Southern Oceans during the Last Glacial Maximum (LGM).

Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene

NASA Astrophysics Data System (ADS)

Rehfeld, Kira; Münch, Thomas; Ho, Sze Ling; Laepple, Thomas

2018-02-01

Changes in climate variability are as important for society to address as are changes in mean climate. Contrasting temperature variability during the Last Glacial Maximum and the Holocene can provide insights into the relationship between the mean state of the climate and its variability. However, although glacial-interglacial changes in variability have been quantified for Greenland, a global view remains elusive. Here we use a network of marine and terrestrial temperature proxies to show that temperature variability decreased globally by a factor of four as the climate warmed by 3-8 degrees Celsius from the Last Glacial Maximum (around 21,000 years ago) to the Holocene epoch (the past 11,500 years). This decrease had a clear zonal pattern, with little change in the tropics (by a factor of only 1.6-2.8) and greater change in the mid-latitudes of both hemispheres (by a factor of 3.3-14). By contrast, Greenland ice-core records show a reduction in temperature variability by a factor of 73, suggesting influences beyond local temperature or a decoupling of atmospheric and global surface temperature variability for Greenland. The overall pattern of reduced variability can be explained by changes in the meridional temperature gradient, a mechanism that points to further decreases in temperature variability in a warmer future.

Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene.

PubMed

Rehfeld, Kira; Münch, Thomas; Ho, Sze Ling; Laepple, Thomas

2018-02-15

Changes in climate variability are as important for society to address as are changes in mean climate. Contrasting temperature variability during the Last Glacial Maximum and the Holocene can provide insights into the relationship between the mean state of the climate and its variability. However, although glacial-interglacial changes in variability have been quantified for Greenland, a global view remains elusive. Here we use a network of marine and terrestrial temperature proxies to show that temperature variability decreased globally by a factor of four as the climate warmed by 3-8 degrees Celsius from the Last Glacial Maximum (around 21,000 years ago) to the Holocene epoch (the past 11,500 years). This decrease had a clear zonal pattern, with little change in the tropics (by a factor of only 1.6-2.8) and greater change in the mid-latitudes of both hemispheres (by a factor of 3.3-14). By contrast, Greenland ice-core records show a reduction in temperature variability by a factor of 73, suggesting influences beyond local temperature or a decoupling of atmospheric and global surface temperature variability for Greenland. The overall pattern of reduced variability can be explained by changes in the meridional temperature gradient, a mechanism that points to further decreases in temperature variability in a warmer future.

Fire and deforestation dynamics in Amazonia (1973–2014)

PubMed Central

Field, Robert D.; van der Werf, Guido R.; Estrada de Wagt, Ivan A.; Houghton, Richard A.; Rizzo, Luciana V.; Artaxo, Paulo; Tsigaridis, Kostas

2017-01-01

Abstract Consistent long‐term estimates of fire emissions are important to understand the changing role of fire in the global carbon cycle and to assess the relative importance of humans and climate in shaping fire regimes. However, there is limited information on fire emissions from before the satellite era. We show that in the Amazon region, including the Arc of Deforestation and Bolivia, visibility observations derived from weather stations could explain 61% of the variability in satellite‐based estimates of bottom‐up fire emissions since 1997 and 42% of the variability in satellite‐based estimates of total column carbon monoxide concentrations since 2001. This enabled us to reconstruct the fire history of this region since 1973 when visibility information became available. Our estimates indicate that until 1987 relatively few fires occurred in this region and that fire emissions increased rapidly over the 1990s. We found that this pattern agreed reasonably well with forest loss data sets, indicating that although natural fires may occur here, deforestation and degradation were the main cause of fires. Compared to fire emissions estimates based on Food and Agricultural Organization's Global Forest and Resources Assessment data, our estimates were substantially lower up to the 1990s, after which they were more in line. These visibility‐based fire emissions data set can help constrain dynamic global vegetation models and atmospheric models with a better representation of the complex fire regime in this region. PMID:28286373

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.

LDAS-Monde: Global scale satellite driven Land Data Assimilation System based on SURFEX modelling platform

NASA Astrophysics Data System (ADS)

Munier, Simon; Albergel, Clément; Leroux, Delphine; Calvet, Jean-Christophe

2017-04-01

In the past decades, large efforts have been made to improve our understanding of the dynamics of the terrestrial water cycle, including vertical and horizontal water fluxes as well as water stored in the biosphere. The soil water content is closely related to the development of the vegetation, which is in turn closely related to the water and energy exchanges with the atmosphere (through evapotranspiration) as well as to carbon fluxes. Land Surface Models (LSMs) are usually designed to represent biogeophysical variables, such as Surface and Root Zone Soil Moisture (SSM, RZSM) or Leaf Area Index (LAI), in order to simulate water, energy and carbon fluxes at the interface between land and atmosphere. With the recent increase of satellite missions and derived products, LSMs can benefit from Earth Observations via Data Assimilation systems to improve their representation of different biogeophysical variables. This study, which is part of the eartH2Observe European project (http://www.earth2observe.eu), presents LDAS-Monde, a global Land Data Assimilation System using an implementation of the Simplified Extended Kalman Filter (SEKF) in the Météo-France's modelling platform (SURFEX). SURFEX is based on the coupling of the multilayer, CO2-responsive version of the Interactions Between Soil, Biosphere, and Atmosphere model (ISBA) coupled with Météo-France's version of the Total Runoff Integrating Pathways continental hydrological system (CTRIP). Two global operational datasets derived from satellite observations are assimilated simultaneously: (i) SSM from the ESA Climate Change Initiative and (ii) LAI from the Copernicus Global Land Service project. Atmospheric forcing used in SURFEX are derived from the ERA-Interim reanalysis and corrected from GPCC precipitations. The simulations are conducted at the global scale at a 1 degree spatial resolution over the period 2000-2014. An analysis of the model sensitivity to the assimilated observations is performed over different regions of the globe under various hydro-climatic conditions. The impact of the SEKF on different biogeophysical and hydrological variables is assessed. It is shown that the assimilation scheme greatly improves the representation of the observed variables (SSM and LAI) and that it effectively affects most of the other variables related to the terrestrial water and vegetation cycles. Future developments include the optimization of LDAS-Monde in order to improve the spatial resolution and then take full advantage of the potential of Earth Observations.

The SeaFlux Turbulent Flux Dataset Version 1.0 Documentation

NASA Technical Reports Server (NTRS)

Clayson, Carol Anne; Roberts, J. Brent; Bogdanoff, Alec S.

2012-01-01

Under the auspices of the World Climate Research Programme (WCRP) Global Energy and Water cycle EXperiment (GEWEX) Data and Assessment Panel (GDAP), the SeaFlux Project was created to investigate producing a high-resolution satellite-based dataset of surface turbulent fluxes over the global oceans. The most current release of the SeaFlux product is Version 1.0; this represents the initial release of turbulent surface heat fluxes, associated near-surface variables including a diurnally varying sea surface temperature.

Analysis of terrain map matching using multisensing techniques for applications to autonomous vehicle navigation

NASA Technical Reports Server (NTRS)

Page, Lance; Shen, C. N.

1991-01-01

This paper describes skyline-based terrain matching, a new method for locating the vantage point of laser range-finding measurements on a global map previously prepared by satellite or aerial mapping. Skylines can be extracted from the range-finding measurements and modelled from the global map, and are represented in parametric, cylindrical form with azimuth angle as the independent variable. The three translational parameters of the vantage point are determined with a three-dimensional matching of these two sets of skylines.

VIIRS Marine Isoprene Product and Initial Applications

NASA Astrophysics Data System (ADS)

Tong, D.; Wang, M.; Wang, B.; Pan, L.; Lee, P.; Goldberg, M.

2017-12-01

Isoprene is a reactive biogenic hydrocarbon that affects atmospheric chemistry, aerosol loading, and cloud formation. We have developed a marine isoprene emission algorithm based on ocean color data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP). and global meteorology simulated by NOAA Global Forecasting System (GFS). This algorithm is implemented to generate a multi-year data record (2012-2015) of marine isoprene. The product was validated using historic ocean observations of marine isoprene, as well as in-situ data collected during two recent cruises (SPACES/OASIS in 2014 and ASTRA-OMZ in 2015). Result shows that the VIIRS product has captured the seasonal and spatial variability of global oceanic isoprene emission, which is controlled by a myriad of biological and environmental variables including chlorophyll-a concentration, phytoplankton functional types, seawater light attenuation rate, wind speed, and sea surface temperature. The VIIRS isoprene emission displays considerable seasonal and spatial variations, with peaks in spring over seawater abundant with nutrient inputs. Year to year variations are small, with the annual global emissions ranging from 0.20 to 0.25 Tg C/yr. This new dataset provides the first multi-year observations of global isoprene emissions that can be used to study a variety of environmental issues such as coastal air quality, global aerosol, and cloud formation. Some "early-adopter" applications of this product are briefly discussed.

Role of vegetation in interplay of climate, soil and groundwater recharge in a global dataset

NASA Astrophysics Data System (ADS)

Kim, J. H.; Jackson, R. B.

2010-12-01

Groundwater is an essential resource for people and ecosystems worldwide. Our capacity to ameliorate predicted global water shortages and to maintain sustainable water supplies depend on a better understanding of the controls of recharge and how vegetation change may affect recharge mechanisms. The goals of this study are to quantify the importance of vegetation as a dominant control on recharge globally and to compare the importance of vegetation with other hydrologically important variables, including climate and soil. We based our global analysis on > 500 recharge estimates from the literature that contained information on vegetation, soil and climate or location. Plant functional types significantly affected groundwater recharge rates substantially. After climatic factors (water inputs, PET, and seasonality), vegetation types explained about 15% of the residuals in the dataset. Across all climatic factors, croplands had the highest recharge rates, followed by grasslands, scrublands and woodlands (average recharge: 75, 63, 30, 22 mm/yr respectively). Recharge under woodlands showed the most nonlinear response to water inputs. Differences in recharge between the vegetation types were more exaggerated at arid climates and in clay soils, indicating greater biological control on soil water fluxes in these conditions. Our results shows that vegetation greatly affects recharge rates globally and alters relationship between recharge and physical variables allowing us to better predict recharge rates globally.

The Navy’s Application of Ocean Forecasting to Decision Support

DTIC Science & Technology

2014-09-01

Prediction Center (OPC) website for graphics or the National Operational Model Archive and Distribution System ( NOMADS ) for data files. Regional...inputs: » GLOBE = Global Land One-km Base Elevation » WVS = World Vector Shoreline » DBDB2 = Digital Bathymetry Data Base 2 minute resolution » DBDBV... Digital Bathymetry Data Base variable resolution Oceanography | Vol. 27, No.3130 Very High-Resolution Coastal Circulation Models Nearshore

Estimates of reservoir methane emissions based on a spatially balanced probabilistic-survey

EPA Science Inventory

Global estimates of methane (CH4) emissions from reservoirs are poorly constrained, partly due to the challenges of accounting for intra-reservoir spatial variability. Reservoir-scale emission rates are often estimated by extrapolating from measurement made at a few locations; h...

Can we detect oceanic biodiversity hotspots from space?

PubMed

De Monte, Silvia; Soccodato, Alice; Alvain, Séverine; d'Ovidio, Francesco

2013-10-01

Understanding the variability of marine biodiversity is a central issue in microbiology. Current observational programs are based on in situ studies, but their implementation at the global scale is particularly challenging, owing to the ocean extent, its temporal variability and the heterogeneity of the data sources on which compilations are built. Here, we explore the possibility of identifying phytoplanktonic biodiversity hotspots from satellite. We define a Shannon entropy index based on patchiness in ocean color bio-optical anomalies. This index provides a high resolution (1 degree) global coverage. It shows a relation to temperature and mid-latitude maxima in accordance with those previously evidenced in microbiological biodiversity model and observational studies. Regional maxima are in remarkable agreement with several known biodiversity hotspots for plankton organisms and even for higher levels of the marine trophic chain, as well as with some in situ planktonic biodiversity estimates (from Atlantic Meridional Transect cruise). These results encourage to explore marine biodiversity with a coordinated effort of the molecular, ecological and remote sensing communities.

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.

Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability

NASA Astrophysics Data System (ADS)

Zheng, Xiao-Tong; Hui, Chang; Yeh, Sang-Wook

2018-06-01

El Niño-Southern Oscillation (ENSO) is the dominant mode of variability in the coupled ocean-atmospheric system. Future projections of ENSO change under global warming are highly uncertain among models. In this study, the effect of internal variability on ENSO amplitude change in future climate projections is investigated based on a 40-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. A large uncertainty is identified among ensemble members due to internal variability. The inter-member diversity is associated with a zonal dipole pattern of sea surface temperature (SST) change in the mean along the equator, which is similar to the second empirical orthogonal function (EOF) mode of tropical Pacific decadal variability (TPDV) in the unforced control simulation. The uncertainty in CESM-LE is comparable in magnitude to that among models of the Coupled Model Intercomparison Project phase 5 (CMIP5), suggesting the contribution of internal variability to the intermodel uncertainty in ENSO amplitude change. However, the causations between changes in ENSO amplitude and the mean state are distinct between CESM-LE and CMIP5 ensemble. The CESM-LE results indicate that a large ensemble of 15 members is needed to separate the relative contributions to ENSO amplitude change over the twenty-first century between forced response and internal variability.

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.

Sensitivity of potential evapotranspiration estimation to the Thornthwaite and Penman-Monteith methods in the study of global drylands

NASA Astrophysics Data System (ADS)

Yang, Qing; Ma, Zhuguo; Zheng, Ziyan; Duan, Yawen

2017-12-01

Drylands are among those regions most sensitive to climate and environmental changes and human-induced perturbations. The most widely accepted definition of the term dryland is a ratio, called the Surface Wetness Index (SWI), of annual precipitation to potential evapotranspiration (PET) being below 0.65. PET is commonly estimated using the Thornthwaite (PET Th) and Penman-Monteith equations (PET PM). The present study compared spatiotemporal characteristics of global drylands based on the SWI with PET Th and PET PM. Results showed vast differences between PET Th and PET PM; however, the SWI derived from the two kinds of PET showed broadly similar characteristics in the interdecadal variability of global and continental drylands, except in North America, with high correlation coefficients ranging from 0.58 to 0.89. It was found that, during 1901-2014, global hyper-arid and semi-arid regions expanded, arid and dry sub-humid regions contracted, and drylands underwent interdecadal fluctuation. This was because precipitation variations made major contributions, whereas PET changes contributed to a much lesser degree. However, distinct differences in the interdecadal variability of semi-arid and dry sub-humid regions were found. This indicated that the influence of PET changes was comparable to that of precipitation variations in the global dry-wet transition zone. Additionally, the contribution of PET changes to the variations in global and continental drylands gradually enhanced with global warming, and the Thornthwaite method was found to be increasingly less applicable under climate change.

Consistency assessment with global and bridging development strategies in emerging markets.

PubMed

Li, Gang; Chen, Josh; Quan, Hui; Shentu, Yue

2013-11-01

Global trial strategy with the participation of all major regions including countries from emerging markets surely increases new drug development efficiency. Nevertheless, there are circumstances in which some countries in emerging markets cannot join the original global trial. To evaluate the extrapolability of the original trial results to a new country, a bridging trial in the country has to be conducted. In this paper, we first evaluate the efficiency loss of the bridging trial strategy compared to that of the global trial strategy as a function of between-study variability from consistency assessment perspective. The provided evidence should encourage countries in emerging markets to make a greater effort to participate in the original global trial. We then discuss sample size requirement for desired assurance probability for consistency assessment based on various approaches for both global and bridging trial strategies. Examples are presented for numerical demonstration and comparisons. Copyright © 2013 Elsevier Inc. All rights reserved.

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

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

Sun, Yan; Piao, Shilong; Huang, Mengtian

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

DOE PAGES

Sun, Yan; Piao, Shilong; Huang, Mengtian; ...

2015-12-23

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

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.

Potential of new machine learning methods for understanding long-term interannual variability of carbon and energy fluxes and states from site to global scale

NASA Astrophysics Data System (ADS)

Reichstein, M.; Jung, M.; Bodesheim, P.; Mahecha, M. D.; Gans, F.; Rodner, E.; Camps-Valls, G.; Papale, D.; Tramontana, G.; Denzler, J.; Baldocchi, D. D.

2016-12-01

Machine learning tools have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere-atmosphere exchange, while interannual variability is harder to model (e.g. Jung et al. 2011, JGR Biogeosciences). Here we hypothesize that innterannual variability is harder to describe for two reasons. 1) The signal-to-noise ratio in both, predictors (e.g. remote sensing) and target variables (e.g. net ecosystem exchange) is relatively weak, 2) The employed machine learning methods do not sufficiently account for dynamic lag and carry-over effects. In this presentation we can largely confirm both hypotheses: 1) We show that based on FLUXNET data and an ensemble of machine learning methods we can arrive at estimates of global NEE that correlate well with the residual land sink overall and CO2 flux inversions over latitudinal bands. Furthermore these results highlight the importance of variations in water availability for variations in carbon fluxes locally, while globally, as a scale-emergent property, tropical temperatures correlate well with the atmospheric CO2 growth rate, because of spatial anticorrelation and compensation of water availability. 2) We evidence with synthetic and real data that machine learning methods with embed dynamic memory effects of the system such as recurrent neural networks (RNNs) are able to better capture lag and carry-over effect which are caused by dynamic carbon pools in vegetation and soils. For these methods, long-term replicate observations are an essential asset.

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.

Cognitive variability in bipolar II disorder: who is cognitively impaired and who is preserved.

PubMed

Solé, Brisa; Jiménez, Esther; Torrent, Carla; Del Mar Bonnin, Caterina; Torres, Imma; Reinares, María; Priego, Ángel; Salamero, Manel; Colom, Francesc; Varo, Cristina; Vieta, Eduard; Martínez-Arán, Anabel

2016-05-01

Although it is well established that euthymic patients with bipolar disorder can have cognitive impairment, substantial heterogeneity exists and little is known about the extent and severity of impairment within the bipolar II disorder subtype. Therefore, the main aim of this study was to analyze cognitive variability in a sample of patients with bipolar II disorder. The neuropsychological performance of 116 subjects, including 64 euthymic patients with bipolar II disorder and 52 healthy control subjects, was examined and compared by means of a comprehensive neurocognitive battery. Neurocognitive data were analyzed using a cluster analysis to examine whether there were specific groups based on neurocognitive patterns. Subsequently, subjects from each cluster were compared on demographic, clinical, and functional variables. A three-cluster solution was identified with an intact neurocognitive group (n = 29, 48.3%), an intermediate or selectively impaired group (n = 24, 40.0%), and a globally impaired group (n = 7, 11.6%). Among the three clusters, statistically significant differences were observed in premorbid intelligence quotient (p = 0.002), global functional outcome (p = 0.021), and leisure activities (p = 0.001), with patients in the globally impaired cluster showing the lowest attainments. No differences in other clinical characteristics were found among the groups. These results confirm that neurocognitive variability is also present among patients with bipolar II disorder. Approximately one-half of the patients with bipolar II disorder were cognitively impaired, and among them 12% were severely and globally impaired. The identification of different cognitive profiles may help to develop cognitive remediation programs specifically tailored for each cognitive profile. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Satellite-based Assessment of Global Warm Cloud Properties Associated with Aerosols, Atmospheric Stability, and Diurnal Cycle

NASA Technical Reports Server (NTRS)

Matsui, Toshihisa; Masunaga, Hirohiko; Kreidenweis, Sonia M.; Pielke, Roger A., Sr.; Tao, Wei-Kuo; Chin, Mian; Kaufman, Yoram J.

2006-01-01

This study examines variability in marine low cloud properties derived from semi-global observations by the Tropical Rainfall Measuring Mission (TRMM) satellite, as linked to the aerosol index (AI) and lower-tropospheric stability (LTS). AI is derived from the Moderate Resolution Imaging Spectroradiometer (Terra MODIS) sensor and the Goddard Chemistry Aerosol Radiation and Transportation (GOCART) model, and is used to represent column-integrated aerosol concentrations. LTS is derived from the NCEP/NCAR reanalysis, and represents the background thermodynamic environment in which the clouds form. Global statistics reveal that cloud droplet size tends to be smallest in polluted (high-AI) and strong inversion (high-LTS) environments. Statistical quantification shows that cloud droplet size is better correlated with AI than it is with LTS. Simultaneously, the cloud liquid water path (CLWP) tends to decrease as AI increases. This correlation does not support the hypothesis or assumption that constant or increased CLWP is associated with high aerosol concentrations. Global variability in corrected cloud albedo (CCA), the product of cloud optical depth and cloud fraction, is very well explained by LTS, while both AI and LTS are needed to explain local variability in CCA. Most of the local correlations between AI and cloud properties are similar to the results from the global statistics, while weak anomalous aerosol-cloud correlations appear locally in the regions where simultaneous high (low) AI and low (high) LTS compensate each other. Daytime diurnal cycles explain additional variability in cloud properties. CCA has the largest diurnal cycle in high-LTS regions. Cloud droplet size and CLWP have weak diurnal cycles that differ between clean and polluted environments. The combined results suggest that investigations of marine low cloud radiative forcing and its relationship to hypothesized aerosol indirect effects must consider the combined effects of aerosols, thermodynamics, and the diurnal cycle.

Climate variability has a stabilizing effect on the coexistence of prairie grasses

PubMed Central

Adler, Peter B.; HilleRisLambers, Janneke; Kyriakidis, Phaedon C.; Guan, Qingfeng; Levine, Jonathan M.

2006-01-01

How expected increases in climate variability will affect species diversity depends on the role of such variability in regulating the coexistence of competing species. Despite theory linking temporal environmental fluctuations with the maintenance of diversity, the importance of climate variability for stabilizing coexistence remains unknown because of a lack of appropriate long-term observations. Here, we analyze three decades of demographic data from a Kansas prairie to demonstrate that interannual climate variability promotes the coexistence of three common grass species. Specifically, we show that (i) the dynamics of the three species satisfy all requirements of “storage effect” theory based on recruitment variability with overlapping generations, (ii) climate variables are correlated with interannual variation in species performance, and (iii) temporal variability increases low-density growth rates, buffering these species against competitive exclusion. Given that environmental fluctuations are ubiquitous in natural systems, our results suggest that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity. Field evidence for positive effects of variability on coexistence also emphasizes the need to consider changes in both climate means and variances when forecasting the effects of global change on species diversity. PMID:16908862

The role of the oceans in changes of the Earth's climate system

NASA Astrophysics Data System (ADS)

von Schuckmann, K.

2016-12-01

Any changes to the Earth's climate system affect an imbalance of the Earth's energy budget due to natural or human made climate forcing. The current positive Earth's energy imbalance is mostly caused by human activity, and is driving global warming. Variations in the world's ocean heat storage and its associated volume changes are a key factor to gauge global warming, to assess changes in the Earth's energy budget and to estimate contributions to the global sea level budget. Present-day sea-level rise is one of the major symptoms of the current positive Earth Energy Imbalance. Sea level also responds to natural climate variability that is superimposing and altering the global warming signal. The most prominent signature in the global mean sea level interannual variability is caused by El Niño-Southern Oscillation. It has been also shown that sea level variability in other regions of the Indo-Pacific area significantly alters estimates of the rate of sea level rise, i.e. in the Indonesian archipelago. In summary, improving the accuracy of our estimates of global Earth's climate state and variability is critical for advancing the understanding and prediction of the evolution of our climate, and an overview on recent findings on the role of the global ocean in changes of the Earth's climate system with particular focus on sea level variability in the Indo-Pacific region will be given in this contribution.

Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances.

PubMed

Sáez, Carlos; Robles, Montserrat; García-Gómez, Juan M

2017-02-01

Biomedical data may be composed of individuals generated from distinct, meaningful sources. Due to possible contextual biases in the processes that generate data, there may exist an undesirable and unexpected variability among the probability distribution functions (PDFs) of the source subsamples, which, when uncontrolled, may lead to inaccurate or unreproducible research results. Classical statistical methods may have difficulties to undercover such variabilities when dealing with multi-modal, multi-type, multi-variate data. This work proposes two metrics for the analysis of stability among multiple data sources, robust to the aforementioned conditions, and defined in the context of data quality assessment. Specifically, a global probabilistic deviation and a source probabilistic outlyingness metrics are proposed. The first provides a bounded degree of the global multi-source variability, designed as an estimator equivalent to the notion of normalized standard deviation of PDFs. The second provides a bounded degree of the dissimilarity of each source to a latent central distribution. The metrics are based on the projection of a simplex geometrical structure constructed from the Jensen-Shannon distances among the sources PDFs. The metrics have been evaluated and demonstrated their correct behaviour on a simulated benchmark and with real multi-source biomedical data using the UCI Heart Disease data set. The biomedical data quality assessment based on the proposed stability metrics may improve the efficiency and effectiveness of biomedical data exploitation and research.

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.

Change in the magnitude and mechanisms of global temperature variability with warming

PubMed Central

Brown, Patrick T.; Ming, Yi; Li, Wenhong; Hill, Spencer A.

2017-01-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future. PMID:29391875

Standardization of SOPs to Evaluations: Impacts on Regulatory Decisions using Learning and Memory as Case Studies

EPA Science Inventory

In an era of global trade and regulatory cooperation, consistent and scientifically based interpretation of developmental neurotoxicity (DNT) studies is essential, particularly for non­ standard assays and variable endpoints. Because there is flexibility in the selection of ...

Can we Observe and Assess Whether the Global Hydrological Cycle is "Intensifying"?

NASA Astrophysics Data System (ADS)

Wood, E. F.; Sheffield, J.

2012-12-01

There is controversy over whether the hydrological cycle is "intensifying" (or "accelerating"), and if so how and where? Resolving this critical question is a central goal of both national (e.g. NASA's Energy and Water cycle Study: NEWS) and international (WCRP Global Energy and Water cycle Experiment: GEWEX) programs. Its resolution has significant implications for understanding changes in hydroclimatic states and variability, and in future water security at regional to global scales. Over the last decade a number of papers have addressed trends and change in specific water cycle variables with results that can best be described as inconclusive, regardless of the conclusions of specific papers. In this presentation a number of recent studies will be reviewed for their consistency in assessing whether collectively one can make conclusions regarding how the hydrologic cycle is changing. The presentation will also demonstrate a pathway for analyzing where to observe for the detection of change based on a NASA-supported, global, 1983-2009, terrestrial water cycle Earth System Data Record project being led by the author. Initial results will be presented and a discussion presented on the extent that the proposed strategy can be used to detect change in the terrestrial hydrological cycle.

Employment relations and global health: a typological study of world labor markets.

PubMed

Chung, Haejoo; Muntaner, Carles; Benach, Joan

2010-01-01

In this study, the authors investigate the global labor market and employment relations, which are central building blocks of the welfare state; the aim is to propose a global typology of labor markets to explain global inequalities in population health. Countries are categorized into core (21), semi-peripheral (42), and peripheral (71) countries, based on gross national product per capita (Atlas method). Labor market-related variables and factors are then used to generate clusters of countries with principal components and cluster analysis methods. The authors then examine the relationship between the resulting clusters and health outcomes. The clusters of countries are largely geographically defined, each cluster with similar historical background and developmental strategy. However, there are interesting exceptions, which warrant further elaboration. The relationship between health outcomes and clusters largely follows the authors' expectations (except for communicable diseases): more egalitarian labor institutions have better health outcomes. The world system, then, can be divided according to different types of labor markets that are predictive of population health outcomes at each level of economic development. As is the case for health and social policies, variability in labor market characteristics is likely to reflect, in part, the relative strength of a country's political actors.

Global search tool for the Advanced Photon Source Integrated Relational Model of Installed Systems (IRMIS) database.

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

Quock, D. E. R.; Cianciarulo, M. B.; APS Engineering Support Division

2007-01-01

The Integrated Relational Model of Installed Systems (IRMIS) is a relational database tool that has been implemented at the Advanced Photon Source to maintain an updated account of approximately 600 control system software applications, 400,000 process variables, and 30,000 control system hardware components. To effectively display this large amount of control system information to operators and engineers, IRMIS was initially built with nine Web-based viewers: Applications Organizing Index, IOC, PLC, Component Type, Installed Components, Network, Controls Spares, Process Variables, and Cables. However, since each viewer is designed to provide details from only one major category of the control system, themore » necessity for a one-stop global search tool for the entire database became apparent. The user requirements for extremely fast database search time and ease of navigation through search results led to the choice of Asynchronous JavaScript and XML (AJAX) technology in the implementation of the IRMIS global search tool. Unique features of the global search tool include a two-tier level of displayed search results, and a database data integrity validation and reporting mechanism.« less

Bridging the Global Precipitation and Soil Moisture Active Passive Missions: Variability of Microwave Surface Emissivity from In situ and Remote Sensing Perspectives

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P.; Hong, Y.; Turk, J.

2016-12-01

The overland precipitation retrievals from satellite passive microwave (PMW) sensors such as the Global Precipitation Mission (GPM) microwave imager (GMI) are impacted by the land surface emissivity. The estimation of PMW emissivity faces challenges because it is highly variable under the influence of surface properties such as soil moisture, surface roughness and vegetation. This study proposes an improved quantitative understanding of the relationship between the emissivity and surface parameters. Surface parameter information is obtained through (i) in-situ measurements from the International Soil Moisture Network and (ii) satellite measurements from the Soil Moisture Active and Passive mission (SMAP) which provides global scale soil moisture estimates. The variation of emissivity is quantified with soil moisture, surface temperature and vegetation at various frequencies/polarization and over different types of land surfaces to sheds light into the processes governing the emission of the land. This analysis is used to estimate the emissivity under rainy conditions. The framework built with in-situ measurements serves as a benchmark for satellite-based analyses, which paves a way toward global scale emissivity estimates using SMAP.

Global lake response to the recent warming hiatus

NASA Astrophysics Data System (ADS)

Winslow, Luke A.; Leach, Taylor H.; Rose, Kevin C.

2018-05-01

Understanding temporal variability in lake warming rates over decadal scales is important for understanding observed change in aquatic systems. We analyzed a global dataset of lake surface water temperature observations (1985‑2009) to examine how lake temperatures responded to a recent global air temperature warming hiatus (1998‑2012). Prior to the hiatus (1985‑1998), surface water temperatures significantly increased at an average rate of 0.532 °C decade‑1 (±0.214). In contrast, water temperatures did not change significantly during the hiatus (average rate ‑0.087 °C decade‑1 ±0.223). Overall, 83% of lakes in our dataset (129 of 155) had faster warming rates during the pre-hiatus period than during the hiatus period. These results demonstrate that lakes have exhibited decadal-scale variability in warming rates coherent with global air temperatures and represent an independent line of evidence for the recent warming hiatus. Our analyses provide evidence that lakes are sentinels of broader climatological processes and indicate that warming rates based on datasets where a large proportion of observations were collected during the hiatus period may underestimate longer-term trends.

NASA Scientific Forum on Climate Variability and Global Change: UNISPACE 3

NASA Technical Reports Server (NTRS)

Schiffer, Robert A.; Unninayar, Sushel

1999-01-01

The Forum on Climate Variability and Global Change is intended to provide a glimpse into some of the advances made in our understanding of key scientific and environmental issues resulting primarily from improved observations and modeling on a global basis. This publication contains the papers presented at the forum.

Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

ERIC Educational Resources Information Center

Weissman, Alexander

2013-01-01

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…

Trends and Variability of Global Fire Emissions Due To Historical Anthropogenic Activities

NASA Astrophysics Data System (ADS)

Ward, Daniel S.; Shevliakova, Elena; Malyshev, Sergey; Rabin, Sam

2018-01-01

Globally, fires are a major source of carbon from the terrestrial biosphere to the atmosphere, occurring on a seasonal cycle and with substantial interannual variability. To understand past trends and variability in sources and sinks of terrestrial carbon, we need quantitative estimates of global fire distributions. Here we introduce an updated version of the Fire Including Natural and Agricultural Lands model, version 2 (FINAL.2), modified to include multiday burning and enhanced fire spread rate in forest crowns. We demonstrate that the improved model reproduces the interannual variability and spatial distribution of fire emissions reported in present-day remotely sensed inventories. We use FINAL.2 to simulate historical (post-1700) fires and attribute past fire trends and variability to individual drivers: land use and land cover change, population growth, and lightning variability. Global fire emissions of carbon increase by about 10% between 1700 and 1900, reaching a maximum of 3.4 Pg C yr-1 in the 1910s, followed by a decrease to about 5% below year 1700 levels by 2010. The decrease in emissions from the 1910s to the present day is driven mainly by land use change, with a smaller contribution from increased fire suppression due to increased human population and is largest in Sub-Saharan Africa and South Asia. Interannual variability of global fire emissions is similar in the present day as in the early historical period, but present-day wildfires would be more variable in the absence of land use change.

On the fall 2010 Enhancements of the Global Precipitation Climatology Centre's Data Sets

NASA Astrophysics Data System (ADS)

Becker, A. W.; Schneider, U.; Meyer-Christoffer, A.; Ziese, M.; Finger, P.; Rudolf, B.

2010-12-01

Precipitation is meanwhile a top listed parameter on the WMO GCOS list of 44 essential climate variables (ECV). This is easily justified by its crucial role to sustain any form of life on earth as major source of fresh water, its major impact on weather, climate, climate change and related issues of society’s adaption to the latter. Finally its occurrence is highly variable in space and time thus bearing the potential to trigger major flood and draught related disasters. Since its start in 1989 the Global precipitation Climatology Centre (GPCC) performs global analyses of monthly precipitation for the earth’s land-surface on the basis of in-situ measurements. The effort was inaugurated as part of the Global Precipitation Climatology Project of the WMO World Climate Research Program (WCRP). Meanwhile, the data set has continuously grown both in temporal coverage (original start of the evaluation period was 1986), as well as extent and quality of the underlying data base. The number of stations involved in the related data base has approximately doubled in the past 8 years by trespassing the 40, 60 and 80k thresholds in 2002, 2006 and 2010. Core data source of the GPCC analyses are the data from station networks operated by the National Meteorological Services worldwide; data deliveries have been received from ca. 190 countries. The GPCC integrates also other global precipitation data collections (i.e. FAO, CRU and GHCN), as well as regional data sets. Currently the Africa data set from S. Nicholson (Univ. Tallahassee) is integrated. As a result of these efforts the GPCC holds the worldwide largest and most comprehensive collection of precipitation data, which is continuously updated and extended. Due to the high spatial-temporal variability of precipitation, even its global analysis requires this high number of stations to provide for a sufficient density of measurement data on almost any place on the globe. The acquired data sets are pre-checked, reformatted and then imported into a relational data base, where they are archived separately in source specific slots, thus allowing an inter-comparison of data from the different sources. Any time new data sets are imported to the data base the metadata in the input data set are compared to those already available in the data base. In case of discrepancies (e.g. deviating coordinates), external geographical sources of information are utilized to decide whether a correction of the metadata in the data base is required or not, thus resulting in a perpetual improvement of the station meta data. The presentation shall give an account on the four major products derived from the GPCC data base, which are two near real-time ones comprising the precipitation data retrieved from the GTS, and two offline products that allow for hydro-climatological assessments. The real-time products are used for example to calibrate Satellite based precipitation measurements. To illustrate the potential of the offline (Full Data) products we will present an asessment of the strong 2010 La Nina season that has apparently caused severe weather patterns world wide, including the flood disasters in Pakistan and Wuhan, China.

Collaboration pathway(s) using new tools for optimizing `operational' climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2015-09-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a long term solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the collective needs of policy makers, scientific communities and global academic users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent rule-based expert system (RBES) optimization modeling of the intended NPOESS architecture becomes a surrogate for global operational climate monitoring architecture(s). These rulebased systems tools provide valuable insight for global climate architectures, by comparison/evaluation of alternatives and the sheer range of trade space explored. Optimization of climate monitoring architecture(s) for a partial list of ECV (essential climate variables) is explored and described in detail with dialogue on appropriate rule-based valuations. These optimization tool(s) suggest global collaboration advantages and elicit responses from the audience and climate science community. This paper will focus on recent research exploring joint requirement implications of the high profile NPOESS architecture and extends the research and tools to optimization for a climate centric case study. This reflects work from SPIE RS Conferences 2013 and 2014, abridged for simplification30, 32. First, the heavily securitized NPOESS architecture; inspired the recent research question - was Complexity (as a cost/risk factor) overlooked when considering the benefits of aggregating different missions into a single platform. Now years later a complete reversal; should agencies considering Disaggregation as the answer. We'll discuss what some academic research suggests. Second, using the GCOS requirements of earth climate observations via ECV (essential climate variables) many collected from space-based sensors; and accepting their definitions of global coverages intended to ensure the needs of major global and international organizations (UNFCCC and IPCC) are met as a core objective. Consider how new optimization tools like rule-based engines (RBES) offer alternative methods of evaluating collaborative architectures and constellations? What would the trade space of optimized operational climate monitoring architectures of ECV look like? Third, using the RBES tool kit (2014) demonstrate with application to a climate centric rule-based decision engine - optimizing architectural trades of earth observation satellite systems, allowing comparison(s) to existing architectures and gaining insights for global collaborative architectures. How difficult is it to pull together an optimized climate case study - utilizing for example 12 climate based instruments on multiple existing platforms and nominal handful of orbits; for best cost and performance benefits against the collection requirements of representative set of ECV. How much effort and resources would an organization expect to invest to realize these analysis and utility benefits?

High-resolution station-based diurnal ionospheric total electron content (TEC) from dual-frequency GPS observations

NASA Astrophysics Data System (ADS)

ćepni, Murat S.; Potts, Laramie V.; Miima, John B.

2013-09-01

electron content (TEC) estimates derived from Global Navigation Satellite System (GNSS) signal delays provide a rich source of information about the Earth's ionosphere. Networks of Global Positioning System (GPS) receivers data can be used to represent the ionosphere by a Global Ionospheric Map (GIM). Data input for GIMs is dual-frequency GNSS-only or a mixture of GNSS and altimetry observations. Parameterization of GNSS-only GIMs approaches the ionosphere as a single-layer model (SLM) to determine GPS TEC models over a region. Limitations in GNSS-only GIM TEC are due largely to the nonhomogenous global distribution of GPS tracking stations with large data gaps over the oceans. The utility of slant GPS ionospheric-induced path delays for high temporal resolution from a single-station data rate offers better representation of TEC over a small region. A station-based vertical TEC (TECV) approach modifies the traditional single-layer model (SLM) GPS TEC method by introducing a zenith angle weighting (ZAW) filter to capture signal delays from mostly near-zenith satellite passes. Comparison with GIMs shows the station-dependent TEC (SD-TEC) model exhibits robust performance under variable space weather conditions. The SD-TEC model was applied to investigate ionospheric TEC variability during the geomagnetic storm event of 9 March 2012 at midlatitude station NJJJ located in New Jersey, USA. The high temporal resolution TEC results suggest TEC production and loss rate differences before, during, and after the storm.

Detection of greenhouse-gas-induced climatic change. Progress report, 1 December 1991--30 June 1994

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

Wigley, T.M.L.; Jones, P.D.

1994-07-01

In addition to changes due to variations in greenhouse gas concentrations, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the enhanced greenhouse effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas concentration changes and other factors. Analyses will be guided bymore » a variety of models, from simple energy balance climate models to ocean General Circulation Models. Appendices A--G contain the following seven papers: (A) Recent global warmth moderated by the effects of the Mount Pinatubo eruption; (B) Recent warming in global temperature series; (C) Correlation methods in fingerprint detection studies; (D) Balancing the carbon budget. Implications for projections of future carbon dioxide concentration changes; (E) A simple model for estimating methane concentration and lifetime variations; (F) Implications for climate and sea level of revised IPCC emissions scenarios; and (G) Sulfate aerosol and climatic change.« less

Coastal Low-Level Wind Jets: A Global Study Based On An Ensemble Of Reanalysis

NASA Astrophysics Data System (ADS)

Cardoso, R. M.; Lima, D. C. A.; Soares, P. M. M.; Semedo, A.

2017-12-01

Reanalyses data are a useful tool for climate and atmospheric studies since they provide physically consistent spatial and temporal information of observable and unobservable atmospheric parameters. Here, we propose the analysis of coastal low-level jets (CLLJs) resorting to three global reanalyses. The six hourly data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), the Japanese 55-year Reanalysis (JRA-55) and the Modern Era Retrospective-analysis for Research and Applications (MERRA2), are used to build an ensemble of reanalyses, for a period encompassing 1980-2016. A detailed global climatology of CLLJs is presented based on a reanalyses ensemble. This gives robustness to the CLLJs representation and also reduces uncertainty. The annual and diurnal cycle as well as the inter-annual variability are analysed in order to evaluate the temporal fluctuations of frequency of occurrence of CLLJ. The ensemble mean displays a good representation of their seasonal spatial variability. The Oman and Benguela CLLJs show, respectively, a decrease and increase of frequency of occurrence, which is statistically significant during boreal summer and austral spring for the period of study. The Oman CLLJ is the most intense and occurs in higher altitudes when compared with the other jets occurring during the season where each CLLJs have higher mean incidence.

Evaluation of the Atmospheric Boundary-Layer Electrical Variability

NASA Astrophysics Data System (ADS)

Anisimov, Sergey V.; Galichenko, Sergey V.; Aphinogenov, Konstantin V.; Prokhorchuk, Aleksandr A.

2017-12-01

Due to the chaotic motion of charged particles carried by turbulent eddies, electrical quantities in the atmospheric boundary layer (ABL) have short-term variability superimposed on long-term variability caused by sources from regional to global scales. In this study the influence of radon exhalation rate, aerosol distribution and turbulent transport efficiency on the variability of fair-weather atmospheric electricity is investigated via Lagrangian stochastic modelling. For the mid-latitude lower atmosphere undisturbed by precipitation, electrified clouds, or thunderstorms, the model is capable of reproducing the diurnal variation in atmospheric electrical parameters detected by ground-based measurements. Based on the analysis of field observations and numerical simulation it is found that the development of the convective boundary layer, accompanied by an increase in turbulent kinetic energy, forms the vertical distribution of radon and its decaying short-lived daughters to be approximately coincident with the barometric law for several eddy turnover times. In the daytime ABL the vertical distribution of atmospheric electrical conductivity tends to be uniform except within the surface layer, due to convective mixing of radon and its radioactive decay products. At the same time, a decrease in the conductivity near the ground is usually observed. This effect leads to an enhanced ground-level atmospheric electric field compared to that normally observed in the nocturnal stably-stratified boundary layer. The simulation showed that the variability of atmospheric electric field in the ABL associated with internal origins is significant in comparison to the variability related to changes in global parameters. It is suggested that vertical profiles of electrical quantities can serve as informative parameters on ABL turbulent dynamics and can even more broadly characterize the state of the environment.

Global fast dynamic terminal sliding mode control for a quadrotor UAV.

PubMed

Xiong, Jing-Jing; Zhang, Guo-Bao

2017-01-01

A control method based on global fast dynamic terminal sliding mode control (TSMC) technique is proposed to design the flight controller for performing the finite-time position and attitude tracking control of a small quadrotor UAV. Firstly, the dynamic model of the quadrotor is divided into two subsystems, i.e., a fully actuated subsystem and an underactuated subsystem. Secondly, the dynamic flight controllers of the quadrotor are formulated based on global fast dynamic TSMC, which is able to guarantee that the position and velocity tracking errors of all system state variables converge to zero in finite-time. Moreover, the global fast dynamic TSMC is also able to eliminate the chattering phenomenon caused by the switching control action and realize the high precision performance. In addition, the stabilities of two subsystems are demonstrated by Lyapunov theory, respectively. Lastly, the simulation results are given to illustrate the effectiveness and robustness of the proposed control method in the presence of external disturbances. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Maintaining Balance: The Increasing Role of Energy Storage for Renewable Integration

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

Stenclik, Derek; Denholm, Paul; Chalamala, Babu

For nearly a century, global power systems have focused on three key functions: generating, transmitting, and distributing electricity as a real-time commodity. Physics requires that electricity generation always be in real-time balance with load-despite variability in load on time scales ranging from subsecond disturbances to multiyear trends. With the increasing role of variable generation from wind and solar, the retirement of fossil-fuel-based generation, and a changing consumer demand profile, grid operators are using new methods to maintain this balance.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks.

PubMed

Navarro Jimenez, M; Le Maître, O P; Knio, O M

2016-12-28

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Extending Structural Analyses of the Rosenberg Self-Esteem Scale to Consider Criterion-Related Validity: Can Composite Self-Esteem Scores Be Good Enough?

PubMed

Donnellan, M Brent; Ackerman, Robert A; Brecheen, Courtney

2016-01-01

Although the Rosenberg Self-Esteem Scale (RSES) is the most widely used measure of global self-esteem in the literature, there are ongoing disagreements about its factor structure. This methodological debate informs how the measure should be used in substantive research. Using a sample of 1,127 college students, we test the overall fit of previously specified models for the RSES, including a newly proposed bifactor solution (McKay, Boduszek, & Harvey, 2014 ). We extend previous work by evaluating how various latent factors from these structural models are related to a set of criterion variables frequently studied in the self-esteem literature. A strict unidimensional model poorly fit the data, whereas models that accounted for correlations between negatively and positively keyed items tended to fit better. However, global factors from viable structural models had similar levels of association with criterion variables and with the pattern of results obtained with a composite global self-esteem variable calculated from observed scores. Thus, we did not find compelling evidence that different structural models had substantive implications, thereby reducing (but not eliminating) concerns about the integrity of the self-esteem literature based on overall composite scores for the RSES.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

DOE PAGES

Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

2016-12-23

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes thatmore » the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. Here, a sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.« less

Global Qualitative Flow-Path Modeling for Local State Determination in Simulation and Analysis

NASA Technical Reports Server (NTRS)

Malin, Jane T. (Inventor); Fleming, Land D. (Inventor)

1998-01-01

For qualitative modeling and analysis, a general qualitative abstraction of power transmission variables (flow and effort) for elements of flow paths includes information on resistance, net flow, permissible directions of flow, and qualitative potential is discussed. Each type of component model has flow-related variables and an associated internal flow map, connected into an overall flow network of the system. For storage devices, the implicit power transfer to the environment is represented by "virtual" circuits that include an environmental junction. A heterogeneous aggregation method simplifies the path structure. A method determines global flow-path changes during dynamic simulation and analysis, and identifies corresponding local flow state changes that are effects of global configuration changes. Flow-path determination is triggered by any change in a flow-related device variable in a simulation or analysis. Components (path elements) that may be affected are identified, and flow-related attributes favoring flow in the two possible directions are collected for each of them. Next, flow-related attributes are determined for each affected path element, based on possibly conflicting indications of flow direction. Spurious qualitative ambiguities are minimized by using relative magnitudes and permissible directions of flow, and by favoring flow sources over effort sources when comparing flow tendencies. The results are output to local flow states of affected components.

Vorticity-divergence semi-Lagrangian global atmospheric model SL-AV20: dynamical core

NASA Astrophysics Data System (ADS)

Tolstykh, Mikhail; Shashkin, Vladimir; Fadeev, Rostislav; Goyman, Gordey

2017-05-01

SL-AV (semi-Lagrangian, based on the absolute vorticity equation) is a global hydrostatic atmospheric model. Its latest version, SL-AV20, provides global operational medium-range weather forecast with 20 km resolution over Russia. The lower-resolution configurations of SL-AV20 are being tested for seasonal prediction and climate modeling. The article presents the model dynamical core. Its main features are a vorticity-divergence formulation at the unstaggered grid, high-order finite-difference approximations, semi-Lagrangian semi-implicit discretization and the reduced latitude-longitude grid with variable resolution in latitude. The accuracy of SL-AV20 numerical solutions using a reduced lat-lon grid and the variable resolution in latitude is tested with two idealized test cases. Accuracy and stability of SL-AV20 in the presence of the orography forcing are tested using the mountain-induced Rossby wave test case. The results of all three tests are in good agreement with other published model solutions. It is shown that the use of the reduced grid does not significantly affect the accuracy up to the 25 % reduction in the number of grid points with respect to the regular grid. Variable resolution in latitude allows us to improve the accuracy of a solution in the region of interest.

Global sensitivity analysis in stochastic simulators of uncertain reaction networks

NASA Astrophysics Data System (ADS)

Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

2016-12-01

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

SSS variability inferred from recent SMOS reprocessing at CATDS

NASA Astrophysics Data System (ADS)

Boutin, Jacqueline; Vergely, Jean-Luc; Marchand, Stéphane; Tarot, Stéphane; Hasson, Audrey; Reverdin, Gilles

2017-04-01

The Soil Moisture and Ocean Salinity (SMOS) satellite mission has monitored sea surface salinity (SSS) over the global ocean for over 7 years. In this poster, we present results obtained at the LOCEAN/ACRI-st expertise center using recent CATDS (Centre Aval de Traitement des Données) SMOS RE05 reprocessing., We find that correction for systematic errors and removal of data contaminated by ice and radio frequency interferences in fresh regions (river mouths, high latitudes) has been improved with respect to SMOS CATDS RE04 reprocessing. We analyze SSS variability as observed by SMOS on a wide range of spatial and temporal scales using various statistical indicators such as mean, median, standard deviation, minimum, maximum values and spectral analysis. We compare them with ARGO interpolated fields (In Situ Analysis System fields) at global scale and with ship SSS transects from the GOSUD and ORE SSS data base. This allows us 1) to demonstrate and quantify the improvement of SMOS SSS fields with respect to earlier versions and 2) to study SSS variability, especially at spatial scales between 50km and 600km not well covered globally by in situ network. The complementarity of this information with respect to SMAP (Soil Moisture Active Passive) SSS fields will be discussed.

Multicriteria evaluation of discharge simulation in Dynamic Global Vegetation Models

NASA Astrophysics Data System (ADS)

Yang, Hui; Piao, Shilong; Zeng, Zhenzhong; Ciais, Philippe; Yin, Yi; Friedlingstein, Pierre; Sitch, Stephen; Ahlström, Anders; Guimberteau, Matthieu; Huntingford, Chris; Levis, Sam; Levy, Peter E.; Huang, Mengtian; Li, Yue; Li, Xiran; Lomas, Mark R.; Peylin, Philippe; Poulter, Ben; Viovy, Nicolas; Zaehle, Soenke; Zeng, Ning; Zhao, Fang; Wang, Lei

2015-08-01

In this study, we assessed the performance of discharge simulations by coupling the runoff from seven Dynamic Global Vegetation Models (DGVMs; LPJ, ORCHIDEE, Sheffield-DGVM, TRIFFID, LPJ-GUESS, CLM4CN, and OCN) to one river routing model for 16 large river basins. The results show that the seasonal cycle of river discharge is generally modeled well in the low and middle latitudes but not in the high latitudes, where the peak discharge (due to snow and ice melting) is underestimated. For the annual mean discharge, the DGVMs chained with the routing model show an underestimation. Furthermore, the 30 year trend of discharge is also underestimated. For the interannual variability of discharge, a skill score based on overlapping of probability density functions (PDFs) suggests that most models correctly reproduce the observed variability (correlation coefficient higher than 0.5; i.e., models account for 50% of observed interannual variability) except for the Lena, Yenisei, Yukon, and the Congo river basins. In addition, we compared the simulated runoff from different simulations where models were forced with either fixed or varying land use. This suggests that both seasonal and annual mean runoff has been little affected by land use change but that the trend itself of runoff is sensitive to land use change. None of the models when considered individually show significantly better performances than any other and in all basins. This suggests that based on current modeling capability, a regional-weighted average of multimodel ensemble projections might be appropriate to reduce the bias in future projection of global river discharge.

Multi-criteria Evaluation of Discharge Simulation in Dynamic Global Vegetation Models

NASA Astrophysics Data System (ADS)

Yang, H.; Piao, S.; Zeng, Z.; Ciais, P.; Yin, Y.; Friedlingstein, P.; Sitch, S.; Ahlström, A.; Guimberteau, M.; Huntingford, C.; Levis, S.; Levy, P. E.; Huang, M.; Li, Y.; Li, X.; Lomas, M.; Peylin, P. P.; Poulter, B.; Viovy, N.; Zaehle, S.; Zeng, N.; Zhao, F.; Wang, L.

2015-12-01

In this study, we assessed the performance of discharge simulations by coupling the runoff from seven Dynamic Global Vegetation Models (DGVMs; LPJ, ORCHIDEE, Sheffield-DGVM, TRIFFID, LPJ-GUESS, CLM4CN, and OCN) to one river routing model for 16 large river basins. The results show that the seasonal cycle of river discharge is generally modelled well in the low and mid latitudes, but not in the high latitudes, where the peak discharge (due to snow and ice melting) is underestimated. For the annual mean discharge, the DGVMs chained with the routing model show an underestimation. Furthermore the 30-year trend of discharge is also under-estimated. For the inter-annual variability of discharge, a skill score based on overlapping of probability density functions (PDFs) suggests that most models correctly reproduce the observed variability (correlation coefficient higher than 0.5; i.e. models account for 50% of observed inter-annual variability) except for the Lena, Yenisei, Yukon, and the Congo river basins. In addition, we compared the simulated runoff from different simulations where models were forced with either fixed or varying land use. This suggests that both seasonal and annual mean runoff has been little affected by land use change, but that the trend itself of runoff is sensitive to land use change. None of the models when considered individually show significantly better performances than any other and in all basins. This suggests that based on current modelling capability, a regional-weighted average of multi-model ensemble projections might be appropriate to reduce the bias in future projection of global river discharge.

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

GLEAM v3: updated land evaporation and root-zone soil moisture datasets

NASA Astrophysics Data System (ADS)

Martens, Brecht; Miralles, Diego; Lievens, Hans; van der Schalie, Robin; de Jeu, Richard; Fernández-Prieto, Diego; Verhoest, Niko

2016-04-01

Evaporation determines the availability of surface water resources and the requirements for irrigation. In addition, through its impacts on the water, carbon and energy budgets, evaporation influences the occurrence of rainfall and the dynamics of air temperature. Therefore, reliable estimates of this flux at regional to global scales are of major importance for water management and meteorological forecasting of extreme events. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to the limited global coverage of in situ measurements. Remote sensing techniques can help to overcome the lack of ground data. However, evaporation is not directly observable from satellite systems. As a result, recent efforts have focussed on combining the observable drivers of evaporation within process-based models. The Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu) estimates terrestrial evaporation based on daily satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics and soil moisture. Since the publication of the first version of the model in 2011, GLEAM has been widely applied for the study of trends in the water cycle, interactions between land and atmosphere and hydrometeorological extreme events. A third version of the GLEAM global datasets will be available from the beginning of 2016 and will be distributed using www.gleam.eu as gateway. The updated datasets include separate estimates for the different components of the evaporative flux (i.e. transpiration, bare-soil evaporation, interception loss, open-water evaporation and snow sublimation), as well as variables like the evaporative stress, potential evaporation, root-zone soil moisture and surface soil moisture. A new dataset using SMOS-based input data of surface soil moisture and vegetation optical depth will also be distributed. The most important updates in GLEAM include the revision of the soil moisture data assimilation system, the evaporative stress functions and the infiltration of rainfall. In this presentation, we will highlight the changes of the methodology and present the new datasets, their validation against in situ observations and the comparisons against alternative datasets of terrestrial evaporation, such as GLDAS-Noah, ERA-Interim and previous GLEAM datasets. Preliminary results indicate that the magnitude and the spatio-temporal variability of the evaporation estimates have been slightly improved upon previous versions of the datasets.

A global dataset of crowdsourced land cover and land use reference data.

PubMed

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-06-13

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

A global dataset of crowdsourced land cover and land use reference data

PubMed Central

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F.; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-01-01

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general. PMID:28608851

Use of Climatic Information In Regional Water Resources Assessment

NASA Astrophysics Data System (ADS)

Claps, P.

Relations between climatic parameters and hydrological variables at the basin scale are investigated, with the aim of evaluating in a parsimonious way physical parameters useful both for a climatic classification of an area and for supporting statistical models of water resources assessment. With reference to the first point, literature methods for distributed evaluation of parameters such as temperature, global and net solar radiation, precipitation, have been considered at the annual scale with the aim of considering the viewpoint of the robust evaluation of parameters based on few basic physical variables of simple determination. Elevation, latitude and average annual number of sunny days have demonstrated to be the essential parameters with respect to the evaluation of climatic indices related to the soil water deficit and to the radiative balance. The latter term was evaluated at the monthly scale and validated (in the `global' term) with measured data. in questo caso riferite al bilancio idrico a scala annuale. Budyko, Thornthwaite and Emberger climatic indices were evaluated on the 10,000 km2 territory of the Basilicata region (southern Italy) based on a 1.1. km grid. They were compared in terms of spatial variability and sensitivity to the variation of the basic variables in humid and semi-arid areas. The use of the climatic index data with respect to statistical parameters of the runoff series in some gauging stations of the region demonstrated the possibility to support regionalisation of the annual runoff using climatic information, with clear distinction of the variability of the coefficient of variation in terms of the humidity-aridity of the basin.

Study of Tropospheric Ozone and UV Reflectivity Using TOMS Data

NASA Technical Reports Server (NTRS)

Yung, Yuk L.

2002-01-01

Perhaps the single most important result from the study of Chuang and Yung is that the interannual variability of the Earth's albedo (especially in Spring) on land is dominated by snow/ice, and not by clouds. This interannual variability could be the major driver of changes in the atmosphere and the biosphere. It is plausible that the interannual variability of snow/ice, through interactions with the atmosphere and biosphere, is responsible for the interannual variability of atmospheric CO2. By carefully studying the albedo variations off the Peru coast, we found evidence for indirect aerosol effect on clouds. Based on a detailed analysis of the cloud data obtained by the International Satellite Cloud Climatology Project (SCCP) in the years 1983-1991, we show that besides the reported 3 % variation in global cloudiness, the global mean cloud optical thickness (MCOT) also has significant variation which is out of phase with that of the global cloudiness. The combined effect of the two opposing variations may be a null effect on the cloud reflectivity. These results are consistent with the Total Ozone Mapping Spectrometer (TOMS) reflectively measurements. The MCOT variation is further shown to be correlated with both the solar cycle and the ENSO (El Nino Southern Oscillation) cycle. Our present analysis cannot distinguish which of the above two provides better correlation, although independent data from the High resolution Infrared Radiation Sounder (HIRS) from 1990 to 1996 favor the solar cycle. Future data are needed to identify the true cause of these changes.

Global Variability and Changes in Ocean Total Alkalinity from Aquarius Satellite

NASA Astrophysics Data System (ADS)

Fine, R. A.; Willey, D. A.; Millero, F. J., Jr.

2016-02-01

To document effects of ocean acidification it is important to have an understanding of the processes and parameters that influence alkalinity. Alkalinity is a gauge on the ability of seawater to neutralize acids. We use Aquarius satellite data, which allow unprecedented global mapping of surface total alkalinity as it correlates strongly with salinity and to a lesser extent with temperature. Spatial variability in total alkalinity and salinity exceed temporal variability, the latter includes seasonal and differences compared to climatological data. The northern hemisphere has more spatial and monthly variability in total alkalinity and salinity, while less variability in Southern Ocean alkalinity is due to less salinity variability and upwelling of waters enriched in alkalinity. Satellite alkalinity data are providing a global baseline that can be used for comparing with future carbon data, and for evaluating spatial and temporal variability and past trends. For the first time it is shown that recent satellite derived total alkalinity in the subtropics have increased as compared with climatological data; this is reflective of large scale changes in the global water cycle. Total alkalinity increases imply increased dissolution of calcareous minerals and difficulty for calcifying organisms to make their shells.

Information Competency and Creative Initiative of Personality and Their Manifestation in Activity

ERIC Educational Resources Information Center

Tabachuk, Natalia P.; Ledovskikh, Irina A.; Shulika, Nadezhda A.; Karpova, Irina V.; Kazinets, Victor A.; Polichka, Anatolii E.

2018-01-01

The relevance of the research is due to the global trends of development of the information society that are associated with the rapid advancement of civilization (IT penetration, increased computer availability, variability) and innovation processes in the sphere of education (competency-based approach, humanization and humanitarization). These…

Joint variability of global runoff and global sea surface temperatures

USGS Publications Warehouse

McCabe, G.J.; Wolock, D.M.

2008-01-01

Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905-2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Ni??o-Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Aflantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.

QUEST1 Variability Survey. II. Variability Determination Criteria and 200k Light Curve Catalog

NASA Astrophysics Data System (ADS)

Rengstorf, A. W.; Mufson, S. L.; Andrews, P.; Honeycutt, R. K.; Vivas, A. K.; Abad, C.; Adams, B.; Bailyn, C.; Baltay, C.; Bongiovanni, A.; Briceño, C.; Bruzual, G.; Coppi, P.; Della Prugna, F.; Emmet, W.; Ferrín, I.; Fuenmayor, F.; Gebhard, M.; Hernández, J.; Magris, G.; Musser, J.; Naranjo, O.; Oemler, A.; Rosenzweig, P.; Sabbey, C. N.; Sánchez, Ge.; Sánchez, Gu.; Schaefer, B.; Schenner, H.; Sinnott, J.; Snyder, J. A.; Sofia, S.; Stock, J.; van Altena, W.

2004-12-01

The QUEST (QUasar Equatorial Survey Team) Phase 1 camera has collected multibandpass photometry on a large strip of high Galactic latitude sky over a period of 26 months. This robust data set has been reduced and nightly catalogs compared to determine the photometric variability of the ensemble objects. Subsequent spectroscopic observations have confirmed a subset of the photometric variables as quasars, as previously reported. This paper reports on the details of the data reduction and analysis pipeline and presents multiple bandpass light curves for 198,213 QUEST1 objects, along with global variability information and matched Sloan photometry. Based on observations obtained at the Llano del Hato National Astronomical Observatory, operated by the Centro de Investigaciones de Astronomía for the Ministerio de Ciencia y Tecnologia of Venezuela.

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 local and global climate forcings induced inhomogeneity of Indian rainfall.

PubMed

Nair, P J; Chakraborty, A; Varikoden, H; Francis, P A; Kuttippurath, J

2018-04-16

India is home for more than a billion people and its economy is largely based on agrarian society. Therefore, rainfall received not only decides its livelihood, but also influences its water security and economy. This situation warrants continuous surveillance and analysis of Indian rainfall. These kinds of studies would also help forecasters to better tune their models for accurate weather prediction. Here, we introduce a new method for estimating variability and trends in rainfall over different climate regions of India. The method based on multiple linear regression helps to assess contributions of different remote and local climate forcings to seasonal and regional inhomogeneity in rainfall. We show that the Indian Summer Monsoon Rainfall (ISMR) variability is governed by Eastern and Central Pacific El Niño Southern Oscillation, equatorial zonal winds, Atlantic zonal mode and surface temperatures of the Arabian Sea and Bay of Bengal, and the North East Monsoon Rainfall variability is controlled by the sea surface temperature of the North Atlantic and extratropial oceans. Also, our analyses reveal significant positive trends (0.43 mm/day/dec) in the North West for ISMR in the 1979-2017 period. This study cautions against the significant changes in Indian rainfall in a perspective of global climate change.

Rainfall extremes from TRMM data and the Metastatistical Extreme Value Distribution

NASA Astrophysics Data System (ADS)

Zorzetto, Enrico; Marani, Marco

2017-04-01

A reliable quantification of the probability of weather extremes occurrence is essential for designing resilient water infrastructures and hazard mitigation measures. However, it is increasingly clear that the presence of inter-annual climatic fluctuations determines a substantial long-term variability in the frequency of occurrence of extreme events. This circumstance questions the foundation of the traditional extreme value theory, hinged on stationary Poisson processes or on asymptotic assumptions to derive the Generalized Extreme Value (GEV) distribution. We illustrate here, with application to daily rainfall, a new approach to extreme value analysis, the Metastatistical Extreme Value Distribution (MEVD). The MEVD relaxes the above assumptions and is based on the whole distribution of daily rainfall events, thus allowing optimal use of all available observations. Using a global dataset of rain gauge observations, we show that the MEVD significantly outperforms the Generalized Extreme Value distribution, particularly for long average recurrence intervals and when small samples are available. The latter property suggests MEVD to be particularly suited for applications to satellite rainfall estimates, which only cover two decades, thus making extreme value estimation extremely challenging. Here we apply MEVD to the TRMM TMPA 3B42 product, an 18-year dataset of remotely-sensed daily rainfall providing a quasi-global coverage. Our analyses yield a global scale mapping of daily rainfall extremes and of their distributional tail properties, bridging the existing large gaps in ground-based networks. Finally, we illustrate how our global-scale analysis can provide insight into how properties of local rainfall regimes affect tail estimation uncertainty when using the GEV or MEVD approach. We find a dependence of the estimation uncertainty, for both the GEV- and MEV-based approaches, on the average annual number and on the inter-annual variability of rainy days. In particular, estimation uncertainty decreases 1) as the mean annual number of wet days increases, and 2) as the variability in the number of rainy days, expressed by its coefficient of variation, decreases. We tentatively explain this behavior in terms of the assumptions underlying the two approaches.

System analysis to estimate subsurface flow: from global level to the State of Minnesota

NASA Astrophysics Data System (ADS)

Shmagin, Boris A.; Kanivetsky, Roman

2002-06-01

Stream runoff data globally and in the state of Minnesota were used to estimate subsurface water flow. This system approach is based, in principal, on unity of groundwater and surface water systems, and it is in stark contrast to the traditional deterministic approach based on modeling. In coordination with methodology of system analysis, two levels of study were used to estimate subsurface flow. First, the global stream runoff data were assessed to estimate the temporal-spatial variability of surface water runoff. Factor analysis was used to study the temporal-spatial variability of global runoff for the period from 1918 to 1967. Results of these analysis demonstrate that the variability of global runoff could be represented by seven major components (factor scores) that could be grouped into seven distinct independent grouping from the total of 18 continental slopes on the Earth. Computed variance value in this analysis is 76% and supports such analysis. The global stream runoff for this period is stationary, and is more closely connected with the stream flow of Asia to the Pacific Ocean as well as with the stream runoff of North America towards the Arctic and Pacific Oceans. The second level examines the distribution of river runoff (annual and for February) for various landscapes and the hydrogeological conditions in the State of Minnesota (218,000 km2). The annual and minimal monthly rate of stream runoff for 115 gauging stations with a period of observation of 47 years (1935-1981) were used to characterize the spatio-temporal distribution of stream runoff in Minnesota. Results of this analysis demonstrate that the annual stream runoff rate changes from 6.3, towards 3.95, and then to 2.09 l s-1 km-2 (the difference is significant based on Student's criteria). These values in Minnesota correspond to ecological provinces from a mixed forest province towards the broadleaf forest and to prairie province, respectively. The distribution of minimal monthly stream runoff rate (February runoff) is controlled by hydrogeological systems in Minnesota. The difference between the two hydrogeological regions, Precambrian crystalline basement and Paleozoic artesian basin of 0.83 and 2.09 l/s/km2, is statistically significant. Within these regions, the monthly minimal runoff (0.5 and 1.68, and 0.87 and 3.11 l s-1 km-2 for February, respectively) is also distinctly different for delineated subregions, depending on whether or not the Quaternary cover is present. The spatio-temporal structure that emerges could thus be used to generate river runoff and subsurface flow maps at any scale - from the global level to local detail. Such analysis was carried out in Minnesota with the detailed mapping of the subsurface flow for the Twin Cities Metropolitan area.

System analysis to estimate subsurface flow: From global level to the State of Minnesota

USGS Publications Warehouse

Shmagin, B.A.; Kanivetsky, R.

2002-01-01

Stream runoff data globally and in the state of Minnesota were used to estimate subsurface water flow. This system approach is based, in principal, on unity of groundwater and surface water systems, and it is in stark contrast to the traditional deterministic approach based on modeling. In coordination with methodology of system analysis, two levels of study were used to estimate subsurface flow. First, the global stream runoff data were assessed to estimate the temporal-spatial variability of surface water runoff. Factor analysis was used to study the temporal-spatial variability of global runoff for the period from 1918 to 1967. Results of these analysis demonstrate that the variability of global runoff could be represented by seven major components (factor scores) that could be grouped into seven distinct independent grouping from the total of 18 continental slopes on the Earth. Computed variance value in this analysis is 76% and supports such analysis. The global stream runoff for this period is stationary, and is more closely connected with the stream flow of Asia to the Pacific Ocean as well as with the stream runoff of North America towards the Arctic and Pacific Oceans. The second level examines the distribution of river runoff (annual and for February) for various landscapes and the hydrogeological conditions in the State of Minnesota (218,000 km2). The annual and minimal monthly rate of stream runoff for 115 gauging stations with a period of observation of 47 years (1935-1981) were used to characterize the spatio-temporal distribution of stream runoff in Minnesota. Results of this analysis demonstrate that the annual stream runoff rate changes from 6.3, towards 3.95, and then to 2.09 1 s-1 km-2 (the difference is significant based on Student's criteria). These values in Minnesota correspond to ecological provinces from a mixed forest province towards the broadleaf forest and to prairie province, respectively. The distribution of minimal monthly stream runoff rate (February runoff) is controlled by hydrogeological systems in Minnesota. The difference between the two hydrogeological regions, Precambrian crystalline basement and Paleozoic artesian basin of 0.83 and 2.09 1/s/km2, is statistically significant. Within these regions, the monthly minimal runoff (0.5 and 1.68, and 0.87 and 3.11 1 s-1 km-2 for February, respectively) is also distinctly different for delineated subregions, depending on whether or not the Quaternary cover is present. The spatio-temporal structure that emerges could thus be used to generate river runoff and subsurface flow maps at any scale - from the global level to local detail. Such analysis was carried out in Minnesota with the detailed mapping of the subsurface flow for the Twin Cities Metropolitan area.

Quantitative assessment of drivers of recent global temperature variability: an information theoretic approach

NASA Astrophysics Data System (ADS)

Bhaskar, Ankush; Ramesh, Durbha Sai; Vichare, Geeta; Koganti, Triven; Gurubaran, S.

2017-12-01

Identification and quantification of possible drivers of recent global temperature variability remains a challenging task. This important issue is addressed adopting a non-parametric information theory technique, the Transfer Entropy and its normalized variant. It distinctly quantifies actual information exchanged along with the directional flow of information between any two variables with no bearing on their common history or inputs, unlike correlation, mutual information etc. Measurements of greenhouse gases: CO2, CH4 and N2O; volcanic aerosols; solar activity: UV radiation, total solar irradiance ( TSI) and cosmic ray flux ( CR); El Niño Southern Oscillation ( ENSO) and Global Mean Temperature Anomaly ( GMTA) made during 1984-2005 are utilized to distinguish driving and responding signals of global temperature variability. Estimates of their relative contributions reveal that CO2 ({˜ } 24 %), CH4 ({˜ } 19 %) and volcanic aerosols ({˜ }23 %) are the primary contributors to the observed variations in GMTA. While, UV ({˜ } 9 %) and ENSO ({˜ } 12 %) act as secondary drivers of variations in the GMTA, the remaining play a marginal role in the observed recent global temperature variability. Interestingly, ENSO and GMTA mutually drive each other at varied time lags. This study assists future modelling efforts in climate science.

Analysis of trends and dominant periodicities in drought variables in India: A wavelet transform based approach

NASA Astrophysics Data System (ADS)

Joshi, Nitin; Gupta, Divya; Suryavanshi, Shakti; Adamowski, Jan; Madramootoo, Chandra A.

2016-12-01

In this study, seasonal trends as well as dominant and significant periods of variability of drought variables were analyzed for 30 rainfall subdivisions in India over 141 years (1871-2012). Standardized precipitation index (SPI) was used as a meteorological drought indicator, and various drought variables (monsoon SPI, non-monsoon SPI, yearly SPI, annual drought duration, annual drought severity and annual drought peak) were analyzed. Discrete wavelet transform was used in conjunction with the Mann-Kendall test to analyze trends and dominant periodicities associated with the drought variables. Furthermore, continuous wavelet transform (CWT) based global wavelet spectrum was used to analyze significant periods of variability associated with the drought variables. From the trend analysis, we observed that over the second half of the 20th century, drought occurrences increased significantly in subdivisions of Northeast and Central India. In both short-term (2-8 years) and decadal (16-32 years) periodicities, the drought variables were found to influence the trend. However, CWT analysis indicated that the dominant periodic components were not significant for most of the geographical subdivisions. Although inter-annual and inter-decadal periodic components play an important role, they may not completely explain the variability associated with the drought variables across the country.

Analysis of the trade-off between high crop yield and low yield instability at the global scale

NASA Astrophysics Data System (ADS)

Ben-Ari, Tamara; Makowski, David

2016-10-01

Yield dynamics of major crops species vary remarkably among continents. Worldwide distribution of cropland influences both the expected levels and the interannual variability of global yields. An expansion of cultivated land in the most productive areas could theoretically increase global production, but also increase global yield instability if the most productive regions are characterized by high interannual yield variability. In this letter, we use portfolio analysis to quantify the tradeoff between the expected values and the interannual variance of global yield. We compute optimal frontiers for four crop species i.e., maize, rice, soybean and wheat and show how the distribution of cropland among large world regions can be optimized to either increase expected global crop production or decrease its interannual variability. We also show that a preferential allocation of cropland in the most productive regions can increase global expected yield at the expense of yield stability. Theoretically, optimizing the distribution of a small fraction of total cultivated areas can help find a good compromise between low instability and high crop yields at the global scale.

Global dynamics of oscillator populations under common noise

NASA Astrophysics Data System (ADS)

Braun, W.; Pikovsky, A.; Matias, M. A.; Colet, P.

2012-07-01

Common noise acting on a population of identical oscillators can synchronize them. We develop a description of this process which is not limited to the states close to synchrony, but provides a global picture of the evolution of the ensembles. The theory is based on the Watanabe-Strogatz transformation, allowing us to obtain closed stochastic equations for the global variables. We show that at the initial stage, the order parameter grows linearly in time, while at the later stages the convergence to synchrony is exponentially fast. Furthermore, we extend the theory to nonidentical ensembles with the Lorentzian distribution of natural frequencies and determine the stationary values of the order parameter in dependence on driving noise and mismatch.

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar

2016-01-01

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Assessing the influence of watershed characteristics on chlorophyll a in waterbodies at global and regional scales

USGS Publications Warehouse

Woelmer, Whitney; Kao, Yu-Chun; Bunnell, David B.; Deines, Andrew M.; Bennion, David; Rogers, Mark W.; Brooks, Colin N.; Sayers, Michael J.; Banach, David M.; Grimm, Amanda G.; Shuchman, Robert A.

2016-01-01

Prediction of primary production of lentic water bodies (i.e., lakes and reservoirs) is valuable to researchers and resource managers alike, but is very rarely done at the global scale. With the development of remote sensing technologies, it is now feasible to gather large amounts of data across the world, including understudied and remote regions. To determine which factors were most important in explaining the variation of chlorophyll a (Chl-a), an indicator of primary production in water bodies, at global and regional scales, we first developed a geospatial database of 227 water bodies and watersheds with corresponding Chl-a, nutrient, hydrogeomorphic, and climate data. Then we used a generalized additive modeling approach and developed model selection criteria to select models that most parsimoniously related Chl-a to predictor variables for all 227 water bodies and for 51 lakes in the Laurentian Great Lakes region in the data set. Our best global model contained two hydrogeomorphic variables (water body surface area and the ratio of watershed to water body surface area) and a climate variable (average temperature in the warmest model selection criteria to select models that most parsimoniously related Chl-a to predictor variables quarter) and explained ~ 30% of variation in Chl-a. Our regional model contained one hydrogeomorphic variable (flow accumulation) and the same climate variable, but explained substantially more variation (58%). Our results indicate that a regional approach to watershed modeling may be more informative to predicting Chl-a, and that nearly a third of global variability in Chl-a may be explained using hydrogeomorphic and climate variables.

Electronic neural network for solving traveling salesman and similar global optimization problems

NASA Technical Reports Server (NTRS)

Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)

1993-01-01

This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.

Unveiling Mars nightside mesosphere dynamics by IUVS/MAVEN global images of NO nightglow

NASA Astrophysics Data System (ADS)

Stiepen, A.; Jain, S. K.; Schneider, N. M.; Milby, Z.; Deighan, J. I.; Gonzàlez-Galindo, F.; Gérard, J.-C.; Forget, F.; Bougher, S.; Stewart, A. I. F.; Royer, E.; Stevens, M. H.; Evans, J. S.; Chaffin, M. S.; Crismani, M.; McClintock, W. E.; Clarke, J. T.; Holsclaw, G. W.; Montmessin, F.; Lo, D. Y.

2017-09-01

We analyze the morphology of the ultraviolet nightglow in the Martian upper atmosphere through Nitric Oxide (NO) δ and γ bands emissions observed by the Imaging Ultraviolet Spectrograph instrument on the Mars Atmosphere and Volatile EvolutioN spacecraft. The seasonal dynamics of the Martian thermosphere-mesosphere can be constrained based on the distribution of these emissions. We show evidence for local (emission streaks and splotches) and global (longitudinal and seasonal) variability in brightness of the emission and provide quantitative comparisons to GCM simulations.

Abbreviation as a Reflection of Terms Variability in Language for Specific Purposes: Translational Features (Terminology Case Study in German, English, Kazakh, and Russia)

ERIC Educational Resources Information Center

Beisembayeva, Gulshat Z.; Yeskindirova, Manshuk Z.; Tulebayeva, Samal A.

2016-01-01

The range of modern dynamic social changes, globalization of world powers' economic cooperation, acceleration of technocratic processes have widespread impact on term systems' variability in language, in particular, on terminological variability for specific purposes. This globalized extra-linguistic factor provokes avalanche growth of…

Global Sensitivity Analysis as Good Modelling Practices tool for the identification of the most influential process parameters of the primary drying step during freeze-drying.

PubMed

Van Bockstal, Pieter-Jan; Mortier, Séverine Thérèse F C; Corver, Jos; Nopens, Ingmar; Gernaey, Krist V; De Beer, Thomas

2018-02-01

Pharmaceutical batch freeze-drying is commonly used to improve the stability of biological therapeutics. The primary drying step is regulated by the dynamic settings of the adaptable process variables, shelf temperature T s and chamber pressure P c . Mechanistic modelling of the primary drying step leads to the optimal dynamic combination of these adaptable process variables in function of time. According to Good Modelling Practices, a Global Sensitivity Analysis (GSA) is essential for appropriate model building. In this study, both a regression-based and variance-based GSA were conducted on a validated mechanistic primary drying model to estimate the impact of several model input parameters on two output variables, the product temperature at the sublimation front T i and the sublimation rate ṁ sub . T s was identified as most influential parameter on both T i and ṁ sub , followed by P c and the dried product mass transfer resistance α Rp for T i and ṁ sub , respectively. The GSA findings were experimentally validated for ṁ sub via a Design of Experiments (DoE) approach. The results indicated that GSA is a very useful tool for the evaluation of the impact of different process variables on the model outcome, leading to essential process knowledge, without the need for time-consuming experiments (e.g., DoE). Copyright © 2017 Elsevier B.V. All rights reserved.

An étude on global vacuum energy sequester

DOE PAGES

D’Amico, Guido; Kaloper, Nemanja; Padilla, Antonio; ...

2017-09-18

Recently two of the authors proposed a mechanism of vacuum energy sequester as a means of protecting the observable cosmological constant from quantum radiative corrections. The original proposal was based on using global Lagrange multipliers, but later a local formulation was provided. Subsequently other interesting claims of a different non-local approach to the cosmological constant problem were made, based again on global Lagrange multipliers. We examine some of these proposals and find their mutual relationship. We explain that the proposals which do not treat the cosmological constant counterterm as a dynamical variable require fine tunings to have acceptable solutions. Furthermore,more » the counterterm often needs to be retuned at every order in the loop expansion to cancel the radiative corrections to the cosmological constant, just like in standard GR. These observations are an important reminder of just how the proposal of vacuum energy sequester avoids such problems.« less

Global energetics and local physics as drivers of past, present and future monsoons

NASA Astrophysics Data System (ADS)

Biasutti, Michela; Voigt, Aiko; Boos, William R.; Braconnot, Pascale; Hargreaves, Julia C.; Harrison, Sandy P.; Kang, Sarah M.; Mapes, Brian E.; Scheff, Jacob; Schumacher, Courtney; Sobel, Adam H.; Xie, Shang-Ping

2018-06-01

Global constraints on momentum and energy govern the variability of the rainfall belt in the intertropical convergence zone and the structure of the zonal mean tropical circulation. The continental-scale monsoon systems are also facets of a momentum- and energy-constrained global circulation, but their modern and palaeo variability deviates substantially from that of the intertropical convergence zone. The mechanisms underlying deviations from expectations based on the longitudinal mean budgets are neither fully understood nor simulated accurately. We argue that a framework grounded in global constraints on energy and momentum yet encompassing the complexities of monsoon dynamics is needed to identify the causes of the mismatch between theory, models and observations, and ultimately to improve regional climate projections. In a first step towards this goal, disparate regional processes must be distilled into gross measures of energy flow in and out of continents and between the surface and the tropopause, so that monsoon dynamics may be coherently diagnosed across modern and palaeo observations and across idealized and comprehensive simulations. Accounting for zonal asymmetries in the circulation, land/ocean differences in surface fluxes, and the character of convective systems, such a monsoon framework would integrate our understanding at all relevant scales: from the fine details of how moisture and energy are lifted in the updrafts of thunderclouds, up to the global circulations.

Time variable eddy mixing in the global Sea Surface Salinity maxima

NASA Astrophysics Data System (ADS)

Busecke, J. J. M.; Abernathey, R.; Gordon, A. L.

2016-12-01

Lateral mixing by mesoscale eddies is widely recognized as a crucial mechanism for the global ocean circulation and the associated heat/salt/tracer transports. The Salinity in the Upper Ocean Processes Study (SPURS) confirmed the importance of eddy mixing for the surface salinity fields even in the center of the subtropical gyre of the North Atlantic. We focus on the global salinity maxima due to their role as indicators for global changes in the hydrological cycle as well as providing the source water masses for the shallow overturning circulation. We introduce a novel approach to estimate the contribution of eddy mixing to the global sea surface salinity maxima. Using a global 2D tracer experiments in a 1/10 degree MITgcm setup driven by observed surface velocities, we analyze the effect of eddy mixing using a water mass framework, thus focussing on the diffusive flux across surface isohalines. This enables us to diagnose temporal variability on seasonal to inter annual time scales, revealing regional differences in the mechanism causing temporal variability.Sensitivity experiments with various salinity backgrounds reveal robust inter annual variability caused by changes in the surface velocity fields potentially forced by large scale climate.

Global linkages between teleconnection patterns and the terrestrial biosphere

NASA Astrophysics Data System (ADS)

Dahlin, Kyla M.; Ault, Toby R.

2018-07-01

Interannual variability in the global carbon cycle is largely due to variations in carbon uptake by terrestrial ecosystems, yet linkages between climate variability and variability in the terrestrial carbon cycle are not well understood at the global scale. Using a 30-year satellite record of semi-monthly leaf area index (LAI), we show that four modes of climate variability - El Niño/Southern Oscillation, the North Atlantic Oscillation, the Atlantic Meridional Mode, and the Indian Ocean Dipole Mode - strongly impact interannual vegetation growth patterns, with 68% of the land surface impacted by at least one of these teleconnection patterns, yet the spatial distribution of these impacts is heterogeneous. Considering the patterns' impacts by biome, none has an exclusively positive or negative relationship with LAI. Our findings imply that future changes in the frequency and/or magnitude of teleconnection patterns will lead to diverse changes to the terrestrial biosphere and the global carbon cycle.

Effects of climate variability on global scale flood risk

NASA Astrophysics Data System (ADS)

Ward, P.; Dettinger, M. D.; Kummu, M.; Jongman, B.; Sperna Weiland, F.; Winsemius, H.

2013-12-01

In this contribution we demonstrate the influence of climate variability on flood risk. Globally, flooding is one of the worst natural hazards in terms of economic damages; Munich Re estimates global losses in the last decade to be in excess of $240 billion. As a result, scientifically sound estimates of flood risk at the largest scales are increasingly needed by industry (including multinational companies and the insurance industry) and policy communities. Several assessments of global scale flood risk under current and conditions have recently become available, and this year has seen the first studies assessing how flood risk may change in the future due to global change. However, the influence of climate variability on flood risk has as yet hardly been studied, despite the fact that: (a) in other fields (drought, hurricane damage, food production) this variability is as important for policy and practice as long term change; and (b) climate variability has a strong influence in peak riverflows around the world. To address this issue, this contribution illustrates the influence of ENSO-driven climate variability on flood risk, at both the globally aggregated scale and the scale of countries and large river basins. Although it exerts significant and widespread influences on flood peak discharges in many parts of the world, we show that ENSO does not have a statistically significant influence on flood risk once aggregated to global totals. At the scale of individual countries, though, strong relationships exist over large parts of the Earth's surface. For example, we find particularly strong anomalies of flood risk in El Niño or La Niña years (compared to all years) in southern Africa, parts of western Africa, Australia, parts of Central Eurasia (especially for El Niño), the western USA (especially for La Niña), and parts of South America. These findings have large implications for both decadal climate-risk projections and long-term future climate change research. We carried out the research by simulating daily river discharge using a global hydrological model (PCR-GLOBWB), forced with gridded climate reanalysis time-series. From this, we derived peak annual flood volumes for large-scale river basins globally. These were used to force a global inundation model (dynRout) to map inundation extent and depth for return periods between 2 and 1000 years, under El Niño conditions, neutral conditions, and La Niña conditions. Theses flood hazard maps were combined with global datasets on socioeconomic variables such as population and income to represent the socioeconomic exposure to flooding, and depth-damage curves to represent vulnerability.

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.

Understanding Coupling of Global and Diffuse Solar Radiation with Climatic Variability

NASA Astrophysics Data System (ADS)

Hamdan, Lubna

Global solar radiation data is very important for wide variety of applications and scientific studies. However, this data is not readily available because of the cost of measuring equipment and the tedious maintenance and calibration requirements. Wide variety of models have been introduced by researchers to estimate and/or predict the global solar radiations and its components (direct and diffuse radiation) using other readily obtainable atmospheric parameters. The goal of this research is to understand the coupling of global and diffuse solar radiation with climatic variability, by investigating the relationships between these radiations and atmospheric parameters. For this purpose, we applied multilinear regression analysis on the data of National Solar Radiation Database 1991--2010 Update. The analysis showed that the main atmospheric parameters that affect the amount of global radiation received on earth's surface are cloud cover and relative humidity. Global radiation correlates negatively with both variables. Linear models are excellent approximations for the relationship between atmospheric parameters and global radiation. A linear model with the predictors total cloud cover, relative humidity, and extraterrestrial radiation is able to explain around 98% of the variability in global radiation. For diffuse radiation, the analysis showed that the main atmospheric parameters that affect the amount received on earth's surface are cloud cover and aerosol optical depth. Diffuse radiation correlates positively with both variables. Linear models are very good approximations for the relationship between atmospheric parameters and diffuse radiation. A linear model with the predictors total cloud cover, aerosol optical depth, and extraterrestrial radiation is able to explain around 91% of the variability in diffuse radiation. Prediction analysis showed that the linear models we fitted were able to predict diffuse radiation with efficiency of test adjusted R2 values equal to 0.93, using the data of total cloud cover, aerosol optical depth, relative humidity and extraterrestrial radiation. However, for prediction purposes, using nonlinear terms or nonlinear models might enhance the prediction of diffuse radiation.

Temperature-driven global sea-level variability in the Common Era.

PubMed

Kopp, Robert E; Kemp, Andrew C; Bittermann, Klaus; Horton, Benjamin P; Donnelly, Jeffrey P; Gehrels, W Roland; Hay, Carling C; Mitrovica, Jerry X; Morrow, Eric D; Rahmstorf, Stefan

2016-03-15

We assess the relationship between temperature and global sea-level (GSL) variability over the Common Era through a statistical metaanalysis of proxy relative sea-level reconstructions and tide-gauge data. GSL rose at 0.1 ± 0.1 mm/y (2σ) over 0-700 CE. A GSL fall of 0.2 ± 0.2 mm/y over 1000-1400 CE is associated with ∼ 0.2 °C global mean cooling. A significant GSL acceleration began in the 19th century and yielded a 20th century rise that is extremely likely (probability [Formula: see text]) faster than during any of the previous 27 centuries. A semiempirical model calibrated against the GSL reconstruction indicates that, in the absence of anthropogenic climate change, it is extremely likely ([Formula: see text]) that 20th century GSL would have risen by less than 51% of the observed [Formula: see text] cm. The new semiempirical model largely reconciles previous differences between semiempirical 21st century GSL projections and the process model-based projections summarized in the Intergovernmental Panel on Climate Change's Fifth Assessment Report.

Global Precipitation Products at NASA GES DISC for Supporting Agriculture Research and Applications

NASA Technical Reports Server (NTRS)

Liu, Zhong; Teng, W.; Ostrenga, D.; Albayrak, R.; Savtchenko, A.; Yang, W.; Vollmer, B.; Meyer, D.

2017-01-01

This presentation describes precipitation products available at the NASA GES DISC that support agricultural research. XXXX Key environmental variables for agriculture: precipitation, temperature, water (soil moisture), solar radiation, NDVI, etc. Rainfed agriculture - major farming practices that rely on rainfall for water. Rainfed agriculture: >95% of farmed land (sub- Saharan Africa); 90% (Latin America); 75% (Near East and North Africa); 65% (East Asia); 60% (South Asia). Precipitation is very important for rainfed agriculture. Droughts can cause severe damage. Precipitation information can be used to monitor the growing season. The Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA data centers, located in Greenbelt, Maryland, USA. The GES DISC is a major data archive center for global precipitation, water & energy cycles, atmospheric composition, and climate variability Global and regional precipitation datasets (satellite-based and data assimilation Data services (subsetting, format conversion, online visualization, etc.) User services are available FAQs, How to (recipes), Glossary, etc. Social media (Twitter, YouTube, User forum) Help desk (phone, email, online feedback) Training materials (ARSET => Applied Remote Sensing Training) Liu,

Temperature-driven global sea-level variability in the Common Era

PubMed Central

Kopp, Robert E.; Kemp, Andrew C.; Bittermann, Klaus; Horton, Benjamin P.; Donnelly, Jeffrey P.; Gehrels, W. Roland; Hay, Carling C.; Mitrovica, Jerry X.; Morrow, Eric D.; Rahmstorf, Stefan

2016-01-01

We assess the relationship between temperature and global sea-level (GSL) variability over the Common Era through a statistical metaanalysis of proxy relative sea-level reconstructions and tide-gauge data. GSL rose at 0.1 ± 0.1 mm/y (2σ) over 0–700 CE. A GSL fall of 0.2 ± 0.2 mm/y over 1000–1400 CE is associated with ∼0.2 °C global mean cooling. A significant GSL acceleration began in the 19th century and yielded a 20th century rise that is extremely likely (probability P≥0.95) faster than during any of the previous 27 centuries. A semiempirical model calibrated against the GSL reconstruction indicates that, in the absence of anthropogenic climate change, it is extremely likely (P=0.95) that 20th century GSL would have risen by less than 51% of the observed 13.8±1.5 cm. The new semiempirical model largely reconciles previous differences between semiempirical 21st century GSL projections and the process model-based projections summarized in the Intergovernmental Panel on Climate Change’s Fifth Assessment Report. PMID:26903659

Use of WONCA global standards to evaluate family medicine postgraduate education for curriculum development and review in Nepal and Myanmar.

PubMed

Gibson, Christine; Ladak, Farah; Shrestha, Ashis; Yadav, Bharat; Thu, Kyaw; Aye, Tin

2016-09-01

Family medicine is an integral part of primary care within health systems. Globally, training programmes exhibit a great degree of variability in content and skill acquisition. While this may in part reflect the needs of a given setting, there exists standard criteria that all family medicine programmes should consider core activities. WONCA has provided an open-access list of standards that their expert community considers essential for family medicine (GP) post-graduate training. Evaluation of developing or existing training programmes using these standards can provide insight into the degree of variability, gaps within programmes and equally as important, gaps within recommendations. In collaboration with the host institution, two family medicine programmes in Nepal and Myanmar were evaluated based on WONCA global standards. The results of the evaluation demonstrated that such a process can allow for critical review of curriculum in various stages of development and evaluation. The implications of reviewing training programmes according to WONCA standards can lead to enhanced training world-wide and standardisation of training for post-graduate family medicine.

Degree of Ice Particle Surface Roughness Inferred from Polarimetric Observations

NASA Technical Reports Server (NTRS)

Hioki, Souichiro; Yang, Ping; Baum, Bryan A.; Platnick, Steven; Meyer, Kerry G.; King, Michael D.; Riedi, Jerome

2016-01-01

The degree of surface roughness of ice particles within thick, cold ice clouds is inferred from multidirectional, multi-spectral satellite polarimetric observations over oceans, assuming a column-aggregate particle habit. An improved roughness inference scheme is employed that provides a more noise-resilient roughness estimate than the conventional best-fit approach. The improvements include the introduction of a quantitative roughness parameter based on empirical orthogonal function analysis and proper treatment of polarization due to atmospheric scattering above clouds. A global 1-month data sample supports the use of a severely roughened ice habit to simulate the polarized reflectivity associated with ice clouds over ocean. The density distribution of the roughness parameter inferred from the global 1- month data sample and further analyses of a few case studies demonstrate the significant variability of ice cloud single-scattering properties. However, the present theoretical results do not agree with observations in the tropics. In the extra-tropics, the roughness parameter is inferred but 74% of the sample is out of the expected parameter range. Potential improvements are discussed to enhance the depiction of the natural variability on a global scale.

Response of the North Pacific Oscillation to global warming in the models of the Intergovernmental Panel on Climate Change Fourth Assessment Report

NASA Astrophysics Data System (ADS)

Chen, Zheng; Gan, Bolan; Wu, Lixin

2017-09-01

Based on 22 of the climate models from phase 3 of the Coupled Model Intercomparison Project, we investigate the ability of the models to reproduce the spatiotemporal features of the wintertime North Pacific Oscillation (NPO), which is the second most important factor determining the wintertime sea level pressure field in simulations of the pre-industrial control climate, and evaluate the NPO response to the future most reasonable global warming scenario (the A1B scenario). We reveal that while most models simulate the geographic distribution and amplitude of the NPO pattern satisfactorily, only 13 models capture both features well. However, the temporal variability of the simulated NPO could not be significantly correlated with the observations. Further analysis indicates the weakened NPO intensity for a scenario of strong global warming is attributable to the reduced lower-tropospheric baroclinicity at mid-latitudes, which is anticipated to disrupt large-scale and low-frequency atmospheric variability, resulting in the diminished transfer of energy to the NPO, together with its northward shift.

Global thermodynamics of confined inhomogeneous dilute gases: A semi-classical approach

NASA Astrophysics Data System (ADS)

Poveda-Cuevas, F. J.; Reyes-Ayala, I.; Seman, J. A.; Romero-Rochín, V.

2018-04-01

In this work we present our contribution to the Latin American School of Physics "Marcos Moshinsky" 2017 on Quantum Correlations which was held in Mexico City during the summer of 2017. We review the efforts that have been done to construct a global thermodynamic description of ultracold dilute gases confined in inhomogeneous potentials. This is difficult because the presence of this non-uniform trap makes the pressure of the gas to be a spatially dependent variable and its volume an ambiguously defined quantity. In this paper we introduce new global thermodynamic variables, equivalent to pressure and volume, and propose a realistic model of the equation of state of the system. This model is based on a mean-field approach which asymptotically reaches the Thomas-Fermi limit for a weakly interacting Bose gas. We put special emphasis to the transition between the normal and superfluid phases by studying the behavior of the isothermal compressibility across the transition. We reveal how the potential modifies the critical properties of the transition by determining the critical exponents associated to the divergences not of the susceptibilities but of their derivatives.

Spread in the magnitude of climate model interdecadal global temperature variability traced to disagreements over high-latitude oceans

NASA Astrophysics Data System (ADS)

Brown, Patrick T.; Li, Wenhong; Jiang, Jonathan H.; Su, Hui

2016-12-01

Unforced variability in global mean surface air temperature can obscure or exaggerate global warming on interdecadal time scales; thus, understanding both the magnitude and generating mechanisms of such variability is of critical importance for both attribution studies as well as decadal climate prediction. Coupled atmosphere-ocean general circulation models (climate models) simulate a wide range of magnitudes of unforced interdecadal variability in global mean surface air temperature (UITglobal), hampering efforts to quantify the influence of UITglobal on contemporary global temperature trends. Recently, a preliminary consensus has emerged that unforced interdecadal variability in local surface temperatures (UITlocal) over the tropical Pacific Ocean is particularly influential on UITglobal. Therefore, a reasonable hypothesis might be that the large spread in the magnitude of UITglobal across climate models can be explained by the spread in the magnitude of simulated tropical Pacific UITlocal. Here we show that this hypothesis is mostly false. Instead, the spread in the magnitude of UITglobal is linked much more strongly to the spread in the magnitude of UITlocal over high-latitude regions characterized by significant variability in oceanic convection, sea ice concentration, and energy flux at both the surface and the top of the atmosphere. Thus, efforts to constrain the climate model produced range of UITglobal magnitude would be best served by focusing on the simulation of air-sea interaction at high latitudes.

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

USGS Publications Warehouse

Cook, B.D.; Bolstad, P.V.; Naesset, E.; Anderson, R. Scott; Garrigues, S.; Morisette, J.T.; Nickeson, J.; Davis, K.J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Food Prices and Climate Extremes: A Model of Global Grain Price Variability with Storage

NASA Astrophysics Data System (ADS)

Otto, C.; Schewe, J.; Frieler, K.

2015-12-01

Extreme climate events such as droughts, floods, or heat waves affect agricultural production in major cropping regions and therefore impact the world market prices of staple crops. In the last decade, crop prices exhibited two very prominent price peaks in 2007-2008 and 2010-2011, threatening food security especially for poorer countries that are net importers of grain. There is evidence that these spikes in grain prices were at least partly triggered by actual supply shortages and the expectation of bad harvests. However, the response of the market to supply shocks is nonlinear and depends on complex and interlinked processes such as warehousing, speculation, and trade policies. Quantifying the contributions of such different factors to short-term price variability remains difficult, not least because many existing models ignore the role of storage which becomes important on short timescales. This in turn impedes the assessment of future climate change impacts on food prices. Here, we present a simple model of annual world grain prices that integrates grain stocks into the supply and demand functions. This firstly allows us to model explicitly the effect of storage strategies on world market price, and thus, for the first time, to quantify the potential contribution of trade policies to price variability in a simple global framework. Driven only by reported production and by long--term demand trends of the past ca. 40 years, the model reproduces observed variations in both the global storage volume and price of wheat. We demonstrate how recent price peaks can be reproduced by accounting for documented changes in storage strategies and trade policies, contrasting and complementing previous explanations based on different mechanisms such as speculation. Secondly, we show how the integration of storage allows long-term projections of grain price variability under climate change, based on existing crop yield scenarios.

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

NASA Technical Reports Server (NTRS)

Cook, Bruce D.; Bolstad, Paul V.; Naesset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

A Darwinian approach to control-structure design

NASA Technical Reports Server (NTRS)

Zimmerman, David C.

1993-01-01

Genetic algorithms (GA's), as introduced by Holland (1975), are one form of directed random search. The form of direction is based on Darwin's 'survival of the fittest' theories. GA's are radically different from the more traditional design optimization techniques. GA's work with a coding of the design variables, as opposed to working with the design variables directly. The search is conducted from a population of designs (i.e., from a large number of points in the design space), unlike the traditional algorithms which search from a single design point. The GA requires only objective function information, as opposed to gradient or other auxiliary information. Finally, the GA is based on probabilistic transition rules, as opposed to deterministic rules. These features allow the GA to attack problems with local-global minima, discontinuous design spaces and mixed variable problems, all in a single, consistent framework.

Comparison of Modeling Approaches for Carbon Partitioning: Impact on Estimates of Global Net Primary Production and Equilibrium Biomass of Woody Vegetation from MODIS GPP

NASA Astrophysics Data System (ADS)

Ise, T.; Litton, C. M.; Giardina, C. P.; Ito, A.

2009-12-01

Plant partitioning of carbon (C) to above- vs. belowground, to growth vs. respiration, and to short vs. long lived tissues exerts a large influence on ecosystem structure and function with implications for the global C budget. Importantly, outcomes of process-based terrestrial vegetation models are likely to vary substantially with different C partitioning algorithms. However, controls on C partitioning patterns remain poorly quantified, and studies have yielded variable, and at times contradictory, results. A recent meta-analysis of forest studies suggests that the ratio of net primary production (NPP) and gross primary production (GPP) is fairly conservative across large scales. To illustrate the effect of this unique meta-analysis-based partitioning scheme (MPS), we compared an application of MPS to a terrestrial satellite-based (MODIS) GPP to estimate NPP vs. two global process-based vegetation models (Biome-BGC and VISIT) to examine the influence of C partitioning on C budgets of woody plants. Due to the temperature dependence of maintenance respiration, NPP/GPP predicted by the process-based models increased with latitude while the ratio remained constant with MPS. Overall, global NPP estimated with MPS was 17 and 27% lower than the process-based models for temperate and boreal biomes, respectively, with smaller differences in the tropics. Global equilibrium biomass of woody plants was then calculated from the NPP estimates and tissue turnover rates from VISIT. Since turnover rates differed greatly across tissue types (i.e., metabolically active vs. structural), global equilibrium biomass estimates were sensitive to the partitioning scheme employed. The MPS estimate of global woody biomass was 7-21% lower than that of the process-based models. In summary, we found that model output for NPP and equilibrium biomass was quite sensitive to the choice of C partitioning schemes. Carbon use efficiency (CUE; NPP/GPP) by forest biome and the globe. Values are means for 2001-2006.

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Variability of the recent climate of eastern Africa

NASA Astrophysics Data System (ADS)

Schreck, Carl J., III; Semazzi, Fredrick H. M.

2004-05-01

The primary objective of this study is to investigate the recent variability of the eastern African climate. The region of interest is also known as the Greater Horn of Africa (GHA), and comprises the countries of Burundi, Djibouti, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan, Uganda, and Tanzania.The analysis was based primarily on the construction of empirical orthogonal functions (EOFs) of gauge rainfall data and on CPC Merged Analysis of Precipitation (CMAP) data, derived from a combination of rain-gauge observations and satellite estimates. The investigation is based on the period 1961-2001 for the short rains season of eastern Africa of October through to December. The EOF analysis was supplemented by projection of National Centers for Environmental Prediction wind data onto the rainfall eigenmodes to understand the rainfall-circulation relationships. Furthermore, correlation and composite analyses have been performed with the Climatic Research Unit globally averaged surface-temperature time series to explore the potential relationship between the climate of eastern Africa and global warming.The most dominant mode of variability (EOF1) based on CMAP data over eastern Africa corresponds to El Niño-southern oscillation (ENSO) climate variability. It is associated with above-normal rainfall amounts during the short rains throughout the entire region, except for Sudan. The corresponding anomalous low-level circulation is dominated by easterly inflow from the Indian Ocean, and to a lesser extent the Congo tropical rain forest, into the positive rainfall anomaly region that extends across most of eastern Africa. The easterly inflow into eastern Africa is part of diffluent outflow from the maritime continent during the warm ENSO events. The second eastern African EOF (trend mode) is associated with decadal variability. In distinct contrast from the ENSO mode pattern, the trend mode is characterized by positive rainfall anomalies over the northern sector of eastern Africa and opposite conditions over the southern sector. This rainfall trend mode eluded detection in previous studies that did not include recent decades of data, because the signal was still relatively weak. The wind projection onto this mode indicates that the primary flow that feeds the positive anomaly region over the northern part of eastern Africa emanates primarily from the rainfall-deficient southern region of eastern Africa and Sudan. Although we do not assign attribution of the trend mode to global warming (in part because of the relatively short period of analysis), the evidence, based on our results and previous studies, strongly suggests a potential connection.

Magma ascent and lava flow emplacement rates during the 2011 Axial Seamount eruption based on CO2 degassing

NASA Astrophysics Data System (ADS)

Jones, M. R.; Soule, S. A.; Gonnermann, H. M.; Le Roux, V.; Clague, D. A.

2018-07-01

Quantitative metrics for eruption rates at mid-ocean ridges (MORs) would improve our understanding of the structure and formation of the uppermost oceanic crust and would provide a means to link volcanic processes with the conditions of the underlying magmatic system. However, these metrics remain elusive because no MOR eruptions have been directly observed. The possibility of disequilibrium degassing in mid-ocean ridge basalts (MORB), due to high eruptive depressurization rates, makes the analysis of volatile concentrations in MORB glass a promising method for evaluating eruption rates. In this study, we estimate magma ascent and lava flow emplacement rates during the 2011 eruption of Axial Seamount based on numerical modeling of diffusion-controlled bubble growth and new measurements of dissolved volatiles, vesicularity, and vesicle size distributions in erupted basalts. This dataset provides a unique view of the variability in magma ascent (∼0.02-1.2 m/s) and lava flow rates (∼0.1-0.7 m/s) during a submarine MOR eruption based on 50 samples collected from a >10 km long fissure system and three individual lava flow lobes. Samples from the 2011 eruption display an unprecedented range in dissolved CO2 concentrations, nearly spanning the full range observed on the global MOR system. The variable vesicularity and dissolved CO2 concentrations in these samples can be explained by differences in the extent of degassing, dictated by flow lengths and velocities during both vertical ascent and horizontal flow along the seafloor. Our results document, for the first time, the variability in magma ascent rates during a submarine eruption (∼0.02-1.2 m/s), which spans the global range previously proposed based on CO2 degassing. The slowest ascent rates are associated with hummocky flows while faster ascent rates produce channelized sheet flows. This study corroborates degassing-based models for eruption rates using comparisons with independent methods and documents the relationship between eruption dynamics, magma ascent rates, and the morphology of eruptive products. Globally, this approach allows interrogation of the processes that govern mid-ocean ridge eruptions and influence the formation of the oceanic crust.

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.

Reliability Sensitivity Analysis and Design Optimization of Composite Structures Based on Response Surface Methodology

NASA Technical Reports Server (NTRS)

Rais-Rohani, Masoud

2003-01-01

This report discusses the development and application of two alternative strategies in the form of global and sequential local response surface (RS) techniques for the solution of reliability-based optimization (RBO) problems. The problem of a thin-walled composite circular cylinder under axial buckling instability is used as a demonstrative example. In this case, the global technique uses a single second-order RS model to estimate the axial buckling load over the entire feasible design space (FDS) whereas the local technique uses multiple first-order RS models with each applied to a small subregion of FDS. Alternative methods for the calculation of unknown coefficients in each RS model are explored prior to the solution of the optimization problem. The example RBO problem is formulated as a function of 23 uncorrelated random variables that include material properties, thickness and orientation angle of each ply, cylinder diameter and length, as well as the applied load. The mean values of the 8 ply thicknesses are treated as independent design variables. While the coefficients of variation of all random variables are held fixed, the standard deviations of ply thicknesses can vary during the optimization process as a result of changes in the design variables. The structural reliability analysis is based on the first-order reliability method with reliability index treated as the design constraint. In addition to the probabilistic sensitivity analysis of reliability index, the results of the RBO problem are presented for different combinations of cylinder length and diameter and laminate ply patterns. The two strategies are found to produce similar results in terms of accuracy with the sequential local RS technique having a considerably better computational efficiency.

Carbon fluxes in tropical forest ecosystems: the value of Eddy-covariance data for individual-based dynamic forest gap models

NASA Astrophysics Data System (ADS)

Roedig, Edna; Cuntz, Matthias; Huth, Andreas

2015-04-01

The effects of climatic inter-annual fluctuations and human activities on the global carbon cycle are uncertain and currently a major issue in global vegetation models. Individual-based forest gap models, on the other hand, model vegetation structure and dynamics on a small spatial (<100 ha) and large temporal scale (>1000 years). They are well-established tools to reproduce successions of highly-diverse forest ecosystems and investigate disturbances as logging or fire events. However, the parameterizations of the relationships between short-term climate variability and forest model processes are often uncertain in these models (e.g. daily variable temperature and gross primary production (GPP)) and cannot be constrained from forest inventories. We addressed this uncertainty and linked high-resolution Eddy-covariance (EC) data with an individual-based forest gap model. The forest model FORMIND was applied to three diverse tropical forest sites in the Amazonian rainforest. Species diversity was categorized into three plant functional types. The parametrizations for the steady-state of biomass and forest structure were calibrated and validated with different forest inventories. The parameterizations of relationships between short-term climate variability and forest model processes were evaluated with EC-data on a daily time step. The validations of the steady-state showed that the forest model could reproduce biomass and forest structures from forest inventories. The daily estimations of carbon fluxes showed that the forest model reproduces GPP as observed by the EC-method. Daily fluctuations of GPP were clearly reflected as a response to daily climate variability. Ecosystem respiration remains a challenge on a daily time step due to a simplified soil respiration approach. In the long-term, however, the dynamic forest model is expected to estimate carbon budgets for highly-diverse tropical forests where EC-measurements are rare.

The Space-Time Variation of Global Crop Yields, Detecting Simultaneous Outliers and Identifying the Teleconnections with Climatic Patterns

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Pal, I.; Khanbilvardi, R.

2017-12-01

An understanding of the climate factors that influence the space-time variability of crop yields is important for food security purposes and can help us predict global food availability. In this study, we address how the crop yield trends of countries globally were related to each other during the last several decades and the main climatic variables that triggered high/low crop yields simultaneously across the world. Robust Principal Component Analysis (rPCA) is used to identify the primary modes of variation in wheat, maize, sorghum, rice, soybeans, and barley yields. Relations between these modes of variability and important climatic variables, especially anomalous sea surface temperature (SSTa), are examined from 1964 to 2010. rPCA is also used to identify simultaneous outliers in each year, i.e. systematic high/low crop yields across the globe. The results demonstrated spatiotemporal patterns of these crop yields and the climate-related events that caused them as well as the connection of outliers with weather extremes. We find that among climatic variables, SST has had the most impact on creating simultaneous crop yields variability and yield outliers in many countries. An understanding of this phenomenon can benefit global crop trade networks.

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling

NASA Astrophysics Data System (ADS)

Amatulli, Giuseppe; Domisch, Sami; Tuanmu, Mao-Ning; Parmentier, Benoit; Ranipeta, Ajay; Malczyk, Jeremy; Jetz, Walter

2018-03-01

Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at Data Citation 1 and for download and visualization at http://www.earthenv.org/topography.

The Association between Bullying and Psychological Health among Senior High School Students in Ghana, West Africa

ERIC Educational Resources Information Center

Owusu, Andrew; Hart, Peter; Oliver, Brittney; Kang, Minsoo

2011-01-01

Background: School-based bullying, a global challenge, negatively impacts the health and development of both victims and perpetrators. This study examined the relationship between bullying victimization and selected psychological variables among senior high school (SHS) students in Ghana, West Africa. Methods: This study utilized data from the…

Unlocking the climate riddle in forested ecosystems

Treesearch

Greg C. Liknes; Christopher W. Woodall; Brian F. Walters; Sara A. Goeking

2012-01-01

Climate information is often used as a predictor in ecological studies, where temporal averages are typically based on climate normals (30-year means) or seasonal averages. While ensemble projections of future climate forecast a higher global average annual temperature, they also predict increased climate variability. It remains to be seen whether forest ecosystems...

A global satellite assisted precipitation climatology

USGS Publications Warehouse

Funk, Christopher C.; Verdin, Andrew P.; Michaelsen, Joel C.; Pedreros, Diego; Husak, Gregory J.; Peterson, P.

2015-01-01

Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high resolution (0.05°) global precipitation climatologies that perform reasonably well in data sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0,http://dx.doi.org/10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

A global satellite-assisted precipitation climatology

NASA Astrophysics Data System (ADS)

Funk, C.; Verdin, A.; Michaelsen, J.; Peterson, P.; Pedreros, D.; Husak, G.

2015-10-01

Accurate representations of mean climate conditions, especially in areas of complex terrain, are an important part of environmental monitoring systems. As high-resolution satellite monitoring information accumulates with the passage of time, it can be increasingly useful in efforts to better characterize the earth's mean climatology. Current state-of-the-science products rely on complex and sometimes unreliable relationships between elevation and station-based precipitation records, which can result in poor performance in food and water insecure regions with sparse observation networks. These vulnerable areas (like Ethiopia, Afghanistan, or Haiti) are often the critical regions for humanitarian drought monitoring. Here, we show that long period of record geo-synchronous and polar-orbiting satellite observations provide a unique new resource for producing high-resolution (0.05°) global precipitation climatologies that perform reasonably well in data-sparse regions. Traditionally, global climatologies have been produced by combining station observations and physiographic predictors like latitude, longitude, elevation, and slope. While such approaches can work well, especially in areas with reasonably dense observation networks, the fundamental relationship between physiographic variables and the target climate variables can often be indirect and spatially complex. Infrared and microwave satellite observations, on the other hand, directly monitor the earth's energy emissions. These emissions often correspond physically with the location and intensity of precipitation. We show that these relationships provide a good basis for building global climatologies. We also introduce a new geospatial modeling approach based on moving window regressions and inverse distance weighting interpolation. This approach combines satellite fields, gridded physiographic indicators, and in situ climate normals. The resulting global 0.05° monthly precipitation climatology, the Climate Hazards Group's Precipitation Climatology version 1 (CHPclim v.1.0, doi:10.15780/G2159X), is shown to compare favorably with similar global climatology products, especially in areas with complex terrain and low station densities.

Carbon cycle. The dominant role of semi-arid ecosystems in the trend and variability of the land CO₂ sink.

PubMed

Ahlström, Anders; Raupach, Michael R; Schurgers, Guy; Smith, Benjamin; Arneth, Almut; Jung, Martin; Reichstein, Markus; Canadell, Josep G; Friedlingstein, Pierre; Jain, Atul K; Kato, Etsushi; Poulter, Benjamin; Sitch, Stephen; Stocker, Benjamin D; Viovy, Nicolas; Wang, Ying Ping; Wiltshire, Andy; Zaehle, Sönke; Zeng, Ning

2015-05-22

The growth rate of atmospheric carbon dioxide (CO2) concentrations since industrialization is characterized by large interannual variability, mostly resulting from variability in CO2 uptake by terrestrial ecosystems (typically termed carbon sink). However, the contributions of regional ecosystems to that variability are not well known. Using an ensemble of ecosystem and land-surface models and an empirical observation-based product of global gross primary production, we show that the mean sink, trend, and interannual variability in CO2 uptake by terrestrial ecosystems are dominated by distinct biogeographic regions. Whereas the mean sink is dominated by highly productive lands (mainly tropical forests), the trend and interannual variability of the sink are dominated by semi-arid ecosystems whose carbon balance is strongly associated with circulation-driven variations in both precipitation and temperature. Copyright © 2015, American Association for the Advancement of Science.

Data-based estimates of the ocean carbon sink variability - first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM)

NASA Astrophysics Data System (ADS)

Rödenbeck, C.; Bakker, D. C. E.; Gruber, N.; Iida, Y.; Jacobson, A. R.; Jones, S.; Landschützer, P.; Metzl, N.; Nakaoka, S.; Olsen, A.; Park, G.-H.; Peylin, P.; Rodgers, K. B.; Sasse, T. P.; Schuster, U.; Shutler, J. D.; Valsala, V.; Wanninkhof, R.; Zeng, J.

2015-12-01

Using measurements of the surface-ocean CO2 partial pressure (pCO2) and 14 different pCO2 mapping methods recently collated by the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea-air CO2 fluxes are investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO2 seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the eastern equatorial Pacific. Despite considerable spread in the detailed variations, mapping methods that fit the data more closely also tend to agree more closely with each other in regional averages. Encouragingly, this includes mapping methods belonging to complementary types - taking variability either directly from the pCO2 data or indirectly from driver data via regression. From a weighted ensemble average, we find an IAV amplitude of the global sea-air CO2 flux of 0.31 PgC yr-1 (standard deviation over 1992-2009), which is larger than simulated by biogeochemical process models. From a decadal perspective, the global ocean CO2 uptake is estimated to have gradually increased since about 2000, with little decadal change prior to that. The weighted mean net global ocean CO2 sink estimated by the SOCOM ensemble is -1.75 PgC yr-1 (1992-2009), consistent within uncertainties with estimates from ocean-interior carbon data or atmospheric oxygen trends.

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.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

In Situ Global Sea Surface Salinity and Variability from the NCEI Global Thermosalinograph Database

NASA Astrophysics Data System (ADS)

Wang, Z.; Boyer, T.; Zhang, H. M.

2017-12-01

Sea surface salinity (SSS) plays an important role in the global ocean circulations. The variations of sea surface salinity are key indicators of changes in air-sea water fluxes. Using nearly 30 years of in situ measurements of sea surface salinity from thermosalinographs, we will evaluate the variations of the sea surface salinity in the global ocean. The sea surface salinity data used are from our newly-developed NCEI Global Thermosalinograph Database - NCEI-TSG. This database provides a comprehensive set of quality-controlled in-situ sea-surface salinity and temperature measurements collected from over 340 vessels during the period 1989 to the present. The NCEI-TSG is the world's most complete TSG dataset, containing all data from the different TSG data assembly centers, e.g. COAPS (SAMOS), IODE (GOSUD) and AOML, with more historical data from NCEI's archive to be added. Using this unique dataset, we will investigate the spatial variations of the global SSS and its variability. Annual and interannual variability will also be studied at selected regions.

Prediction-Market-Based Quantification of Climate Change Consensus and Uncertainty

NASA Astrophysics Data System (ADS)

Boslough, M.

2012-12-01

Intrade is an online trading exchange that includes climate prediction markets. One such family of contracts can be described as "Global temperature anomaly for 2012 to be greater than x °C or more," where the figure x ranges in increments of .05 from .30 to 1.10 (relative to the 1951-1980 base period), based on data published by NASA GISS. Each market will settle at 10.00 if the published global temperature anomaly for 2012 is equal to or greater than x, and will otherwise settle at 0.00. Similar contracts will be available for 2013. Global warming hypotheses can be cast as probabilistic predictions for future temperatures. The first modern such climate prediction is that of Broecker (1975), whose temperatures are easily separable from his CO2 growth scenario—which he overestimated—by interpolating his table of temperature as a function of CO2 concentration and projecting the current trend into the near future. For the current concentration of 395 ppm, Broecker's equilibrium temperature anomaly prediction relative to pre-industrial is 1.05 °C, or about 0.75 °C relative to the GISS base period. His neglect of lag in response to the changes in radiative forcing was partially compensated by his low sensitivity of 2.4 °C, leading to a slight overestimate. Simple linear extrapolation of the current trend since 1975 yields an estimate of .65 ± .09 °C (net warming of .95 °C) for anthropogenic global warming with a normal distribution of random natural variability. To evaluate an extreme case, we can estimate the prediction Broecker would have made if he had used the Lindzen & Choi (2009) climate sensitivity of 0.5 °C. The net post-industrial warming by 2012 would have been 0.21 °C, for an expected change of -0.09 from the GISS base period. This is the temperature to which the Earth would be expected to revert if the observed warming since the 19th century was merely due to random natural variability that coincidentally mimicked Broecker's anthropogenic change prediction for the past 36 years. Assertions made outside the scientific literature can also be cast into predictions for 2012 temperatures, for example Carter's (2006) argument for a lack of warming since 1998 can be extrapolated to a 2012 value of 0.56 °C (net warming of .86 °C), and Easterbrook's (2010) claim of global cooling can be extrapolated to a 2012 value of .42 °C (net warming of .72 °C). All contracts in the current market ensembles are consistent with net warming from pre-industrial temperatures. They are also capable of distinguishing the level of acceptance of the various global warming hypotheses, even by their respective proponents. Moreover, they can be used as a market-based consensus estimate of future warming and climate variability that is weighted according to level of risk taken on by those providing the estimates, while filtering out the opinions of individuals unwilling to accept any financial risk associated with being wrong.

Life cycles of transient planetary waves

NASA Technical Reports Server (NTRS)

Nathan, Terrence

1993-01-01

In recent years there has been an increasing effort devoted to understanding the physical and dynamical processes that govern the global-scale circulation of the atmosphere. This effort has been motivated, in part, from: (1) a wealth of new satellite data; (2) an urgent need to assess the potential impact of chlorofluorocarbons on our climate; (3) an inadequate understanding of the interactions between the troposphere and stratosphere and the role that such interactions play in short and long-term climate variability; and (4) the realization that addressing changes in our global climate requires understanding the interactions among various components of the earth system. The research currently being carried out represents an effort to address some of these issues by carrying out studies that combine radiation, ozone, seasonal thermal forcing and dynamics. Satellite and ground-based data that is already available is being used to construct basic states for our analytical and numerical models. Significant accomplishments from 1991-1992 are presented and include the following: ozone-dynamics interaction; (2) periodic local forcing and low frequency variability; and (3) steady forcing and low frequency variability.

Integration of observations, modelling approaches and remote sensing to address ecosystem response to climate change and disturbance in Africa

NASA Astrophysics Data System (ADS)

Falge, Eva; Brümmer, Christian

2017-04-01

African societies face growing global change challenges and several associated risks. These include rapidly changing patterns of human settlements and an intensified use of ecosystem services. At the same time, climate variability and change are amplifying stress on the functionality of ecosystems and their critical role as important greenhouse gas sinks. A recent review (Valentini et al. 2014) attests Africa a key role in the global carbon cycle contributing an absolute annual carbon sink (-0.61 ± 0.58 Pg C yr-1), which may partly been offset through understudied emissions of CH4 and N2O. The net sink strength is characterized by a substantial sub-regional spatial variability due to biome distribution and degree of anthropogenic influences. 52% of the global carbon emissions by fire are due to African wildfires, which contribute with 1.03 ± 0.22 Pg C yr-1 twice the emissions caused by land use change in Africa (0.51 ± 0.10 Pg C yr-1). Moreover, a quarter of the interannual variability of the global carbon budget is due to the year-to-year variation (± 0.5 Pg C yr-1) of carbon fluxes on the African continent. Among the archetypes to address the above-mentioned challenges in an integrated and multidisciplinary way are better data bases which serve as constraints for atmospheric data and models, thorough attempts to reduce GHG flux uncertainties, or enhanced understanding of climatic, hydrological, and socio-economic drivers of temporal and spatial variability of GHG balances. Some examples from the ARS-AfricaE project that will serve to illustrate the wide range of such activities include: Measurements of CO2 exchange, ecosystem structure and eco-physiological properties at paired sites with natural and managed vegetation, Further development and application of the adaptive Dynamic Global Vegetation Model 2 (aDGVM2) to investigate the influence of different atmospheric CO2 scenarios on carbon pools and fluxes of a selected ecosystem in Skukuza, Kruger National Park, South Africa, Setting up individual-based models to predict ecosystem dynamics under (post-) disturbance management, Monitoring vegetation amount and heterogeneity using remotely sensed images and aerial photography over several decades to examine time series of land cover change, and Investigations of livelihood strategies with focus on carbon balance components to develop sustainable management strategies for disturbed ecosystems and land use change. Despite recent advances, major innovations in understanding carbon cycle, greenhouse gases, air quality and measures of adaptation to and mitigation of climate change are still limited by the lack of global accessibility and comparability of relevant data (open data issues), long-term and sustainable interdisciplinary and trans-institutional research collaborations, and ongoing effective dialogues on multiple levels (policy, science, society).

Co-variability of smoke and fire in the Amazon basin

NASA Astrophysics Data System (ADS)

Mishra, Amit Kumar; Lehahn, Yoav; Rudich, Yinon; Koren, Ilan

2015-05-01

The Amazon basin is a hot spot of anthropogenically-driven biomass burning, accounting for approximately 15% of total global fire emissions. It is essential to accurately measure these fires for robust regional and global modeling of key environmental processes. Here we have explored the link between spatio-temporal variability patterns in the Amazon basin's fires and the resulting smoke loading using 11 years (2002-2012) of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Aerosol Robotic Network (AERONET) observations. Focusing on the peak burning season (July-October), our analysis shows strong inter-annual correlation between aerosol optical depth (AOD) and two MODIS fire products: fire radiative power (FRP) and fire pixel counts (FC). Among these two fire products, the FC better indicates the amount of smoke in the basin, as represented in remotely sensed AOD data. This fire product is significantly correlated both with regional AOD retrievals from MODIS and with point AOD measurements from the AERONET stations, pointing to spatial homogenization of the smoke over the basin on a seasonal time scale. However, MODIS AODs are found better than AERONET AODs observation for linking between smoke and fire. Furthermore, MODIS AOD measurements are strongly correlated with number of fires ∼10-20 to the east, most likely due to westward advection of smoke by the wind. These results can be rationalized by the regional topography and the wind regimes. Our analysis can improve data assimilation of satellite and ground-based observations into regional and global model studies, thus improving the assessment of the environmental and climatic impacts of frequency and distribution variability of the Amazon basin's fires. We also provide the optimal spatial and temporal scales for ground-based observations, which could be used for such applications.

A 20 year independent record of sea surface temperature for climate from Along-Track Scanning Radiometers

NASA Astrophysics Data System (ADS)

Merchant, Christopher J.; Embury, Owen; Rayner, Nick A.; Berry, David I.; Corlett, Gary K.; Lean, Katie; Veal, Karen L.; Kent, Elizabeth C.; Llewellyn-Jones, David T.; Remedios, John J.; Saunders, Roger

2012-12-01

A new record of sea surface temperature (SST) for climate applications is described. This record provides independent corroboration of global variations estimated from SST measurements made in situ. Infrared imagery from Along-Track Scanning Radiometers (ATSRs) is used to create a 20 year time series of SST at 0.1° latitude-longitude resolution, in the ATSR Reprocessing for Climate (ARC) project. A very high degree of independence of in situ measurements is achieved via physics-based techniques. Skin SST and SST estimated for 20 cm depth are provided, with grid cell uncertainty estimates. Comparison with in situ data sets establishes that ARC SSTs generally have bias of order 0.1 K or smaller. The precision of the ARC SSTs is 0.14 K during 2003 to 2009, from three-way error analysis. Over the period 1994 to 2010, ARC SSTs are stable, with better than 95% confidence, to within 0.005 K yr-1(demonstrated for tropical regions). The data set appears useful for cleanly quantifying interannual variability in SST and major SST anomalies. The ARC SST global anomaly time series is compared to the in situ-based Hadley Centre SST data set version 3 (HadSST3). Within known uncertainties in bias adjustments applied to in situ measurements, the independent ARC record and HadSST3 present the same variations in global marine temperature since 1996. Since the in situ observing system evolved significantly in its mix of measurement platforms and techniques over this period, ARC SSTs provide an important corroboration that HadSST3 accurately represents recent variability and change in this essential climate variable.

Webinar Presentation: Characterization of Emissions from Small, Variable Solid Fuel Combustion Sources for Determining Global Emissions and Climate Impact

EPA Pesticide Factsheets

This presentation, Characterization of Emissions from Small, Variable Solid Fuel Combustion Sources for Determining Global Emissions and Climate Impact, was given at the STAR Black Carbon 2016 Webinar Series.

The NASA Marshall Space Flight Center Earth Global Reference Atmospheric Model-2010 Version

NASA Technical Reports Server (NTRS)

Leslie, F. W.; Justus, C. G.

2011-01-01

Reference or standard atmospheric models have long been used for design and mission planning of various aerospace systems. The NASA Marshall Space Flight Center Global Reference Atmospheric Model was developed in response to the need for a design reference atmosphere that provides complete global geographical variability and complete altitude coverage (surface to orbital altitudes), as well as complete seasonal and monthly variability of the thermodynamic variables and wind components. In addition to providing the geographical, height, and monthly variation of the mean atmospheric state, it includes the ability to simulate spatial and temporal perturbations.

The tropical Pacific as a key pacemaker of the variable rates of global warming

NASA Astrophysics Data System (ADS)

Kosaka, Yu; Xie, Shang-Ping

2016-09-01

Global mean surface temperature change over the past 120 years resembles a rising staircase: the overall warming trend was interrupted by the mid-twentieth-century big hiatus and the warming slowdown since about 1998. The Interdecadal Pacific Oscillation has been implicated in modulations of global mean surface temperatures, but which part of the mode drives the variability in warming rates is unclear. Here we present a successful simulation of the global warming staircase since 1900 with a global ocean-atmosphere coupled model where tropical Pacific sea surface temperatures are forced to follow the observed evolution. Without prescribed tropical Pacific variability, the same model, on average, produces a continual warming trend that accelerates after the 1960s. We identify four events where the tropical Pacific decadal cooling markedly slowed down the warming trend. Matching the observed spatial and seasonal fingerprints we identify the tropical Pacific as a key pacemaker of the warming staircase, with radiative forcing driving the overall warming trend. Specifically, tropical Pacific variability amplifies the first warming epoch of the 1910s-1940s and determines the timing when the big hiatus starts and ends. Our method of removing internal variability from the observed record can be used for real-time monitoring of anthropogenic warming.

Discrete-time bidirectional associative memory neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liang, variable delays [rapid communication] J.; Cao, J.; Ho, D. W. C.

2005-02-01

Based on the linear matrix inequality (LMI), some sufficient conditions are presented in this Letter for the existence, uniqueness and global exponential stability of the equilibrium point of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Some of the stability criteria obtained in this Letter are delay-dependent, and some of them are delay-independent, they are less conservative than the ones reported so far in the literature. Furthermore, the results provide one more set of easily verified criteria for determining the exponential stability of discrete-time BAM neural networks.

Maintaining Balance: The Increasing Role of Energy Storage for Renewable Integration

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

Stenclik, Derek; Denholm, Paul; Chalamala, Babu

For nearly a century, global power systems have focused on three key functions: to generate, transmit, and distribute electricity as a real-time commodity. Physics requires that electricity generation always be in real-time balance with load, despite variability in load on timescales ranging from sub-second disturbances to multi-year trends. With the increasing role of variable generation from wind and solar, retirements of fossil fuel-based generation, and a changing consumer demand profile, grid operators are using new methods to maintain this balance.

Maintaining Balance: The Increasing Role of Energy Storage for Renewable Integration

DOE PAGES

Stenclik, Derek; Denholm, Paul; Chalamala, Babu

2017-10-17

For nearly a century, global power systems have focused on three key functions: to generate, transmit, and distribute electricity as a real-time commodity. Physics requires that electricity generation always be in real-time balance with load, despite variability in load on timescales ranging from sub-second disturbances to multi-year trends. With the increasing role of variable generation from wind and solar, retirements of fossil fuel-based generation, and a changing consumer demand profile, grid operators are using new methods to maintain this balance.

Decoupling global biases and local interactions between cell biological variables

PubMed Central

Zaritsky, Assaf; Obolski, Uri; Gan, Zhuo; Reis, Carlos R; Kadlecova, Zuzana; Du, Yi; Schmid, Sandra L; Danuser, Gaudenz

2017-01-01

Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic insight into cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu. DOI: http://dx.doi.org/10.7554/eLife.22323.001 PMID:28287393

The Volume of Earth's Lakes

NASA Astrophysics Data System (ADS)

Cael, B. B.

How much water do lakes on Earth hold? Global lake volume estimates are scarce, highly variable, and poorly documented. We develop a mechanistic null model for estimating global lake mean depth and volume based on a statistical topographic approach to Earth's surface. The volume-area scaling prediction is accurate and consistent within and across lake datasets spanning diverse regions. We applied these relationships to a global lake area census to estimate global lake volume and depth. The volume of Earth's lakes is 199,000 km3 (95% confidence interval 196,000-202,000 km3) . This volume is in the range of historical estimates (166,000-280,000 km3) , but the overall mean depth of 41.8 m (95% CI 41.2-42.4 m) is significantly lower than previous estimates (62 - 151 m). These results highlight and constrain the relative scarcity of lake waters in the hydrosphere and have implications for the role of lakes in global biogeochemical cycles. We also evaluate the size (area) distribution of lakes on Earth compared to expectations from percolation theory. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2388357.

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.

A pseudoproxy assessment of data assimilation for reconstructing the atmosphere-ocean dynamics of hydroclimate extremes

NASA Astrophysics Data System (ADS)

Steiger, Nathan J.; Smerdon, Jason E.

2017-10-01

Because of the relatively brief observational record, the climate dynamics that drive multiyear to centennial hydroclimate variability are not adequately characterized and understood. Paleoclimate reconstructions based on data assimilation (DA) optimally fuse paleoclimate proxies with the dynamical constraints of climate models, thus providing a coherent dynamical picture of the past. DA is therefore an important new tool for elucidating the mechanisms of hydroclimate variability over the last several millennia. But DA has so far remained untested for global hydroclimate reconstructions. Here we explore whether or not DA can be used to skillfully reconstruct global hydroclimate variability along with the driving climate dynamics. Through a set of idealized pseudoproxy experiments, we find that an established DA reconstruction approach can in principle be used to reconstruct hydroclimate at both annual and seasonal timescales. We find that the skill of such reconstructions is generally highest near the proxy sites. This set of reconstruction experiments is specifically designed to estimate a realistic upper bound for the skill of this DA approach. Importantly, this experimental framework allows us to see where and for what variables the reconstruction approach may never achieve high skill. In particular for tree rings, we find that hydroclimate reconstructions depend critically on moisture-sensitive trees, while temperature reconstructions depend critically on temperature-sensitive trees. Real-world DA-based reconstructions will therefore likely require a spatial mixture of temperature- and moisture-sensitive trees to reconstruct both temperature and hydroclimate variables. Additionally, we illustrate how DA can be used to elucidate the dynamical mechanisms of drought with two examples: tropical drivers of multiyear droughts in the North American Southwest and in equatorial East Africa. This work thus provides a foundation for future DA-based hydroclimate reconstructions using real-proxy networks while also highlighting the utility of this important tool for hydroclimate research.

CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture.

PubMed

Gommes, René; Wu, Bingfang; Zhang, Ning; Feng, Xueliang; Zeng, Hongwei; Li, Zhongyuan; Chen, Bo

2017-02-01

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; www.cropwatch.com.cn , Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture

NASA Astrophysics Data System (ADS)

Gommes, René; Wu, Bingfang; Zhang, Ning; Feng, Xueliang; Zeng, Hongwei; Li, Zhongyuan; Chen, Bo

2017-02-01

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; http://www.cropwatch.com.cn, Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

Global variability of cloud condensation nuclei concentrations

NASA Astrophysics Data System (ADS)

Makkonen, Risto; Krüger, Olaf

2017-04-01

Atmospheric aerosols can influence cloud optical and dynamical processes by acting as cloud condensation nuclei (CCN). Globally, these indirect aerosol effects are significant to the radiative budget as well as a source of high uncertainty in anthropogenic radiative forcing. While historically many global climate models have fixed CCN concentrations to a certain level, most state-of-the-art models calculate aerosol-cloud interactions with sophisticated methodologies based on interactively simulated aerosol size distributions. However, due to scarcity of atmospheric observations simulated global CCN concentrations remain poorly constrained. Here we assess global CCN variability with a climate model, and attribute potential trends during 2000-2010 to changes in emissions and meteorological fields. Here we have used ECHAM5.5-HAM2 with model M7 microphysical aerosol model. The model has been upgraded with a secondary organic aerosol (SOA) scheme including ELVOCs. Dust and sea salt emissions are calculated online, based on wind speed and hydrology. Each experiment is 11 years, analysed after a 6-month spin-up period. The MODIS CCN product (Terra platform) is used to evaluate model performance throughout 2000-2010. While optical remote observation of CCN column includes several deficiencies, the products serves as a proxy for changes during the simulation period. In our analysis we utilize the observed and simulated vertical column integrated CCN concentration, and limit our analysis only over marine regions. Simulated annual CCN column densities reach 2ṡ108 cm-2 near strong source regions in central Africa, Arabian Sea, Bay of Bengal and China sea. The spatial concentration gradient in CCN(0.2%) is steep, and column densities drop to <50% a few hundred kilometers away from the coasts. While the spatial distribution of CCN at 0.2% supersaturation is closer to that of MODIS proxy, as opposed to 1.0% supersaturation, the overall column integrated CCN are too low. Still, we can compare the relative response of CCN to emission and meteorological variability. Most evident pattern of high temporal correlation is found over North Atlantic ocean, extending throughout Europe and up to Gulf of Mexico. All of these regions show a generally decreasing trend throughout the decade in control simulations and MODIS CCN, and the simulations including the emission trends clearly improve the simulations with climatological emissions. In regions where the observed intra-annual cycle correlates well with sea-spray emissions, the long-term annual correlation usually remains poor. This could indicate that the model is unable to capture the natural variability in marine aerosol emissions.

The contribution of respiration in tree-stems to the Dole Effect

NASA Astrophysics Data System (ADS)

Angert, A.; Muhr, J.; Negron Juarez, R.; Alegria Muñoz, W.; Kraemer, G.; Ramirez Santillan, J.; Chambers, J. Q.; Trumbore, S. E.

2012-01-01

Understanding the variability and the current value of the Dole Effect, which has been used to infer past changes in biospheric productivity, requires accurate information on the discrimination associated with respiratory oxygen consumption in each of the biosphere components. Respiration in tree stems is an important component of the land carbon cycle. Here we measured, for the first time, the discrimination associated with tree stem oxygen uptake. The measurements included tropical forest trees, which are major contributors to the global fluxes of carbon and oxygen. We found discrimination in the range of 12.6-21.5 ‰, indicating both diffusion limitation, resulting in O2 discrimination values below 20 ‰, and alternative oxidase respiration, which resulted in discrimination values greater than 20 ‰. Discrimination varied seasonally, between and within tree species. Calculations based on these results show that variability in woody plants discrimination can result in significant variations in the global Dole Effect.

The contribution of respiration in tree stems to the Dole Effect

NASA Astrophysics Data System (ADS)

Angert, A.; Muhr, J.; Negron Juarez, R.; Alegria Muñoz, W.; Kraemer, G.; Ramirez Santillan, J.; Chambers, J. Q.; Trumbore, S. E.

2012-10-01

Understanding the variability and the current value of the Dole Effect, which has been used to infer past changes in biospheric productivity, requires accurate information on the isotopic discrimination associated with respiratory oxygen consumption in each of the biosphere components. Respiration in tree stems is an important component of the land carbon cycle. Here we measured, for the first time, the discrimination associated with tree stem oxygen uptake. The measurements included tropical forest trees, which are major contributors to the global fluxes of carbon and oxygen. We found discrimination in the range of 12.6-21.5‰, indicating both diffusion limitation, resulting in O2 discrimination values below 20‰, and alternative oxidase respiration, which resulted in discrimination values greater than 20‰. Discrimination varied seasonally, between and within tree species. Calculations based on these results show that variability in woody plants discrimination can result in significant variations in the global Dole Effect.

Well-balanced schemes for the Euler equations with gravitation: Conservative formulation using global fluxes

NASA Astrophysics Data System (ADS)

Chertock, Alina; Cui, Shumo; Kurganov, Alexander; Özcan, Şeyma Nur; Tadmor, Eitan

2018-04-01

We develop a second-order well-balanced central-upwind scheme for the compressible Euler equations with gravitational source term. Here, we advocate a new paradigm based on a purely conservative reformulation of the equations using global fluxes. The proposed scheme is capable of exactly preserving steady-state solutions expressed in terms of a nonlocal equilibrium variable. A crucial step in the construction of the second-order scheme is a well-balanced piecewise linear reconstruction of equilibrium variables combined with a well-balanced central-upwind evolution in time, which is adapted to reduce the amount of numerical viscosity when the flow is at (near) steady-state regime. We show the performance of our newly developed central-upwind scheme and demonstrate importance of perfect balance between the fluxes and gravitational forces in a series of one- and two-dimensional examples.

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Adler, Robert F.

2002-01-01

The development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale is presented. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR-based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and stratiform rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models.

PubMed

Gregg, Watson W; Rousseaux, Cécile S

2014-09-01

Quantifying change in ocean biology using satellites is a major scientific objective. We document trends globally for the period 1998-2012 by integrating three diverse methodologies: ocean color data from multiple satellites, bias correction methods based on in situ data, and data assimilation to provide a consistent and complete global representation free of sampling biases. The results indicated no significant trend in global pelagic ocean chlorophyll over the 15 year data record. These results were consistent with previous findings that were based on the first 6 years and first 10 years of the SeaWiFS mission. However, all of the Northern Hemisphere basins (north of 10° latitude), as well as the Equatorial Indian basin, exhibited significant declines in chlorophyll. Trend maps showed the local trends and their change in percent per year. These trend maps were compared with several other previous efforts using only a single sensor (SeaWiFS) and more limited time series, showing remarkable consistency. These results suggested the present effort provides a path forward to quantifying global ocean trends using multiple satellite missions, which is essential if we are to understand the state, variability, and possible changes in the global oceans over longer time scales.

Means, Variability and Trends of Precipitation in the Global Climate as Determined by the 25-year GEWEWGPCP Data Set

NASA Technical Reports Server (NTRS)

Adler, R. F.; Gu, G.; Curtis, S.; Huffman, G. J.

2004-01-01

The Global Precipitation Climatology Project (GPCP) 25-year precipitation data set is used as a basis to evaluate the mean state, variability and trends (or inter-decadal changes) of global and regional scales of precipitation. The uncertainties of these characteristics of the data set are evaluated by examination of other, parallel data sets and examination of shorter periods with higher quality data (e.g., TRMM). The global and regional means are assessed for uncertainty by comparing with other satellite and gauge data sets, both globally and regionally. The GPCP global mean of 2.6 mdday is divided into values of ocean and land and major latitude bands (Tropics, mid-latitudes, etc.). Seasonal variations globally and by region are shown and uncertainties estimated. The variability of precipitation year-to-year is shown to be related to ENS0 variations and volcanoes and is evaluated in relation to the overall lack of a significant global trend. The GPCP data set necessarily has a heterogeneous time series of input data sources, so part of the assessment described above is to test the initial results for potential influence by major data boundaries in the record.

Contrasting responses of water use efficiency to drought across global terrestrial ecosystems

PubMed Central

Yang, Yuting; Guan, Huade; Batelaan, Okke; McVicar, Tim R.; Long, Di; Piao, Shilong; Liang, Wei; Liu, Bing; Jin, Zhao; Simmons, Craig T.

2016-01-01

Drought is an intermittent disturbance of the water cycle that profoundly affects the terrestrial carbon cycle. However, the response of the coupled water and carbon cycles to drought and the underlying mechanisms remain unclear. Here we provide the first global synthesis of the drought effect on ecosystem water use efficiency (WUE = gross primary production (GPP)/evapotranspiration (ET)). Using two observational WUE datasets (i.e., eddy-covariance measurements at 95 sites (526 site-years) and global gridded diagnostic modelling based on existing observation and a data-adaptive machine learning approach), we find a contrasting response of WUE to drought between arid (WUE increases with drought) and semi-arid/sub-humid ecosystems (WUE decreases with drought), which is attributed to different sensitivities of ecosystem processes to changes in hydro-climatic conditions. WUE variability in arid ecosystems is primarily controlled by physical processes (i.e., evaporation), whereas WUE variability in semi-arid/sub-humid regions is mostly regulated by biological processes (i.e., assimilation). We also find that shifts in hydro-climatic conditions over years would intensify the drought effect on WUE. Our findings suggest that future drought events, when coupled with an increase in climate variability, will bring further threats to semi-arid/sub-humid ecosystems and potentially result in biome reorganization, starting with low-productivity and high water-sensitivity grassland. PMID:26983909

A TRMM/GPM retrieval of the total mean generator current for the global electric circuit

NASA Astrophysics Data System (ADS)

Peterson, Michael; Deierling, Wiebke; Liu, Chuntao; Mach, Douglas; Kalb, Christina

2017-09-01

A specialized satellite version of the passive microwave electric field retrieval algorithm (Peterson et al., 2015) is applied to observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites to estimate the generator current for the Global Electric Circuit (GEC) and compute its temporal variability. By integrating retrieved Wilson currents from electrified clouds across the globe, we estimate a total mean current of between 1.4 kA (assuming the 7% fraction of electrified clouds producing downward currents measured by the ER-2 is representative) to 1.6 kA (assuming all electrified clouds contribute to the GEC). These current estimates come from all types of convective weather without preference, including Electrified Shower Clouds (ESCs). The diurnal distribution of the retrieved generator current is in excellent agreement with the Carnegie curve (RMS difference: 1.7%). The temporal variability of the total mean generator current ranges from 110% on semi-annual timescales (29% on an annual timescale) to 7.5% on decadal timescales with notable responses to the Madden-Julian Oscillation and El Nino Southern Oscillation. The geographical distribution of current includes significant contributions from oceanic regions in addition to the land-based tropical chimneys. The relative importance of the Americas and Asia chimneys compared to Africa is consistent with the best modern ground-based observations and further highlights the importance of ESCs for the GEC.

A Global Lake Ecological Observatory Network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

USGS Publications Warehouse

David, Hamilton P; Carey, Cayelan C.; Arvola, Lauri; Arzberger, Peter; Brewer, Carol A.; Cole, Jon J; Gaiser, Evelyn; Hanson, Paul C.; Ibelings, Bas W; Jennings, Eleanor; Kratz, Tim K; Lin, Fang-Pang; McBride, Christopher G.; de Motta Marques, David; Muraoka, Kohji; Nishri, Ami; Qin, Boqiang; Read, Jordan S.; Rose, Kevin C.; Ryder, Elizabeth; Weathers, Kathleen C.; Zhu, Guangwei; Trolle, Dennis; Brookes, Justin D

2014-01-01

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate process- based ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest.

Use and uncertainty evaluation of a process-based model for assessing the methane budgets of global terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Ito, A.; Inatomi, M.

2011-07-01

We assessed the global terrestrial budget of methane (CH4) using a process-based biogeochemical model (VISIT) and inventory data. Emissions from wetlands, paddy fields, biomass burning, and plants, and oxidative consumption by upland soils, were simulated by the model. Emissions from livestock ruminants and termites were evaluated by an inventory approach. These CH4 flows were estimated for each of the model's 0.5° × 0.5° grid cells from 1901 to 2009, while accounting for atmospheric composition, meteorological factors, and land-use changes. Estimation uncertainties were examined through ensemble simulations using different parameterization schemes and input data (e.g. different wetland maps and emission factors). From 1996 to 2005, the average global terrestrial CH4 budget was estimated on the basis of 576 simulations, and terrestrial ecosystems were found to be a net source of 320.4 ± 18.9 Tg CH4 yr-1. Wetland and ruminant emissions were the primary sources. The results of our simulations indicate that sources and sinks are distributed highly heterogeneously over the Earth's land surface. Seasonal and interannual variability in the terrestrial budget was assessed. The trend of increasing net terrestrial sources and its relationship with temperature variability imply that terrestrial CH4 feedbacks will play an increasingly important role as a result of future climatic change.

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 gridded dataset of daily precipitation going back to 1950, ideal for analysing precipitation extremes

NASA Astrophysics Data System (ADS)

Contractor, S.; Donat, M.; Alexander, L. V.

2017-12-01

Reliable observations of precipitation are necessary to determine past changes in precipitation and validate models, allowing for reliable future projections. Existing gauge based gridded datasets of daily precipitation and satellite based observations contain artefacts and have a short length of record, making them unsuitable to analyse precipitation extremes. The largest limiting factor for the gauge based datasets is a dense and reliable station network. Currently, there are two major data archives of global in situ daily rainfall data, first is Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and the other by Global Precipitation Climatology Centre (GPCC) part of the Deutsche Wetterdienst (DWD). We combine the two data archives and use automated quality control techniques to create a reliable long term network of raw station data, which we then interpolate using block kriging to create a global gridded dataset of daily precipitation going back to 1950. We compare our interpolated dataset with existing global gridded data of daily precipitation: NOAA Climate Prediction Centre (CPC) Global V1.0 and GPCC Full Data Daily Version 1.0, as well as various regional datasets. We find that our raw station density is much higher than other datasets. To avoid artefacts due to station network variability, we provide multiple versions of our dataset based on various completeness criteria, as well as provide the standard deviation, kriging error and number of stations for each grid cell and timestep to encourage responsible use of our dataset. Despite our efforts to increase the raw data density, the in situ station network remains sparse in India after the 1960s and in Africa throughout the timespan of the dataset. Our dataset would allow for more reliable global analyses of rainfall including its extremes and pave the way for better global precipitation observations with lower and more transparent uncertainties.

River reach classification for the Greater Mekong Region at high spatial resolution

NASA Astrophysics Data System (ADS)

Ouellet Dallaire, C.; Lehner, B.

2014-12-01

River classifications have been used in river health and ecological assessments as coarse proxies to represent aquatic biodiversity when comprehensive biological and/or species data is unavailable. Currently there are no river classifications or biological data available in a consistent format for the extent of the Greater Mekong Region (GMR; including the Irrawaddy, the Salween, the Chao Praya, the Mekong and the Red River basins). The current project proposes a new river habitat classification for the region, facilitated by the HydroSHEDS (HYDROlogical SHuttle Elevation Derivatives at multiple Scales) database at 500m pixel resolution. The classification project is based on the Global River Classification framework relying on the creation of multiple sub-classifications based on different disciplines. The resulting classes from the sub-classification are later combined into final classes to create a holistic river reach classification. For the GMR, a final habitat classification was created based on three sub-classifications: a hydrological sub-classification based only on discharge indices (river size and flow variability); a physio-climatic sub-classification based on large scale indices of climate and elevation (biomes, ecoregions and elevation); and a geomorphological sub-classification based on local morphology (presence of floodplains, reach gradient and sand transport). Key variables and thresholds were identified in collaboration with local experts to ensure that regional knowledge was included. The final classification is composed 54 unique final classes based on 3 sub-classifications with less than 15 classes each. The resulting classifications are driven by abiotic variables and do not include biological data, but they represent a state-of-the art product based on best available data (mostly global data). The most common river habitat type is the "dry broadleaf, low gradient, very small river". These classifications could be applied in a wide range of hydro-ecological assessments and useful for a variety of stakeholders such as NGO, governments and researchers.

Hiatus on the upward staircase of global warming

NASA Astrophysics Data System (ADS)

Xie, S. P.; Kosaka, Y.

2016-12-01

Since the 19th century, global-mean surface temperature (GMST) has risen in staircase-like stages due to contributions from both radiative forcing and internal variability. Our earlier study showed that tropical Pacific variability, specifically the La Nina-like cooling, caused the current hiatus of global warming. We have extended the Pacific Ocean-Global Atmosphere (POGA) pacemaker experiment back to the late 19th century, by restoring tropical Pacific sea surface temperature anomalies towards the observed history. POGA reproduces annual-mean GMST variability with high correlation. We quantify relative contributions from the radiative forcing and tropical Pacific variability for various epochs of the staircase. Beyond the global mean, POGA also captures observed regional trends of surface temperature for these periods, especially over the tropical Indian Ocean, Indian subcontinent, North and South Pacific and North America. The POGA effect for the recent hiatus is comparable in magnitude with that at the beginning of the 20th century, but lasts the longest in duration over the past 150 years. The attendant strengthening of the Pacific trade winds since the 1990s is unprecedented on the instrumental record. To the extent that POGA captures much of the internal variability in GMST, we can infer radiatively forced GMST response. This method has the advantage of being independent of the model's radiative forcing and climate sensitivity. While raw data show a warming of 0.9 degree C for the recent five-year period of 2010-2014 relative to 1900, our new calculation yields a much higher anthropogenic warming of 1.2 C after correcting for the internal variability effect. This indicates that the task is more challenging than thought to implement the Paris consensus of limiting global average temperature change to below 2 C above preindustrial levels.

A mass-conserving mixed Fourier-Galerkin B-Spline-collocation method for Direct Numerical Simulation of the variable-density Navier-Stokes equations

NASA Astrophysics Data System (ADS)

Reuter, Bryan; Oliver, Todd; Lee, M. K.; Moser, Robert

2017-11-01

We present an algorithm for a Direct Numerical Simulation of the variable-density Navier-Stokes equations based on the velocity-vorticity approach introduced by Kim, Moin, and Moser (1987). In the current work, a Helmholtz decomposition of the momentum is performed. Evolution equations for the curl and the Laplacian of the divergence-free portion are formulated by manipulation of the momentum equations and the curl-free portion is reconstructed by enforcing continuity. The solution is expanded in Fourier bases in the homogeneous directions and B-Spline bases in the inhomogeneous directions. Discrete equations are obtained through a mixed Fourier-Galerkin and collocation weighted residual method. The scheme is designed such that the numerical solution conserves mass locally and globally by ensuring the discrete divergence projection is exact through the use of higher order splines in the inhomogeneous directions. The formulation is tested on multiple variable-density flow problems.

Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2011-01-01

A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and land surface hydrology through simulation, downscaling, and data assimilation. An overview of the GPM mission, science status, and synergies with HyMex activities will be presented

Global Precipitation Patterns Associated with ENSO and Tropical Circulations

NASA Technical Reports Server (NTRS)

Curtis, Scott; Adler, Robert; Huffman, George; Bolvin, David; Nelkin, Eric

1999-01-01

Tropical precipitation and the accompanying latent heat release is the engine that drives the global circulation. An increase or decrease in rainfall in the tropics not only leads to the local effects of flooding or drought, but contributes to changes in the large scale circulation and global climate system. Rainfall in the tropics is highly variable, both seasonally (monsoons) and interannually (ENSO). Two experimental observational data sets, developed under the auspices of the Global Precipitation Climatology Project (GPCP), are used in this study to examine the relationships between global precipitation and ENSO and extreme monsoon events over the past 20 years. The V2x79 monthly product is a globally complete, 2.5 deg x 2.5 deg, satellite-gauge merged data set that covers the period 1979 to the present. Indices based on patterns of satellite-derived rainfall anomalies in the Pacific are used to analyze the teleconnections between ENSO and global precipitation, with emphasis on the monsoon systems. It has been well documented that dry (wet) Asian monsoons accompany warm (cold) ENSO events. However, during the summer seasons of the 1997/98 ENSO the precipitation anomalies were mostly positive over India and the Bay of Bengal, which may be related to an epoch-scale variability in the Asian monsoon circulation. The North American monsoon may be less well linked to ENSO, but a positive precipitation anomaly was observed over Mexico around the September following the 1997/98 event. For the twenty-year record, precipitation and SST patterns in the tropics are analyzed during wet and dry monsoons. For the Asian summer monsoon, positive rainfall anomalies accompany two distinct patterns of tropical precipitation and a warm Indian Ocean. Negative anomalies coincide with a wet Maritime Continent.

Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers

NASA Astrophysics Data System (ADS)

Lakshmi Madhavan, Bomidi; Deneke, Hartwig; Witthuhn, Jonas; Macke, Andreas

2017-03-01

The time series of global radiation observed by a dense network of 99 autonomous pyranometers during the HOPE campaign around Jülich, Germany, are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken-cloud conditions compared to clear, cirrus, or overcast skies. The spatial autocorrelation function including its frequency dependence is determined to quantify the degree of similarity of two time series measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies above 1/3 min-1 and points separated by more than 1 km, variations in transmittance become completely uncorrelated. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness; on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid box of 10 km × 10 km and averaging periods of 1.5-3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 (clear), 1.8 (cirrus), 1.5 (overcast), and 4.2 % (broken clouds). The solar global radiation observed at a single station is found to deviate from the spatial average by as much as 14-23 (clear), 8-26 (cirrus), 4-23 (overcast), and 31-79 W m-2 (broken clouds) from domain averages ranging from 1 km × 1 km to 10 km × 10 km in area.

Assessing the Impact of Observations on the Prediction of Effective Atmospheric Angular Momentum from NAVGEM

NASA Astrophysics Data System (ADS)

Baker, N. L.; Langland, R.

2016-12-01

Variations in Earth rotation are measured by comparing a time based on Earth's variable rotation rate about its axis to a time standard based on an internationally coordinated ensemble of atomic clocks that provide a uniform time scale. The variability of Earth's rotation is partly due to the changes in angular momentum that occur in the atmosphere and ocean as weather patterns and ocean features develop, propagate, and dissipate. The NAVGEM Effective Atmospheric Angular Momentum Functions (EAAMF) and their predictions are computed following Barnes et al. (1983), and provided to the U.S. Naval Observatory daily. These along with similar data from the NOAA GFS model are used to calculate and predict the Earth orientation parameters (Stamatakos et al., 2016). The Navy's high-resolution global weather prediction system consists of the Navy Global Environmental Model (NAVGEM; Hogan et al., 2014) and a hybrid four-dimensional variational data assimilation system (4DVar) (Kuhl et al., 2013). An important component of NAVGEM is the Forecast Sensitivity Observation Impact (FSOI). FSOI is a mathematical method to quantify the contribution of individual observations or sets of observations to the reduction in the 24-hr forecast error (Langland and Baker, 2004). The FSOI allows for dynamic monitoring of the relative quality and value of the observations assimilated by NAVGEM, and the relative ability of the data assimilation system to effectively use the observation information to generate an improved forecast. For this study, along with the FSOI based on the global moist energy error norm, we computed the FSOI using an error norm based on the Effective Angular Momentum Functions. This modification allowed us to assess which observations were most beneficial in reducing the 24-hr forecast error for the atmospheric angular momentum.

Interannual to Decadal SST Variability in the Tropical Indian Ocean

NASA Astrophysics Data System (ADS)

Wang, G.; Newman, M.; Han, W.

2017-12-01

The Indian Ocean has received increasing attention in recent years for its large impacts on regional and global climate. However, due mainly to the close interdependence of the climate variation within the Tropical Pacific and the Indian Ocean, the internal sea surface temperature (SST) variability within the Indian Ocean has not been studied extensively on longer time scales. In this presentation we will show analysis of the interannual to decadal SST variability in the Tropical Indian Ocean in observations and Linear Inverse Model (LIM) results. We also compare the decoupled Indian Ocean SST variability from the Pacific against fully coupled one based on LIM integrations, to test the factors influence the features of the leading SST modes in the Indian Ocean. The result shows the Indian Ocean Basin (IOB) mode, which is strongly related to global averaged SST variability, passively responses to the Pacific variation. Without tropical Indo-Pacific coupling interaction, the intensity of IOB significantly decreases by 80%. The Indian Ocean Dipole (IOD) mode demonstrates its independence from the Pacific SST variability since the IOD does not change its long-term characteristics at all without inter-basin interactions. The overall SSTA variance decreases significantly in the Tropical Indian Ocean in the coupling restricted LIM runs, especially when the one-way impact from the Pacific to the Indian Ocean is turned off, suggesting that most of the variability in the Indian Ocean comes from the Pacific influence. On the other hand, the Indian Ocean could also transport anomalies to the Pacific, making the interaction a complete two-way process.

Motivational processes in mild cognitive impairment and Alzheimer's disease: results from the Motivational Reserve in Alzheimer's (MoReA) study.

PubMed

Forstmeier, Simon; Maercker, Andreas

2015-11-17

Brain reserve, i.e., the ability of the brain to tolerate age- and disease-related changes in a way that cognitive function is still maintained, is assumed to be based on the lifelong training of various abilities. The Motivational Reserve in Alzheimer's (MoReA) is a longitudinal study that aims to examine motivational processes as a protective factor in mild Alzheimer's dementia (AD) and mild cognitive impairment (MCI). This paper presents the results of motivational variables, frequency of diagnoses, and prediction of global cognition as well as depression in a one-year longitudinal study. The sample consists of 64 subjects with MCI and 47 subjects with mild AD at baseline. At baseline, the physical/neurological examinations, standard clinical assessment, neuropsychological testing, and assessment of motivational variables were performed. At follow-up (FU) one year later, neuropsychological testing including cognition, functional abilities, behavioral and affective symptoms, and global clinical assessments of severity have been repeated. AD cases have lower motivational capacities as measured with a midlife motivation-related occupational score and informant-reported present motivational processes, but do not differ with regard to delay of gratification (DoG) and self-reported motivational processes. DoG and delay discounting (DD) were relatively stable during the measurement interval. However, 20 % of the MCI cases converted to mild AD at FU, and 17 % of the mild AD cases converted to moderate AD. The rate of depression of Alzheimer's disease was 9 at baseline and 21 % at FU, and the rate of apathy was 7 and 14 %, respectively. Global cognition at FU was mainly predicted by baseline global cognition but also by one of the motivational variables (scenario test). Depression at FU was predicted mainly by two motivational variables (self-reported and informant-reported motivational processes). This research might inform motivation-related strategies for prevention and early intervention with older people or people at risk for AD.

A global map of mangrove forest soil carbon at 30 m spatial resolution

NASA Astrophysics Data System (ADS)

Sanderman, Jonathan; Hengl, Tomislav; Fiske, Greg; Solvik, Kylen; Adame, Maria Fernanda; Benson, Lisa; Bukoski, Jacob J.; Carnell, Paul; Cifuentes-Jara, Miguel; Donato, Daniel; Duncan, Clare; Eid, Ebrahem M.; Ermgassen, Philine zu; Ewers Lewis, Carolyn J.; Macreadie, Peter I.; Glass, Leah; Gress, Selena; Jardine, Sunny L.; Jones, Trevor G.; Ndemem Nsombo, Eugéne; Mizanur Rahman, Md; Sanders, Christian J.; Spalding, Mark; Landis, Emily

2018-05-01

With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m‑3). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86–729 Mg C ha‑1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30–122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products from this work are intended to serve nations seeking to include mangrove habitats in payment-for- ecosystem services projects and in designing effective mangrove conservation strategies.

Essential ocean variables for global sustained observations of biodiversity and ecosystem changes.

PubMed

Miloslavich, Patricia; Bax, Nicholas J; Simmons, Samantha E; Klein, Eduardo; Appeltans, Ward; Aburto-Oropeza, Octavio; Andersen Garcia, Melissa; Batten, Sonia D; Benedetti-Cecchi, Lisandro; Checkley, David M; Chiba, Sanae; Duffy, J Emmett; Dunn, Daniel C; Fischer, Albert; Gunn, John; Kudela, Raphael; Marsac, Francis; Muller-Karger, Frank E; Obura, David; Shin, Yunne-Jai

2018-04-05

Sustained observations of marine biodiversity and ecosystems focused on specific conservation and management problems are needed around the world to effectively mitigate or manage changes resulting from anthropogenic pressures. These observations, while complex and expensive, are required by the international scientific, governance and policy communities to provide baselines against which the effects of human pressures and climate change may be measured and reported, and resources allocated to implement solutions. To identify biological and ecological essential ocean variables (EOVs) for implementation within a global ocean observing system that is relevant for science, informs society, and technologically feasible, we used a driver-pressure-state-impact-response (DPSIR) model. We (1) examined relevant international agreements to identify societal drivers and pressures on marine resources and ecosystems, (2) evaluated the temporal and spatial scales of variables measured by 100+ observing programs, and (3) analysed the impact and scalability of these variables and how they contribute to address societal and scientific issues. EOVs were related to the status of ecosystem components (phytoplankton and zooplankton biomass and diversity, and abundance and distribution of fish, marine turtles, birds and mammals), and to the extent and health of ecosystems (cover and composition of hard coral, seagrass, mangrove and macroalgal canopy). Benthic invertebrate abundance and distribution and microbe diversity and biomass were identified as emerging EOVs to be developed based on emerging requirements and new technologies. The temporal scale at which any shifts in biological systems will be detected will vary across the EOVs, the properties being monitored and the length of the existing time-series. Global implementation to deliver useful products will require collaboration of the scientific and policy sectors and a significant commitment to improve human and infrastructure capacity across the globe, including the development of new, more automated observing technologies, and encouraging the application of international standards and best practices. © 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Assessing the Monthly Averaged Variability of TOA Fluxes from CERES using EBAF, ERBE-like and FLASHFlux Data From 2001 to Present

NASA Astrophysics Data System (ADS)

Stackhouse, Paul; Wong, Takmeng; Kratz, David; Gupta, Shashi; Wiber, Anne; Edwards, Anne

2010-05-01

The FLASHFlux (Fast Longwave and Shortwave radiative Fluxes from CERES and MODIS) project derives daily averaged gridded top-of-atmosphere (TOA) and surface radiative fluxes within one week of observation. Production of CERES based TOA and surface fluxes is achieved by using the latest CERES calibration that is assumed constant in time and by making simplifying assumptions in the computation of time and space averaged quantities. Together these assumptions result in approximately a 1% increase in the uncertainty for FLASHFlux products over CERES. Analysis has clearly demonstrated that the global-annual mean outgoing longwave radiation shows a decrease of ~0.75 Wm-2, from 2007 to 2008, while the global-annual mean reflected shortwave radiation shows a decrease of 0.14 Wm-2 over that same period. Thus, the combined longwave and shortwave changes have resulted in an increase of ~0.89 Wm-2 in net radiation into the Earth climate system in 2008. A time series of TOA fluxes was constructed from CERES EBAF, CERES ERBE-like and FLASHFLUX. Relative to this multi-dataset average from 2001 to 2008, the 2008 global-annual mean anomalies are -0.54/-0.26/+0.80 Wm-2, respectively, for the longwave/shortwave/net radiation. These flux values, which were published in the NOAA 2008 State of the Climate Report, are within their corresponding 2-sigma interannual variabilities for this period. This paper extends these results through 2009, where the net flux is observed to recover. The TOA LW variability is also compared to AIRS OLR showing excellent agreement in the anomalies. The variability appears very well correlated to the to the 2007-2009 La Nina/El Nino cycles, which altered the global distribution of clouds, total column water vapor and temperature. Reassessments of these results are expected when newer Clouds and the Earth's Radiant Energy System (CERES) data are released.

Routine Clinical Quantitative Rest Stress Myocardial Perfusion for Managing Coronary Artery Disease: Clinical Relevance of Test-Retest Variability.

PubMed

Kitkungvan, Danai; Johnson, Nils P; Roby, Amanda E; Patel, Monika B; Kirkeeide, Richard; Gould, K Lance

2017-05-01

Positron emission tomography (PET) quantifies stress myocardial perfusion (in cc/min/g) and coronary flow reserve to guide noninvasively the management of coronary artery disease. This study determined their test-retest precision within minutes and daily biological variability essential for bounding clinical decision-making or risk stratification based on low flow ischemic thresholds or follow-up changes. Randomized trials of fractional flow reserve-guided percutaneous coronary interventions established an objective, quantitative, outcomes-driven standard of physiological stenosis severity. However, pressure-derived fractional flow reserve requires invasive coronary angiogram and was originally validated by comparison to noninvasive PET. The time course and test-retest precision of serial quantitative rest-rest and stress-stress global myocardial perfusion by PET within minutes and days apart in the same patient were compared in 120 volunteers undergoing serial 708 quantitative PET perfusion scans using rubidium 82 (Rb-82) and dipyridamole stress with a 2-dimensional PET-computed tomography scanner (GE DST 16) and University of Texas HeartSee software with our validated perfusion model. Test-retest methodological precision (coefficient of variance) for serial quantitative global myocardial perfusion minutes apart is ±10% (mean ΔSD at rest ±0.09, at stress ±0.23 cc/min/g) and for days apart is ±21% (mean ΔSD at rest ±0.2, at stress ±0.46 cc/min/g) reflecting added biological variability. Global myocardial perfusion at 8 min after 4-min dipyridamole infusion is 10% higher than at standard 4 min after dipyridamole. Test-retest methodological precision of global PET myocardial perfusion by serial rest or stress PET minutes apart is ±10%. Day-to-different-day biological plus methodological variability is ±21%, thereby establishing boundaries of variability on physiological severity to guide or follow coronary artery disease management. Maximum stress increases perfusion and coronary flow reserve, thereby reducing potentially falsely low values mimicking ischemia. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Interannual variability of terrestrial NEP and its attributions to carbon uptake amplitude and period

NASA Astrophysics Data System (ADS)

Niu, S.

2015-12-01

Earth system exhibits strong interannual variability (IAV) in the global carbon cycle as reflected in the year-to-year anomalies of the atmospheric CO2 concentration. Although various analyses suggested that land ecosystems contribute mostly to the IAV of atmospheric CO2 concentration, processes leading to the IAV in the terrestrial carbon (C) cycle are far from clear and hinder our effort in predicting the IAV of global C cycle. Previous studies on IAV of global C cycle have focused on the regulation of climatic variables in tropical or semiarid areas, but generated inconsistent conclusions. Using long-term eddy-flux measurements of net ecosystem production (NEP), atmospheric CO2 inversion NEP, and the MODIS-derived gross primary production (GPP), we demonstrate that seasonal carbon uptake amplitude (CUA) and period (CUP) are two key processes that control the IAV in the terrestrial C cycle. The two processes together explain 78% of the variations in the IAV in eddy covariance NEP, 70% in global atmospheric inversed NEP, and 53% in the IAV of GPP. Moreover, the three lines of evidence consistently show that variability in CUA is much more important than that of CUP in determining the variation of NEP at most eddy-flux sites, and most grids of global NEP and GPP. Our results suggest that the maximum carbon uptake potential in the peak-growing season is a determinant process of global C cycle internnual variability and carbon uptake period may play less important role than previous expectations. This study uncovers the most parsimonious, proximate processes underlying the IAV in global C cycle of the Earth system. Future research is needed to identify how climate factors affect the IAV in terrestrial C cycle through their influence on CUA and CUP.

Global Contexts for Learning: Exploring the Relationship between Low-Context Online Learning and High-Context Learners

ERIC Educational Resources Information Center

Westbrook, Timothy Paul

2014-01-01

Current research on culture and distance education suggests that cultural variables influence student success online. When online courses are writing-based, they may provide easy information dissemination; however, the low-context medium may restrict the learning experience and class dynamic due to the lack of nonverbal communication. Students who…

Spread and global population structure of the diamondback moth Plutella xylostella (Lepidoptera: Plutellidae) and its larval parasitoids Diadegma semiclausum and Diadegma fenestrale (Hymenoptera: Ichneumonidae) based on mtDNA.

PubMed

Juric, I; Salzburger, W; Balmer, O

2017-04-01

The diamondback moth (DBM) (Plutella xylostella) is one of the main pests of brassicaceous crops worldwide and shows resistance against a wide range of synthetic insecticides incurring millions of dollars in control costs every year. The DBM is a prime example of the introduction of an exotic species as a consequence of globalization. In this study we analyzed the genetic population structure of the DBM and two of its parasitic wasps, Diadegma semiclausum and Diadegma fenestrale, based on mitochondrial DNA sequences. We analyzed DBM samples from 13 regions worldwide (n = 278), and samples of the two wasp species from six European and African countries (n = 131), in an attempt to reconstruct the geographic origin and phylogeography of the DBM and its two parasitic wasps. We found high variability in COI sequences in the diamondback moth. Haplotype analysis showed three distinct genetic clusters, one of which could represent a cryptic species. Mismatch analysis confirmed the hypothesized recent spread of diamondback moths in North America, Australia and New Zealand. The highest genetic variability was found in African DBM samples. Our data corroborate prior claims of Africa as the most probable origin of the species but cannot preclude Asia as an alternative. No genetic variability was found in the two Diadegma species. The lack of variability in both wasp species suggests a very recent spread of bottlenecked populations, possibly facilitated by their use as biocontrol agents. Our data thus also contain no signals of host-parasitoid co-evolution.

Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus

PubMed Central

Flombaum, Pedro; Gallegos, José L.; Gordillo, Rodolfo A.; Rincón, José; Zabala, Lina L.; Jiao, Nianzhi; Karl, David M.; Li, William K. W.; Lomas, Michael W.; Veneziano, Daniele; Vera, Carolina S.; Vrugt, Jasper A.; Martiny, Adam C.

2013-01-01

The Cyanobacteria Prochlorococcus and Synechococcus account for a substantial fraction of marine primary production. Here, we present quantitative niche models for these lineages that assess present and future global abundances and distributions. These niche models are the result of neural network, nonparametric, and parametric analyses, and they rely on >35,000 discrete observations from all major ocean regions. The models assess cell abundance based on temperature and photosynthetically active radiation, but the individual responses to these environmental variables differ for each lineage. The models estimate global biogeographic patterns and seasonal variability of cell abundance, with maxima in the warm oligotrophic gyres of the Indian and the western Pacific Oceans and minima at higher latitudes. The annual mean global abundances of Prochlorococcus and Synechococcus are 2.9 ± 0.1 × 1027 and 7.0 ± 0.3 × 1026 cells, respectively. Using projections of sea surface temperature as a result of increased concentration of greenhouse gases at the end of the 21st century, our niche models projected increases in cell numbers of 29% and 14% for Prochlorococcus and Synechococcus, respectively. The changes are geographically uneven but include an increase in area. Thus, our global niche models suggest that oceanic microbial communities will experience complex changes as a result of projected future climate conditions. Because of the high abundances and contributions to primary production of Prochlorococcus and Synechococcus, these changes may have large impacts on ocean ecosystems and biogeochemical cycles. PMID:23703908

On the nature of the variability in the Martian thermospheric mass density: Results from the Mars Global Surveyor Electron Reflectometer

NASA Astrophysics Data System (ADS)

England, S.; Lillis, R. J.

2011-12-01

Knowledge of Mars' thermospheric mass density (~120--200 km altitude) is important for understanding the current state and evolution of the Martian atmosphere and for spacecraft such as the upcoming MAVEN mission that will fly through this region every orbit. Global-scale atmospheric models have been shown thus far to do an inconsistent job of matching mass density observations at these altitudes, especially on the nightside. Thus there is a clear need for a data-driven estimate of the mass density in this region. Given the wide range of conditions and locations over which these must be defined, the dataset of thermospheric mass densities derived from energy and angular distributions of super-thermal electrons measured by the MAG/ER experiment on Mars Global Surveyor, spanning 4 full Martian years, is an extremely valuable resource that can be used to enhance our prediction of these densities beyond what is given by such global-scale models. Here we present an empirical model of the thermospheric density structure based on the MAG/ER dataset. Using this new model, we assess the global-scale response of the thermosphere to dust storms in the lower atmosphere and show that this varies with latitude. Further, we examine the short- and longer-term variability of the thermospheric density and show that it exhibits a complex behavior with latitude and season that is indicative of both atmospheric conditions at lower altitudes and possible lower atmosphere wave sources.

Global views of energetic particle precipitation and their sources: Combining large-scale models with observations during the 21-22 January 2005 magnetic storm (Invited)

NASA Astrophysics Data System (ADS)

Kozyra, J. U.; Brandt, P. C.; Cattell, C. A.; Clilverd, M.; de Zeeuw, D.; Evans, D. S.; Fang, X.; Frey, H. U.; Kavanagh, A. J.; Liemohn, M. W.; Lu, G.; Mende, S. B.; Paxton, L. J.; Ridley, A. J.; Rodger, C. J.; Soraas, F.

2010-12-01

Energetic ions and electrons that precipitate into the upper atmosphere from sources throughout geospace carry the influences of space weather disturbances deeper into the atmosphere, possibly contributing to climate variability. The three-dimensional atmospheric effects of these precipitating particles are a function of the energy and species of the particles, lifetimes of reactive species generated during collisions in the atmosphere, the nature of the driving space weather disturbance, and the large-scale transport properties (meteorology) of the atmosphere in the region of impact. Unraveling the features of system-level coupling between solar magnetic variability, space weather and stratospheric dynamics requires a global view of the precipitation, along with its temporal and spatial variation. However, observations of particle precipitation at the system level are sparse and incomplete requiring they be combined with other observations and with large-scale models to provide the global context that is needed to accelerate progress. We compare satellite and ground-based observations of geospace conditions and energetic precipitation (at ring current, radiation belt and auroral energies) to a simulation of the geospace environment during 21-22 January 2005 by the BATS-R-US MHD model coupled with a self-consistent ring current solution. The aim is to explore the extent to which regions of particle precipitation track global magnetic field distortions and ways in which global models enhance our understanding of linkages between solar wind drivers and evolution of energetic particle precipitation.

Evaluation of Global Photosynthesis and BVOC Emission Covariance with Climate in NASA ModelE2-Y

NASA Astrophysics Data System (ADS)

Unger, N.

2012-12-01

Terrestrial gross primary productivity (GPP), a measure of the total amount of CO2 removed from the atmosphere every year to fuel photosynthesis, is the largest global carbon flux. GPP is vital for human welfare as the basis for food and fiber, and provides the crucial ecosystem service of reducing the accumulation of fossil fuel CO2 in the atmosphere. Land plants emit a significant fraction of the assimilated carbon back to the atmosphere in the form of biogenic volatile organic compounds (BVOCs). Isoprene is the dominant BVOC emission with an estimated global source of 200-660 TgC/yr. Global monoterpene emission estimates range from 30-130 TgC/yr. BVOC photochemical oxidation exerts a profound impact on the distribution and variability of the short-lived climate forcers: ozone, biogenic secondary organic aerosol and methane. Here, we apply multiple observational datasets from a suite of platforms to evaluate an updated global chemistry-climate model that is coupled to a new vegetation biophysics scheme incorporating photosynthesis-dependent BVOC emissions (NASA ModelE2-Y). A fixed vegetation structure dataset based on 8 plant functional types and prescribed phenology including crop planting and harvesting gives GPP of 128 PgC/yr and a global isoprene source of 200TgC/yr. The model GPP captures 85% of the annual average zonal mean variability in a FLUXNET-derived global dataset that was generated by data orientated diagnostic upscaling. We assess model BVOC emission climatology against a comprehensive database of campaign-average above canopy flux measurements and surface concentrations of isoprene and monoterpene collected between 1995-2010 across a wide range of ecosystem types, regions and seasons (> 25 flux estimates; > 22 surface concentration values). We evaluate the diurnal, seasonal and interannual integrity of the model BVOC variability against 9 sites for isoprene and 4 sites for monoterpene. The model captures ~60% of the variability in the time-dependent fluxes across a broad range of different ecosystem types. In tropical ecosystems, the model simulates the campaign-average diurnal cycle with remarkable fidelity (root-mean-square error = 0.20 mgC/m2/hr; normalized mean bias = -5%). The model underpredicts in broadleaf deciduous ecosystems in the United States and Europe. We probe the GPP and BVOC emission covariance with climate in tropical, temperate and boreal ecosystems, and the GPP-HCHO correlation using fire-free HCHO columns from OMI and SCIAMACHY 2005-2008.

Synchronization of a Josephson junction array in terms of global variables

NASA Astrophysics Data System (ADS)

Vlasov, Vladimir; Pikovsky, Arkady

2013-08-01

We consider an array of Josephson junctions with a common LCR load. Application of the Watanabe-Strogatz approach [Physica DPDNPDT0167-278910.1016/0167-2789(94)90196-1 74, 197 (1994)] allows us to formulate the dynamics of the array via the global variables only. For identical junctions this is a finite set of equations, analysis of which reveals the regions of bistability of the synchronous and asynchronous states. For disordered arrays with distributed parameters of the junctions, the problem is formulated as an integro-differential equation for the global variables; here stability of the asynchronous states and the properties of the transition synchrony-asynchrony are established numerically.

CLIVAR-GSOP/GODAE Ocean Synthesis Inter-Comparison of Global Air-Sea Fluxes From Ocean and Coupled Reanalyses

NASA Astrophysics Data System (ADS)

Valdivieso, Maria

2014-05-01

The GODAE OceanView and CLIVAR-GSOP ocean synthesis program has been assessing the degree of consistency between global air-sea flux data sets obtained from ocean or coupled reanalyses (Valdivieso et al., 2014). So far, fifteen global air-sea heat flux products obtained from ocean or coupled reanalyses have been examined: seven are from low-resolution ocean reanalyses (BOM PEODAS, ECMWF ORAS4, JMA/MRI MOVEG2, JMA/MRI MOVECORE, Hamburg Univ. GECCO2, JPL ECCOv4, and NCEP GODAS), five are from eddy-permitting ocean reanalyses developed as part of the EU GMES MyOcean program (Mercator GLORYS2v1, Reading Univ. UR025.3, UR025.4, UKMO GloSea5, and CMCC C-GLORS), and the remaining three are couple reanalyses based on coupled climate models (JMA/MRI MOVE-C, GFDL ECDA and NCEP CFSR). The global heat closure in the products over the period 1993-2009 spanned by all data sets is presented in comparison with observational and atmospheric reanalysis estimates. Then, global maps of ensemble spread in the seasonal cycle, and of the Signal to Noise Ratio of interannual flux variability over the 17-yr common period are shown to illustrate the consistency between the products. We have also studied regional variability in the products, particularly at the OceanSITES project locations (such as, for instance, the TAO/TRITON and PIRATA arrays in the Tropical Pacific and Atlantic, respectively). Comparisons are being made with other products such as OAFlux latent and sensible heat fluxes (Yu et al., 2008) combined with ISCCP satellite-based radiation (Zhang et al., 2004), the ship-based NOC2.0 product (Berry and Kent, 2009), the Large and Yeager (2009) hybrid flux dataset CORE.2, and two atmospheric reanalysis products, the ECMWF ERA-Interim reanalysis (referred to as ERAi, Dee et al., 2011) and the NCEP/DOE reanalysis R2 (referred to as NCEP-R2, Kanamitsu et al., 2002). Preliminary comparisons with the observational flux products from OceanSITES are also underway. References Berry, D.I. and E.C. Kent (2009), A New Air-Sea Interaction Gridded Dataset from ICOADS with Uncertainty Estimates. Bull. Amer. Meteor. Soc 90(5), 645-656. doi: 10.1175/2008BAMS2639.1. Dee, D. P. et al. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553-597. doi: 10.1002/qj.828. Kanamitsu M., Ebitsuzaki W., Woolen J., Yang S.K., Hnilo J.J., Fiorino M., Potter G. (2002), NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83:1631-1643. Large, W. and Yeager, S. (2009), The global climatology of an interannually varying air-sea flux data set. Clim. Dynamics, Volume 33, pp 341-364 Valdivieso, M. and co-authors (2014): Heat fluxes from ocean and coupled reanalyses, Clivar Exchanges. Issue 64. Yu, L., X. Jin, and R. A. Weller (2008), Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and Sensible Heat Fluxes, Ocean Evaporation, and Related Surface Meteorological Variables. Technical Report OAFlux Project (OA2008-01), Woods Hole Oceanographic Institution. Zhang, Y., WB Rossow, AA Lacis, V Oinas, MI Mishchenk (2004), Calculation of radiative fluxes from the surface to top of atmsophere based on ISCCP and other global data sets. Journal of Geophysical Research: Atmospheres (1984-2012) 109 (D19).

Precipitation variability on global pasturelands may affect food security in livestock-dependent regions

NASA Astrophysics Data System (ADS)

Sloat, L.; Gerber, J. S.; Samberg, L. H.; Smith, W. K.; West, P. C.; Herrero, M.; Brendan, P.; Cecile, G.; Katharina, W.; Smith, W. K.

2016-12-01

The need to feed an increasing number of people while maintaining biodiversity and ecosystem services is one of the key challenges currently facing humanity. Livestock systems are likely to be a crucial piece of this puzzle, as urbanization and changing diets in much of the world lead to increases in global meat consumption. This predicted increase in global demand for livestock products will challenge the ability of pastures and rangelands to maintain or increase their productivity. The majority of people that depend on animal production for food security do so through grazing and herding on natural rangelands, and these systems make a significant contribution to global production of meat and milk. The vegetation dynamics of natural forage are highly dependent on climate, and subject to disruption with changes in climate and climate variability. Precipitation heterogeneity has been linked to the ecosystem dynamics of grazing lands through impacts on livestock carrying capacity and grassland degradation potential. Additionally, changes in precipitation variability are linked to the increased incidence of extreme events (e.g. droughts, floods) that negatively impact food production and food security. Here, we use the inter-annual coefficient of variation (CV) of precipitation as a metric to assess climate risk on global pastures. Comparisons of global satellite measures of vegetation greenness to climate reveal that the CV of precipitation is negatively related to mean annual NDVI, such that areas with low year-to-year precipitation variability have the highest measures of vegetation greenness, and vice versa. Furthermore, areas with high CV of precipitation support lower livestock densities and produce less meat. A sliding window analysis of changes in CV of precipitation over the last century shows that, overall, precipitation variability is increasing in global pasture areas, although global maps reveal a patchwork of both positive and negative changes. We use this information to identify regions in which changes in the variability of precipitation may already be affecting the ability of grazing systems to support intensified livestock production, and assess the potential impacts of those changes on pasture productivity.

Oscillations and trends of river discharge in the southern Central Andes and linkages with climate variability

NASA Astrophysics Data System (ADS)

Castino, Fabiana; Bookhagen, Bodo; Strecker, Manfred R.

2017-12-01

This study analyzes the discharge variability of small to medium drainage basins (102-104 km2) in the southern Central Andes of NW Argentina. The Hilbert-Huang Transform (HHT) was applied to evaluate non-stationary oscillatory modes of variability and trends, based on four time series of monthly-normalized discharge anomaly between 1940 and 2015. Statistically significant trends reveal increasing discharge during the past decades and document an intensification of the hydrological cycle during this period. An Ensemble Empirical Mode Decomposition (EEMD) analysis revealed that discharge variability in this region can be best described by five quasi-periodic statistically significant oscillatory modes, with mean periods varying from 1 to ∼20 y. Moreover, we show that discharge variability is most likely linked to the phases of the Pacific Decadal Oscillation (PDO) at multi-decadal timescales (∼20 y) and, to a lesser degree, to the Tropical South Atlantic SST anomaly (TSA) variability at shorter timescales (∼2-5 y). Previous studies highlighted a rapid increase in discharge in the southern Central Andes during the 1970s, inferred to have been associated with the global 1976-77 climate shift. Our results suggest that the rapid discharge increase in the NW Argentine Andes coincides with the periodic enhancement of discharge, which is mainly linked to a negative to positive transition of the PDO phase and TSA variability associated with a long-term increasing trend. We therefore suggest that variations in discharge in this region are largely driven by both natural variability and the effects of global climate change. We furthermore posit that the links between atmospheric and hydrologic processes result from a combination of forcings that operate on different spatiotemporal scales.

Global Water Resources Under Future Changes: Toward an Improved Estimation

NASA Astrophysics Data System (ADS)

Islam, M.; Agata, Y.; Hanasaki, N.; Kanae, S.; Oki, T.

2005-05-01

Global water resources availability in the 21st century is going to be an important concern. Despite its international recognition, however, until now there are very limited global estimates of water resources, which considered the geographical linkage between water supply and demand, defined by runoff and its passage through river network. The available studies are again insufficient due to reasons like different approaches in defining water scarcity, simply based on annual average figures without considering the inter-annual or seasonal variability, absence of the inclusion of virtual water trading, etc. In this study, global water resources under future climate change associated with several socio-economic factors were estimated varying over both temporal and spatial scale. Global runoff data was derived from several land surface models under the GSWP2 (Global Soil Wetness Project) project, which was further processed through TRIP (Total Runoff Integrated Pathways) river routing model to produce a 0.5x0.5 degree grid based figure. Water abstraction was estimated for the same spatial resolution for three sectors as domestic, industrial and agriculture. GCM outputs from CCSR and MRI were collected to predict the runoff changes. Socio-economic factors like population and GDP growth, affected mostly the demand part. Instead of simply looking at annual figures, monthly figures for both supply and demand was considered. For an average year, such a seasonal variability can affect the crop yield significantly. In other case, inter-annual variability of runoff can cause for an absolute drought condition. To account for vulnerabilities of a region to future changes, both inter-annual and seasonal effects were thus considered. At present, the study assumed the future agricultural water uses to be unchanged under climatic changes. In this connection, EPIC model is underway to use for estimating future agricultural water demand under climatic changes on a monthly basis. From the estimation of present stress level (withdrawal to resource ratio), the months between January to May was found to have the highest number of population above water stress level, while the months between June to August having lower population in stress. The regions suffering from high seasonal variability are those of Asian monsoon zone, south-central Africa and central-east part of South America. Inter-annual variability, on the other hand, is dominant mostly along the Middle-east or Sahara regions and the western part of South America and Latin America. Virtual water trading among countries was estimated on per capita basis. It shows that many Middle east countries are able to compensate their water stress significantly through virtual water trading. The overall effect of climate change on lowering of river runoff mostly affected Europe, southern part of China and Latin America. India or Central Africa have better runoff availability under changing climate, but still subject to a higher water stress because of socio-economic factors like high population growth and expected increase in rate of water uses. Decrease in population as well as saturation level of maximum water uses along most European countries, on the contrary, relaxed the pressure of lowering river runoff, causing no significant change in future stress.

Correlation Between Fracture Network Properties and Stress Variability in Geological Media

NASA Astrophysics Data System (ADS)

Lei, Qinghua; Gao, Ke

2018-05-01

We quantitatively investigate the stress variability in fractured geological media under tectonic stresses. The fracture systems studied include synthetic fracture networks following power law length scaling and natural fracture patterns based on outcrop mapping. The stress field is derived from a finite-discrete element model, and its variability is analyzed using a set of mathematical formulations that honor the tensorial nature of stress data. We show that local stress perturbation, quantified by the Euclidean distance of a local stress tensor to the mean stress tensor, has a positive, linear correlation with local fracture intensity, defined as the total fracture length per unit area within a local sampling window. We also evaluate the stress dispersion of the entire stress field using the effective variance, that is, a scalar-valued measure of the overall stress variability. The results show that a well-connected fracture system under a critically stressed state exhibits strong local and global stress variabilities.

An experimental test of the role of environmental temperature variability on ectotherm molecular, physiological and life-history traits: implications for global warming.

PubMed

Folguera, Guillermo; Bastías, Daniel A; Caers, Jelle; Rojas, José M; Piulachs, Maria-Dolors; Bellés, Xavier; Bozinovic, Francisco

2011-07-01

Global climate change is one of the greatest threats to biodiversity; one of the most important effects is the increase in the mean earth surface temperature. However, another but poorly studied main characteristic of global change appears to be an increase in temperature variability. Most of the current analyses of global change have focused on mean values, paying less attention to the role of the fluctuations of environmental variables. We experimentally tested the effects of environmental temperature variability on characteristics associated to the fitness (body mass balance, growth rate, and survival), metabolic rate (VCO(2)) and molecular traits (heat shock protein expression, Hsp70), in an ectotherm, the terrestrial woodlouse Porcellio laevis. Our general hypotheses are that higher values of thermal amplitude may directly affect life-history traits, increasing metabolic cost and stress responses. At first, results supported our hypotheses showing a diversity of responses among characters to the experimental thermal treatments. We emphasize that knowledge about the cellular and physiological mechanisms by which animals cope with environmental changes is essential to understand the impact of mean climatic change and variability. Also, we consider that the studies that only incorporate only mean temperatures to predict the life-history, ecological and evolutionary impact of global temperature changes present important problems to predict the diversity of responses of the organism. This is because the analysis ignores the complexity and details of the molecular and physiological processes by which animals cope with environmental variability, as well as the life-history and demographic consequences of such variability. Copyright © 2011 Elsevier Inc. All rights reserved.

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

NASA Technical Reports Server (NTRS)

Zeng, Fanwei; Collatz, George James; Pinzon, Jorge E.; Ivanoff, Alvaro

2013-01-01

Satellite observations of surface reflected solar radiation contain informationabout variability in the absorption of solar radiation by vegetation. Understanding thecauses of variability is important for models that use these data to drive land surface fluxesor for benchmarking prognostic vegetation models. Here we evaluated the interannualvariability in the new 30.5-year long global satellite-derived surface reflectance index data,Global Inventory Modeling and Mapping Studies normalized difference vegetation index(GIMMS NDVI3g). Pearsons correlation and multiple linear stepwise regression analyseswere applied to quantify the NDVI interannual variability driven by climate anomalies, andto evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVIsignal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systemswhere in some regions and seasons 40 of the NDVI variance could be explained byprecipitation anomalies. Temperature correlations were strongest in northern mid- to-high-latitudes in the spring and early summer where up to 70 of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America,winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wetseason precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

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.

Metabolic power and energetic costs of professional Australian Football match-play.

PubMed

Coutts, Aaron J; Kempton, Thomas; Sullivan, Courtney; Bilsborough, Johann; Cordy, Justin; Rampinini, Ermanno

2015-03-01

To compare the metabolic power demands between positional groups, and examine temporal changes in these parameters during Australian Football match-play. Longitudinal observational study. Global positioning system data were collected from 39 Australian Football players from the same club during 19 Australian Football League competition games over two seasons. A total of 342 complete match samples were obtained for analysis. Players were categorised into one of six positional groups: tall backs, mobile backs, midfielders, tall forwards, mobile forwards and rucks. Instantaneous raw velocity data obtained from the global positioning system units was exported to a customised spreadsheet which provided estimations of both speed-based (e.g. total and high-speed running distance) and derived metabolic power and energy expenditure variables (e.g. average metabolic power, high-power distance, total energy expenditure). There were significant differences between positional groups for both speed-based and metabolic power indices, with midfielders covering more total and high-speed distance, as well as greater average and overall energy expenditure compared to other positions (all p<0.001). There were reductions in total, high-speed, and high-power distance, as well as average metabolic power throughout the match (all p<0.001). Positional differences exist for both metabolic power and traditional running based variables. Generally, midfielders, followed by mobile forwards and mobile backs had greater activity profiles compared to other position groups. We observed that the reductions in most metabolic power variables during the course of the match are comparable to traditional running based metrics. This study demonstrates that metabolic power data may contribute to our understanding of the physical demands of Australian Football. Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Global assessment of surfing conditions: seasonal, interannual and long-term variability

NASA Astrophysics Data System (ADS)

Espejo, A.; Losada, I.; Mendez, F.

2012-12-01

International surfing destinations owe a great debt to specific combinations of wind-wave, thermal conditions and local bathymetry. As surf quality depends on a vast number of geophysical variables, a multivariable standardized index on the basis of expert judgment is proposed to analyze surf resource in a worldwide domain. Data needed is obtained by combining several datasets (reanalyses): 60-year satellite-calibrated spectral wave hindcast (GOW, WaveWatchIII), wind fields from NCEP/NCAR, global sea surface temperature from ERSST.v3b, and global tides from TPXO7.1. A summary of the global surf resource is presented, which highlights the high degree of variability in surfable events. According to general atmospheric circulation, results show that west facing low to middle latitude coasts are more suitable for surfing, especially those in Southern Hemisphere. Month to month analysis reveals strong seasonal changes in the occurrence of surfable events, enhancing those in North Atlantic or North Pacific. Interannual variability is investigated by comparing occurrence values with global and regional climate patterns showing a great influence at both, global and regional scales. Analysis of long term trends shows an increase in the probability of surfable events over the west facing coasts on the planet (i.e. + 30 hours/year in California). The resulting maps provide useful information for surfers and surf related stakeholders, coastal planning, education, and basic research.; Figure 1. Global distribution of medium quality (a) and high quality surf conditions probability (b).

Global intensification in observed short-duration rainfall extremes

NASA Astrophysics Data System (ADS)

Fowler, H. J.; Lewis, E.; Guerreiro, S.; Blenkinsop, S.; Barbero, R.; Westra, S.; Lenderink, G.; Li, X.

2017-12-01

Extreme rainfall events are expected to intensify with a warming climate and this is currently driving extensive research. While daily rainfall extremes are widely thought to have increased globally in recent decades, changes in rainfall extremes on shorter timescales, often associated with flash flooding, have not been documented at global scale due to surface observational limitations and the lack of a global sub-daily rainfall database. The access to and use of such data remains a challenge. For the first time, we have synthesized across multiple data sources providing gauge-based sub-daily rainfall observations across the globe over the last 6 decades. This forms part of the INTENSE project (part of the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes' and the Global Water and Energy Exchanges (GEWEX) Hydroclimate Project cross-cut on sub-daily rainfall). A set of global hydroclimatic indices have been produced based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. This will provide a unique global data resource on sub-daily precipitation whose derived indices will be freely available to the wider scientific community. Because of the physical connection between global warming and the moisture budget, we also sought to infer long-term changes in sub-daily rainfall extremes contingent on global mean temperature. Whereas the potential influence of global warming is uncertain at regional scales, where natural variability dominates, aggregating surface stations across parts of the world may increase the global warming-induced signal. Changes in terms of annual maximum rainfall across various resolutions ranging from 1-h to 24-h are presented and discussed.

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

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

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Basin-scale heterogeneity in Antarctic precipitation and its impact on surface mass variability

DOE PAGES

Fyke, Jeremy; Lenaerts, Jan T. M.; Wang, Hailong

2017-11-15

Annually averaged precipitation in the form of snow, the dominant term of the Antarctic Ice Sheet surface mass balance, displays large spatial and temporal variability. Here we present an analysis of spatial patterns of regional Antarctic precipitation variability and their impact on integrated Antarctic surface mass balance variability simulated as part of a preindustrial 1800-year global, fully coupled Community Earth System Model simulation. Correlation and composite analyses based on this output allow for a robust exploration of Antarctic precipitation variability. We identify statistically significant relationships between precipitation patterns across Antarctica that are corroborated by climate reanalyses, regional modeling and icemore » core records. These patterns are driven by variability in large-scale atmospheric moisture transport, which itself is characterized by decadal- to centennial-scale oscillations around the long-term mean. We suggest that this heterogeneity in Antarctic precipitation variability has a dampening effect on overall Antarctic surface mass balance variability, with implications for regulation of Antarctic-sourced sea level variability, detection of an emergent anthropogenic signal in Antarctic mass trends and identification of Antarctic mass loss accelerations.« less

Global Warming: Discussion for EOS Science Writers Workshop

NASA Technical Reports Server (NTRS)

Hansen, James E

1999-01-01

The existence of global warming this century is no longer an issue of scientific debate. But there are many important questions about the nature and causes of long-term climate change, th roles of nature and human-made climate forcings and unforced (chaotic) climate variability, the practical impacts of climate change, and what, if anything, should be done to reduce global warming, Global warming is not a uniform increase of temperature, but rather involves at complex geographically varying climate change. Understanding of global warming will require improved observations of climate change itself and the forcing factors that can lead to climate change. The NASA Terra mission and other NASA Earth Science missions will provide key measurement of climate change and climate forcings. The strategy to develop an understanding of the causes and predictability of long-term climate change must be based on combination of observations with models and analysis. The upcoming NASA missions will make important contributions to the required observations.

Initial Development of C.A.T.E.S.: A Simulation-Based Competency Assessment Instrument for Neonatal Nurse Practitioners.

PubMed

Cates, Leigh Ann; Bishop, Sheryl; Armentrout, Debra; Verklan, Terese; Arnold, Jennifer; Doughty, Cara

2015-01-01

Determine content validity of global statements and operational definitions and choose scenarios for Competency, Assessment, Technology, Education, and Simulation (C.A.T.E.S.), instrument in development to evaluate multidimensional competency of neonatal nurse practitioners (NNPs). Real-time Delphi (RTD) method to pursue four specific aims (SAs): (1) identify which cognitive, technical, or behavioral dimension of NNP competency accurately reflects each global statement; (2) map the global statements to the National Association of Neonatal Nurse Practitioners (NANNP) core competency domains; (3) define operational definitions for the novice to expert performance subscales; and (4) determine the essential scenarios to assess NNPs. Twenty-five NNPs and nurses with competency and simulation experience Main outcome variable: One hundred percent of global statements correct for competency dimension and all but two correct for NANNP domain. One hundred percent novice to expert operational definitions and eight scenarios chosen. Content validity determined for global statements and novice to expert definitions and essential scenarios chosen.

Decadal Variation's Offset of Global Warming in Recent Tropical Pacific Climate

NASA Astrophysics Data System (ADS)

Yeo, S. R.; Yeh, S. W.; Kim, K. Y.; Kim, W.

2015-12-01

Despite the increasing greenhouse gas concentration, there is no significant warming in the sea surface temperature (SST) over the tropical eastern Pacific since about 2000. This counterintuitive observation has generated substantial interest in the role of low-frequency variation over the Pacific Ocean such as Pacific Decadal Oscillation (PDO) or Interdecadal Pacific Oscillation (IPO). Therefore, it is necessary to appropriately separate low-frequency variability and global warming from SST records. Here we present three primary modes of global SST as a secular warming trend, a low-frequency variability, and a biennial oscillation through the use of novel statistical method. By analyzing temporal behavior of the three-mode, it is found that the opposite contributions of secular warming trend and cold phase of low-frequency variability since 1999 account for the warming hiatus in the tropical eastern Pacific. This result implies that the low-frequency variability modulates the manifestation of global warming signal in the tropical Pacific SST. Furthermore, if the low-frequency variability turns to a positive phase, warming in the tropical eastern Pacific will be amplified and also strong El Niño events will occur more frequently in the near future.

Sampling Analysis of Aerosol Retrievals by Single-track Spaceborne Instrument for Climate Research

NASA Astrophysics Data System (ADS)

Geogdzhayev, I. V.; Cairns, B.; Alexandrov, M. D.; Mishchenko, M. I.

2012-12-01

We examine to what extent the reduced sampling of along-track instruments such as Cloud-Aerosol LIdar with Orthogonal Polarisation (CALIOP) and Aerosol Polarimetry Sensor (APS) affects the statistical accuracy of a satellite climatology of retrieved aerosol optical thickness (AOT) by sub-sampling the retrievals from a wide-swath imaging instrument (MODerate resolution Imaging Spectroradiometer (MODIS)). Owing to its global coverage, longevity, and extensive characterization versus ground based data, the MODIS level-2 aerosol product is an instructive testbed for assessing sampling effects on climatic means derived from along-track instrument data. The advantage of using daily pixel-level aerosol retrievals from MODIS is that limitations caused by the presence of clouds are implicit in the sample, so that their seasonal and regional variations are captured coherently. However, imager data can exhibit cross-track variability of monthly global mean AOTs caused by a scattering-angle dependence. We found that single along-track values can deviate from the imager mean by 15% over land and by more than 20% over ocean. This makes it difficult to separate natural variability from viewing-geometry artifacts complicating direct comparisons of an along-track sub-sample with the full imager data. To work around this problem, we introduce "flipped-track" sampling which, by design, is statistically equivalent to along-track sampling and while closely approximating the imager in terms of angular artifacts. We show that the flipped-track variability of global monthly mean AOT is much smaller than the cross-track one for the 7-year period considered. Over the ocean flipped-track standard error is 85% less than the cross-track one (absolute values 0.0012 versus 0.0079), and over land it is about one third of the cross-track value (0.0054 versus 0.0188) on average. This allows us to attribute the difference between the two errors to the viewing-geometry artifacts and obtain an upper limit on AOT errors caused by along-track sampling. Our results show that using along-track subsets of MODIS aerosol data directly to analyze the sampling adequacy of single-track instruments can lead to false conclusions owing to the apparent enhancement of natural aerosol variability by the track-to-track artifacts. The analysis based on the statistics of the flipped-track means yields better estimates because it allows for better separation of the viewing-geometry artifacts and true natural variability. Published assessments estimate that a global AOT change of 0.01 would yield a climatically important flux change of 0.25 W/m2. Since the standard error estimates that we have obtained are comfortably below 0.01, we conclude that along-track instruments flown on a sun-synchronous orbiting platform have sufficient spatial sampling for estimating aerosol effects on climate. Since AOT is believed to be the most variable characteristic of tropospheric aerosols, our results imply that pixel-wide along-track coverage also provides adequate statistical representation of the global distribution of aerosol microphysical parameters.

Estimating heterotrophic respiration at large scales: Challenges, approaches, and next steps

DOE PAGES

Bond-Lamberty, Ben; Epron, Daniel; Harden, Jennifer; ...

2016-06-27

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of Decomposition Functional Types (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelersmore » and experimentalists to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present an example clustering analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from (but complementary to) already-existing PFTs. A similar analysis incorporating observational data could form the basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with rigorous testing of analytical results; using point measurements and realistic forcing variables to constrain process-based models; and planning by the global modeling community for decoupling decomposition from fixed site data. Lastly, these are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR.« less

Estimating heterotrophic respiration at large scales: Challenges, approaches, and next steps

USGS Publications Warehouse

Bond-Lamberty, Ben; Epron, Daniel; Harden, Jennifer W.; Harmon, Mark E.; Hoffman, Forrest; Kumar, Jitendra; McGuire, Anthony David; Vargas, Rodrigo

2016-01-01

Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic-specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of “Decomposition Functional Types” (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelers and experimentalists to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top-down expert opinion, but ultimately developed using bottom-up, data-driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present an example clustering analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from (but complementary to) already-existing PFTs. A similar analysis incorporating observational data could form the basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in-depth analyses combining open data with rigorous testing of analytical results; using point measurements and realistic forcing variables to constrain process-based models; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR.

Does climate directly influence NPP globally?

PubMed

Chu, Chengjin; Bartlett, Megan; Wang, Youshi; He, Fangliang; Weiner, Jacob; Chave, Jérôme; Sack, Lawren

2016-01-01

The need for rigorous analyses of climate impacts has never been more crucial. Current textbooks state that climate directly influences ecosystem annual net primary productivity (NPP), emphasizing the urgent need to monitor the impacts of climate change. A recent paper challenged this consensus, arguing, based on an analysis of NPP for 1247 woody plant communities across global climate gradients, that temperature and precipitation have negligible direct effects on NPP and only perhaps have indirect effects by constraining total stand biomass (Mtot ) and stand age (a). The authors of that study concluded that the length of the growing season (lgs ) might have a minor influence on NPP, an effect they considered not to be directly related to climate. In this article, we describe flaws that affected that study's conclusions and present novel analyses to disentangle the effects of stand variables and climate in determining NPP. We re-analyzed the same database to partition the direct and indirect effects of climate on NPP, using three approaches: maximum-likelihood model selection, independent-effects analysis, and structural equation modeling. These new analyses showed that about half of the global variation in NPP could be explained by Mtot combined with climate variables and supported strong and direct influences of climate independently of Mtot , both for NPP and for net biomass change averaged across the known lifetime of the stands (ABC = average biomass change). We show that lgs is an important climate variable, intrinsically correlated with, and contributing to mean annual temperature and precipitation (Tann and Pann ), all important climatic drivers of NPP. Our analyses provide guidance for statistical and mechanistic analyses of climate drivers of ecosystem processes for predictive modeling and provide novel evidence supporting the strong, direct role of climate in determining vegetation productivity at the global scale. © 2015 John Wiley & Sons Ltd.

Historical Compilation and Georeferencing of Dengue and Chikungunya outbreak data for Disease Modeling

USDA-ARS?s Scientific Manuscript database

The risk of vector-borne disease spread is increasing due to significant changes and variability in the global climate and increasing global travel and trade. Understanding the relationships between climate variability and disease outbreak patterns are critical to the design and construction of pred...

Global map of solar power production efficiency, considering micro climate factors

NASA Astrophysics Data System (ADS)

Hassanpour Adeh, E.; Higgins, C. W.

2017-12-01

Natural resources degradation and greenhouse gas emissions are creating a global crisis. Renewable energy is the most reliable option to mitigate this environmental dilemma. Abundancy of solar energy makes it highly attractive source of electricity. The existing global spatial maps of available solar energy are created with various models which consider the irradiation, latitude, cloud cover, elevation, shading and aerosols, and neglect the influence of local meteorological conditions. In this research, the influences of microclimatological variables on solar energy productivity were investigated with an in-field study at the Rabbit Hills solar arrays near Oregon State University. The local studies were extended to a global level, where global maps of solar power were produced, taking the micro climate variables into account. These variables included: temperature, relative humidity, wind speed, wind direction, solar radiation. The energy balance approach was used to synthesize the data and compute the efficiencies. The results confirmed that the solar power efficiency can be directly affected by the air temperature and wind speed.

Climatic change controls productivity variation in global grasslands

PubMed Central

Gao, Qingzhu; Zhu, Wenquan; Schwartz, Mark W.; Ganjurjav, Hasbagan; Wan, Yunfan; Qin, Xiaobo; Ma, Xin; Williamson, Matthew A.; Li, Yue

2016-01-01

Detection and identification of the impacts of climate change on ecosystems have been core issues in climate change research in recent years. In this study, we compared average annual values of the normalized difference vegetation index (NDVI) with theoretical net primary productivity (NPP) values based on temperature and precipitation to determine the effect of historic climate change on global grassland productivity from 1982 to 2011. Comparison of trends in actual productivity (NDVI) with climate-induced potential productivity showed that the trends in average productivity in nearly 40% of global grassland areas have been significantly affected by climate change. The contribution of climate change to variability in grassland productivity was 15.2–71.2% during 1982–2011. Climate change contributed significantly to long-term trends in grassland productivity mainly in North America, central Eurasia, central Africa, and Oceania; these regions will be more sensitive to future climate change impacts. The impacts of climate change on variability in grassland productivity were greater in the Western Hemisphere than the Eastern Hemisphere. Confirmation of the observed trends requires long-term controlled experiments and multi-model ensembles to reduce uncertainties and explain mechanisms. PMID:27243565

Current Climate Variability & Change

NASA Astrophysics Data System (ADS)

Diem, J.; Criswell, B.; Elliott, W. C.

2013-12-01

Current Climate Variability & Change is the ninth among a suite of ten interconnected, sequential labs that address all 39 climate-literacy concepts in the U.S. Global Change Research Program's Climate Literacy: The Essential Principles of Climate Sciences. The labs are as follows: Solar Radiation & Seasons, Stratospheric Ozone, The Troposphere, The Carbon Cycle, Global Surface Temperature, Glacial-Interglacial Cycles, Temperature Changes over the Past Millennium, Climates & Ecosystems, Current Climate Variability & Change, and Future Climate Change. All are inquiry-based, on-line products designed in a way that enables students to construct their own knowledge of a topic. Questions representative of various levels of Webb's depth of knowledge are embedded in each lab. In addition to the embedded questions, each lab has three or four essential questions related to the driving questions for the lab suite. These essential questions are presented as statements at the beginning of the material to represent the lab objectives, and then are asked at the end as questions to function as a summative assessment. For example, the Current Climate Variability & Change is built around these essential questions: (1) What has happened to the global temperature at the Earth's surface, in the middle troposphere, and in the lower stratosphere over the past several decades?; (2) What is the most likely cause of the changes in global temperature over the past several decades and what evidence is there that this is the cause?; and (3) What have been some of the clearly defined effects of the change in global temperature on the atmosphere and other spheres of the Earth system? An introductory Prezi allows the instructor to assess students' prior knowledge in relation to these questions, while also providing 'hooks' to pique their interest related to the topic. The lab begins by presenting examples of and key differences between climate variability (e.g., Mt. Pinatubo eruption) and climate change. The next section guides students through the exploration of temporal changes in global temperature from the surface to the lower stratosphere. Students discover that there has been global warming over the past several decades, and the subsequent section allows them to consider solar radiation and greenhouse gases as possible causes of this warming. Students then zoom in on different latitudinal zones to examine changes in temperature for each zone and hypothesize about why one zone may have warmed more than others. The final section, prior to the answering of the essential questions, is an examination of the following effects of the current change in temperatures: loss of sea ice; rise of sea level; loss of permafrost loss; and moistening of the atmosphere. The lab addresses 14 climate-literacy concepts and all seven climate-literacy principles through data and images that are mainly NASA products. It focuses on the satellite era of climate data; therefore, 1979 is the typical starting year for most datasets used by students. Additionally, all time-series analysis end with the latest year with full-year data availability; thus, the climate variability and trends truly are 'current.'

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2004-01-01

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

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.

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.

Clinical and functional outcomes after 2 years in the early detection and intervention for the prevention of psychosis multisite effectiveness trial.

PubMed

McFarlane, William R; Levin, Bruce; Travis, Lori; Lucas, F Lee; Lynch, Sarah; Verdi, Mary; Williams, Deanna; Adelsheim, Steven; Calkins, Roderick; Carter, Cameron S; Cornblatt, Barbara; Taylor, Stephan F; Auther, Andrea M; McFarland, Bentson; Melton, Ryan; Migliorati, Margaret; Niendam, Tara; Ragland, J Daniel; Sale, Tamara; Salvador, Melina; Spring, Elizabeth

2015-01-01

To test effectiveness of the Early Detection, Intervention, and Prevention of Psychosis Program in preventing the onset of severe psychosis and improving functioning in a national sample of at-risk youth. In a risk-based allocation study design, 337 youth (age 12-25) at risk of psychosis were assigned to treatment groups based on severity of positive symptoms. Those at clinically higher risk (CHR) or having an early first episode of psychosis (EFEP) were assigned to receive Family-aided Assertive Community Treatment (FACT); those at clinically lower risk (CLR) were assigned to receive community care. Between-groups differences on outcome variables were adjusted statistically according to regression-discontinuity procedures and evaluated using the Global Test Procedure that combined all symptom and functional measures. A total of 337 young people (mean age: 16.6) were assigned to the treatment group (CHR + EFEP, n = 250) or comparison group (CLR, n = 87). On the primary variable, positive symptoms, after 2 years FACT, were superior to community care (2 df, p < .0001) for both CHR (p = .0034) and EFEP (p < .0001) subgroups. Rates of conversion (6.3% CHR vs 2.3% CLR) and first negative event (25% CHR vs 22% CLR) were low but did not differ. FACT was superior in the Global Test (p = .0007; p = .024 for CHR and p = .0002 for EFEP, vs CLR) and in improvement in participation in work and school (p = .025). FACT is effective in improving positive, negative, disorganized and general symptoms, Global Assessment of Functioning, work and school participation and global outcome in youth at risk for, or experiencing very early, psychosis. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

Projecting the Global Distribution of the Emerging Amphibian Fungal Pathogen, Batrachochytrium dendrobatidis, Based on IPCC Climate Futures.

PubMed

Xie, Gisselle Yang; Olson, Deanna H; Blaustein, Andrew R

2016-01-01

Projected changes in climate conditions are emerging as significant risk factors to numerous species, affecting habitat conditions and community interactions. Projections suggest species range shifts in response to climate change modifying environmental suitability and is supported by observational evidence. Both pathogens and their hosts can shift ranges with climate change. We consider how climate change may influence the distribution of the emerging infectious amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), a pathogen associated with worldwide amphibian population losses. Using an expanded global Bd database and a novel modeling approach, we examined a broad set of climate metrics to model the Bd-climate niche globally and regionally, then project how climate change may influence Bd distributions. Previous research showed that Bd distribution is dependent on climatic variables, in particular temperature. We trained a machine-learning model (random forest) with the most comprehensive global compilation of Bd sampling records (~5,000 site-level records, mid-2014 summary), including 13 climatic variables. We projected future Bd environmental suitability under IPCC scenarios. The learning model was trained with combined worldwide data (non-region specific) and also separately per region (region-specific). One goal of our study was to estimate of how Bd spatial risks may change under climate change based on the best available data. Our models supported differences in Bd-climate relationships among geographic regions. We projected that Bd ranges will shift into higher latitudes and altitudes due to increased environmental suitability in those regions under predicted climate change. Specifically, our model showed a broad expansion of areas environmentally suitable for establishment of Bd on amphibian hosts in the temperate zones of the Northern Hemisphere. Our projections are useful for the development of monitoring designs in these areas, especially for sensitive species and those vulnerable to multiple threats.

A space-based climatology of diurnal MLT tidal winds, temperatures and densities from UARS wind measurements

NASA Astrophysics Data System (ADS)

Svoboda, Aaron A.; Forbes, Jeffrey M.; Miyahara, Saburo

2005-11-01

A self-consistent global tidal climatology, useful for comparing and interpreting radar observations from different locations around the globe, is created from space-based Upper Atmosphere Research Satellite (UARS) horizontal wind measurements. The climatology created includes tidal structures for horizontal winds, temperature and relative density, and is constructed by fitting local (in latitude and height) UARS wind data at 95 km to a set of basis functions called Hough mode extensions (HMEs). These basis functions are numerically computed modifications to Hough modes and are globally self-consistent in wind, temperature, and density. We first demonstrate this self-consistency with a proxy data set from the Kyushu University General Circulation Model, and then use a linear weighted superposition of the HMEs obtained from monthly fits to the UARS data to extrapolate the global, multi-variable tidal structure. A brief explanation of the HMEs’ origin is provided as well as information about a public website that has been set up to make the full extrapolated data sets available.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

NASA Astrophysics Data System (ADS)

Wang, Zhongfeng; Zhang, Xuyang; Hu, Bo; Liu, Jun; Li, Ligang; Gu, Yongqiang; Zhou, Bowen

2018-02-01

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

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.

Does internal variability change in response to global warming? A large ensemble modelling study of tropical rainfall

NASA Astrophysics Data System (ADS)

Milinski, S.; Bader, J.; Jungclaus, J. H.; Marotzke, J.

2017-12-01

There is some consensus on mean state changes of rainfall under global warming; changes of the internal variability, on the other hand, are more difficult to analyse and have not been discussed as much despite their importance for understanding changes in extreme events, such as droughts or floodings. We analyse changes in the rainfall variability in the tropical Atlantic region. We use a 100-member ensemble of historical (1850-2005) model simulations with the Max Planck Institute for Meteorology Earth System Model (MPI-ESM1) to identify changes of internal rainfall variability. To investigate the effects of global warming on the internal variability, we employ an additional ensemble of model simulations with stronger external forcing (1% CO2-increase per year, same integration length as the historical simulations) with 68 ensemble members. The focus of our study is on the oceanic Atlantic ITCZ. We find that the internal variability of rainfall over the tropical Atlantic does change due to global warming and that these changes in variability are larger than changes in the mean state in some regions. From splitting the total variance into patterns of variability, we see that the variability on the southern flank of the ITCZ becomes more dominant, i.e. explaining a larger fraction of the total variance in a warmer climate. In agreement with previous studies, we find that changes in the mean state show an increase and narrowing of the ITCZ. The large ensembles allow us to do a statistically robust differentiation between the changes in variability that can be explained by internal variability and those that can be attributed to the external forcing. Furthermore, we argue that internal variability in a transient climate is only well defined in the ensemble domain and not in the temporal domain, which requires the use of a large ensemble.

Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability

NASA Astrophysics Data System (ADS)

Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.

2018-04-01

This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent deep-water formation in the Labrador Sea, resulting in overestimated North Atlantic SST variability. Concerning the influence of locally (isotropically) increased resolution, the ENSO pattern and index statistics improve significantly with higher resolution around the equator, illustrating the potential of the novel unstructured-mesh method for global climate modeling.

Estimating global distribution of boreal, temperate, and tropical tree plant functional types using clustering techniques

NASA Astrophysics Data System (ADS)

Wang, Audrey; Price, David T.

2007-03-01

A simple integrated algorithm was developed to relate global climatology to distributions of tree plant functional types (PFT). Multivariate cluster analysis was performed to analyze the statistical homogeneity of the climate space occupied by individual tree PFTs. Forested regions identified from the satellite-based GLC2000 classification were separated into tropical, temperate, and boreal sub-PFTs for use in the Canadian Terrestrial Ecosystem Model (CTEM). Global data sets of monthly minimum temperature, growing degree days, an index of climatic moisture, and estimated PFT cover fractions were then used as variables in the cluster analysis. The statistical results for individual PFT clusters were found consistent with other global-scale classifications of dominant vegetation. As an improvement of the quantification of the climatic limitations on PFT distributions, the results also demonstrated overlapping of PFT cluster boundaries that reflected vegetation transitions, for example, between tropical and temperate biomes. The resulting global database should provide a better basis for simulating the interaction of climate change and terrestrial ecosystem dynamics using global vegetation models.

Evaluating atmospheric blocking in the global climate model EC-Earth

NASA Astrophysics Data System (ADS)

Hartung, Kerstin; Hense, Andreas; Kjellström, Erik

2013-04-01

Atmospheric blocking is a phenomenon of the midlatitudal troposphere, which plays an important role in climate variability. Therefore a correct representation of blocking in climate models is necessary, especially for evaluating the results of climate projections. In my master's thesis a validation of blocking in the coupled climate model EC-Earth is performed. Blocking events are detected based on the Tibaldi-Molteni Index. At first, a comparison with the reanalysis dataset ERA-Interim is conducted. The blocking frequency depending on longitude shows a small general underestimation of blocking in the model - a well known problem. Scaife et al. (2011) proposed the correction of model bias as a way to solve this problem. However, applying the correction to the higher resolution EC-Earth model does not yield any improvement. Composite maps show a link between blocking events and surface variables. One example is the formation of a positive surface temperature anomaly north and a negative anomaly south of the blocking anticyclone. In winter the surface temperature in EC-Earth can be reproduced quite well, but in summer a cold bias over the inner-European ocean is present. Using generalized linear models (GLMs) I want to study the connection between regional blocking and global atmospheric variables further. GLMs have the advantage of being applicable to non-Gaussian variables. Therefore the blocking index at each longitude, which is Bernoulli distributed, can be analysed statistically with GLMs. I applied a logistic regression between the blocking index and the geopotential height at 500 hPa to study the teleconnection of blocking events at midlatitudes with global geopotential height. GLMs also offer the possibility of quantifying the connections shown in composite maps. The implementation of the logistic regression can even be expanded to a search for trends in blocking frequency, for example in the scenario simulations.

Global Land Product Validation Protocols: An Initiative of the CEOS Working Group on Calibration and Validation to Evaluate Satellite-derived Essential Climate Variables

NASA Astrophysics Data System (ADS)

Guillevic, P. C.; Nickeson, J. E.; Roman, M. O.; camacho De Coca, F.; Wang, Z.; Schaepman-Strub, G.

2016-12-01

The Global Climate Observing System (GCOS) has specified the need to systematically produce and validate Essential Climate Variables (ECVs). The Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and in particular its subgroup on Land Product Validation (LPV) is playing a key coordination role leveraging the international expertise required to address actions related to the validation of global land ECVs. The primary objective of the LPV subgroup is to set standards for validation methods and reporting in order to provide traceable and reliable uncertainty estimates for scientists and stakeholders. The Subgroup is comprised of 9 focus areas that encompass 10 land surface variables. The activities of each focus area are coordinated by two international co-leads and currently include leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR), vegetation phenology, surface albedo, fire disturbance, snow cover, land cover and land use change, soil moisture, land surface temperature (LST) and emissivity. Recent additions to the focus areas include vegetation indices and biomass. The development of best practice validation protocols is a core activity of CEOS LPV with the objective to standardize the evaluation of land surface products. LPV has identified four validation levels corresponding to increasing spatial and temporal representativeness of reference samples used to perform validation. Best practice validation protocols (1) provide the definition of variables, ancillary information and uncertainty metrics, (2) describe available data sources and methods to establish reference validation datasets with SI traceability, and (3) describe evaluation methods and reporting. An overview on validation best practice components will be presented based on the LAI and LST protocol efforts to date.

Assessing the spatial and temporal variability of fAPAR 2-flux estimates in a temperate mixed coniferous forest

NASA Astrophysics Data System (ADS)

Putzenlechner, Birgitta; Sanchez-Azofeifa, Arturo; Ludwig, Ralf

2016-04-01

The fraction of absorbed photosynthetically active radiation (fAPAR) is recognized as one of the essential climate variables as it characterizes activity and dynamics of the Earth's terrestrial biosphere (GCOS, 2010). By linking photosynthetic active radiation (PAR) to the absorption of plants, fAPAR represents a crucial variable for describing land surface and atmosphere interactions considered in global circulation models as well as in production efficiency models for estimating terrestrial carbon balances. Recent studies report discrepancies between global fAPAR satellite products regarding both absolute values and uncertainty representation, thereby stressing the need for independent ground measurements (D'Odrico et al., 2014; Picket-Heaps et al., 2014; Tao et al., 2015). However, there is a lack of basic information to better understand the spatial and temporal bias of PAR field observations, particularly in forest ecosystems. In theory, it is known that fAPAR estimates are affected by e.g. illumination conditions, leaf area index, leaf color, background brightness, which in turn may lead to considerable bias of field measurements. However, theoretical findings lack validation in the field as well as practical recommendations for field protocols. In this study, the variability of two-flux fAPAR estimates with regards to different illumination conditions (solar zenith angles, diffuse radiation conditions) are investigated. Measurements of PAR are carried out at Graswang environmental monitoring site in Southern Germany within a temperate mixed coniferous forest. A relatively new environmental monitoring technology based on Wireless Sensor Networks (WSN) is applied, allowing for permanent synchronized measurements of transmitted PAR, thereby reducing temporal sampling bias. Transmitted PAR is obtained from 16 photon flux sensors, 1.3 m above the surface. With a reference sensor outside the forest measuring incoming PAR, a two-flux estimate based on the ratio of transmitted PAR and incoming PAR can be calculated for each 10-min timestep during daytime hours. The fAPAR time series exhibit seasonal variability (mean=0.7, sd=0.4 for the average of all PAR sensors calculated for each 10-min timestep) according to phenological development, but also considerable inter-sensor variability between single days. Standard deviations for fAPAR in mid-summer vary between 0.26 for days with overcast sky and 0.19 for clear sky conditions. Diurnal cycles of fAPAR under clear sky conditions show a sharp increase of fAPAR with increasing solar zenith angles, suggesting for an underestimation of fAPAR with low solar zenith angles as it has also been found in studies based on radiative transfer modeling (Widlowski et al., 2010). The experiences gained from the field observations contribute to a bias assessment for ground measurements as demanded by authors of recent studies on comparing global fAPAR satellite products.

Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

PubMed

Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

2013-06-01

Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

Leaf optical properties shed light on foliar trait variability at individual to global scales

NASA Astrophysics Data System (ADS)

Shiklomanov, A. N.; Serbin, S.; Dietze, M.

2017-12-01

Recent syntheses of large trait databases have contributed immensely to our understanding of drivers of plant function at the global scale. However, the global trade-offs revealed by such syntheses, such as the trade-off between leaf productivity and resilience (i.e. "leaf economics spectrum"), are often absent at smaller scales and fail to correlate with actual functional limitations. An improved understanding of how traits vary among communities, species, and individuals is critical to accurate representations of vegetation ecophysiology and ecological dynamics in ecosystem models. Spectral data from both field observations and remote sensing platforms present a rich and widely available source of information on plant traits. Here, we apply Bayesian inversion of the PROSPECT leaf radiative transfer model to a large global database of over 60,000 field spectra and plant traits to (1) comprehensively assess the accuracy of leaf trait estimation using PROSPECT spectral inversion; (2) investigate the correlations between optical traits estimable from PROSPECT and other important foliar traits such as nitrogen and lignin concentrations; and (3) identify dominant sources of variability and characterize trade-offs in optical and non-optical foliar traits. Our work provides a key methodological contribution by validating physically-based retrieval of plant traits from remote sensing observations, and provides insights about trait trade-offs related to plant acclimation, adaptation, and community assembly.

Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

NASA Astrophysics Data System (ADS)

Armal, S.; Devineni, N.; Khanbilvardi, R.

2017-12-01

This study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1,244 stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: Trend can be attributed to changes in global surface temperature anomalies, or to a combination of cyclical climate modes with varying quasi-periodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypotheses is made based on Watanabe-Akaike Information Criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1,244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to the El Nino Southern Oscillations, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic Multi-Decadal Oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the Northwest, West and Southwest climate regions.

A mechanics framework for a progressive failure methodology for laminated composites

NASA Technical Reports Server (NTRS)

Harris, Charles E.; Allen, David H.; Lo, David C.

1989-01-01

A laminate strength and life prediction methodology has been postulated for laminated composites which accounts for the progressive development of microstructural damage to structural failure. A damage dependent constitutive model predicts the stress redistribution in an average sense that accompanies damage development in laminates. Each mode of microstructural damage is represented by a second-order tensor valued internal state variable which is a strain like quantity. The mechanics framework together with the global-local strategy for predicting laminate strength and life is presented in the paper. The kinematic effects of damage are represented by effective engineering moduli in the global analysis and the results of the global analysis provide the boundary conditions for the local ply level stress analysis. Damage evolution laws are based on experimental results.

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

NASA Astrophysics Data System (ADS)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

A TRMM-Calibrated Infrared Technique for Global Rainfall Estimation

NASA Technical Reports Server (NTRS)

Negri, Andrew J.; Adler, Robert F.; Xu, Li-Ming

2003-01-01

This paper presents the development of a satellite infrared (IR) technique for estimating convective and stratiform rainfall and its application in studying the diurnal variability of rainfall on a global scale. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the global tropics during summer 2001. The technique is calibrated separately over land and ocean, making ingenious use of the IR data from the TRMM Visible/Infrared Scanner (VIRS) before application to global geosynchronous satellite data. The low sampling rate of TRMM PR imposes limitations on calibrating IR- based techniques; however, our research shows that PR observations can be applied to improve IR-based techniques significantly by selecting adequate calibration areas and calibration length. The diurnal cycle of rainfall, as well as the division between convective and t i f m rainfall will be presented. The technique is validated using available data sets and compared to other global rainfall products such as Global Precipitation Climatology Project (GPCP) IR product, calibrated with TRMM Microwave Imager (TMI) data. The calibrated CST technique has the advantages of high spatial resolution (4 km), filtering of non-raining cirrus clouds, and the stratification of the rainfall into its convective and stratiform components, the latter being important for the calculation of vertical profiles of latent heating.

Double symbolic joint entropy in nonlinear dynamic complexity analysis

NASA Astrophysics Data System (ADS)

Yao, Wenpo; Wang, Jun

2017-07-01

Symbolizations, the base of symbolic dynamic analysis, are classified as global static and local dynamic approaches which are combined by joint entropy in our works for nonlinear dynamic complexity analysis. Two global static methods, symbolic transformations of Wessel N. symbolic entropy and base-scale entropy, and two local ones, namely symbolizations of permutation and differential entropy, constitute four double symbolic joint entropies that have accurate complexity detections in chaotic models, logistic and Henon map series. In nonlinear dynamical analysis of different kinds of heart rate variability, heartbeats of healthy young have higher complexity than those of the healthy elderly, and congestive heart failure (CHF) patients are lowest in heartbeats' joint entropy values. Each individual symbolic entropy is improved by double symbolic joint entropy among which the combination of base-scale and differential symbolizations have best complexity analysis. Test results prove that double symbolic joint entropy is feasible in nonlinear dynamic complexity analysis.

1993 Earth Observing System reference handbook

NASA Technical Reports Server (NTRS)

Asrar, Ghassem (Editor); Dokken, David Jon (Editor)

1993-01-01

Mission to Planet Earth (MTPE) is a NASA-sponsored concept that uses space- and ground-based measurement systems to provide the scientific basis for understanding global change. The space-based components of MTPE will provide a constellation of satellites to monitor the Earth from space. Sustained observations will allow researchers to monitor climate variables overtime to determine trends; however, space-based monitoring alone is not sufficient. A comprehensive data and information system, a community of scientists performing research with the data acquired, and extensive ground campaigns are all important components. Brief descriptions of the various elements that comprise the overall mission are provided. The Earth Observing System (EOS) - a series of polar-orbiting and low-inclination satellites for long-term global observations of the land surface, biosphere, solid Earth, atmosphere, and oceans - is the centerpiece of MTPE. The elements comprising the EOS mission are described in detail.

Data-based estimates of the ocean carbon sink variability - first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM)

NASA Astrophysics Data System (ADS)

Rödenbeck, C.; Bakker, D. C. E.; Gruber, N.; Iida, Y.; Jacobson, A. R.; Jones, S.; Landschützer, P.; Metzl, N.; Nakaoka, S.; Olsen, A.; Park, G.-H.; Peylin, P.; Rodgers, K. B.; Sasse, T. P.; Schuster, U.; Shutler, J. D.; Valsala, V.; Wanninkhof, R.; Zeng, J.

2015-08-01

Using measurements of the surface-ocean CO2 partial pressure (pCO2) and 14 different pCO2 mapping methods recently collated by the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative, variations in regional and global sea-air CO2 fluxes have been investigated. Though the available mapping methods use widely different approaches, we find relatively consistent estimates of regional pCO2 seasonality, in line with previous estimates. In terms of interannual variability (IAV), all mapping methods estimate the largest variations to occur in the Eastern equatorial Pacific. Despite considerable spead in the detailed variations, mapping methods with closer match to the data also tend to be more consistent with each other. Encouragingly, this includes mapping methods belonging to complementary types - taking variability either directly from the pCO2 data or indirectly from driver data via regression. From a weighted ensemble average, we find an IAV amplitude of the global sea-air CO2 flux of 0.31 PgC yr-1 (standard deviation over 1992-2009), which is larger than simulated by biogeochemical process models. On a decadal perspective, the global CO2 uptake is estimated to have gradually increased since about 2000, with little decadal change prior to 2000. The weighted mean total ocean CO2 sink estimated by the SOCOM ensemble is consistent within uncertainties with estimates from ocean-interior carbon data or atmospheric oxygen trends.

Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

NASA Astrophysics Data System (ADS)

Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

2016-07-01

Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification technique to the Global Ionospheric Map (GIM) TEC data which is provided by the NASA Jet Propulsion Laboratory (JPL), it will be shown that SVM can be a suitable learning method to detect the anomalies in Total Electron Content (TEC) variations. This study is supported by TUBITAK 114E541 project as a part of the Scientific and Technological Research Projects Funding Program (1001).

Periodicity and stability for variable-time impulsive neural networks.

PubMed

Li, Hongfei; Li, Chuandong; Huang, Tingwen

2017-10-01

The paper considers a general neural networks model with variable-time impulses. It is shown that each solution of the system intersects with every discontinuous surface exactly once via several new well-proposed assumptions. Moreover, based on the comparison principle, this paper shows that neural networks with variable-time impulse can be reduced to the corresponding neural network with fixed-time impulses under well-selected conditions. Meanwhile, the fixed-time impulsive systems can be regarded as the comparison system of the variable-time impulsive neural networks. Furthermore, a series of sufficient criteria are derived to ensure the existence and global exponential stability of periodic solution of variable-time impulsive neural networks, and to illustrate the same stability properties between variable-time impulsive neural networks and the fixed-time ones. The new criteria are established by applying Schaefer's fixed point theorem combined with the use of inequality technique. Finally, a numerical example is presented to show the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

The value and limitations of global air-sampling networks for improving our understanding trace gas behavior

NASA Astrophysics Data System (ADS)

Montzka, S. A.

2016-12-01

Measurements from global surface-based air sampling networks provide a fundamental understanding of how and why concentrations of long-lived trace gases are changing over time. Results from these networks are used to quantify trace-gas concentrations and their time-dependent changes on global and smaller scales, and thus provide a means to quantify emission rates, loss frequencies, and mixing processes. Substantial advances in measurement and sampling technologies and the ability of these programs to create and maintain reliable gas standards mean that spatial concentration gradients and time-dependent changes are often very reliably measured. The presence of multiple independent networks allows an assessment of this reliability. Furthermore, recent global `snap-shot' surveys (e.g., HIPPO and ATom) and ongoing atmospheric profiling programs help us assess the ability of surface-based data to describe concentration distributions throughout most of the atmosphere ( 80% of its mass). In this overview talk, I'll explore the usefulness and limitations of existing long-term, ongoing sampling network programs and their advantages and disadvantages for characterizing concentrations on global and regional scales, and how recent advances (and short-term sampling programs) help us assess the accuracy of the surface networks to provide estimates of source and sink magnitudes, and inter-annual variability in both.

Advances in Understanding Global Water Cycle with Advent of Global Precipitation Measurement (GPM) Mission

NASA Technical Reports Server (NTRS)

Smith, Eric A.; Starr, David (Technical Monitor)

2002-01-01

Within this decade the internationally organized Global Precipitation Measurement (GPM) Mission will take an important step in creating a global precipitation observing system from space. One perspective for understanding the nature of GPM is that it will be a hierarchical system of datastreams beginning with very high caliber combined dual frequency radar/passive microwave (PMW) rain-radiometer retrievals, to high caliber PMW rain-radiometer only retrievals, and then on to blends of the former datastreams with additional lower-caliber PMW-based and IR-based rain retrievals. Within the context of the now emerging global water & energy cycle (GWEC) programs of a number of research agencies throughout the world, GPM serves as a centerpiece space mission for improving our understanding of the global water cycle from a global measurement perspective. One of the salient problems within our current understanding of the global water and energy cycle is determining whether a change in the rate of the water cycle is accompanying changes in climate, e.g., climate warming. As there are a number of ways in which to define a rate-change of the global water cycle, it is not entirely clear as to what constitutes such a determination. This paper presents an overview of the GPM Mission and how its observations can be used within the framework of the oceanic and continental water budget equations to determine whether a given perturbation in precipitation is indicative of an actual rate change in the global water cycle, consistent with required responses in water storage and/or water flux transport processes, or whether it is the natural variability of a fixed rate cycle.

Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Trends for GCM Validation

NASA Technical Reports Server (NTRS)

Molnar, Gyula I.; Susskind, Joel; Iredell, Lena F.

2010-01-01

Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.

The Role of Ocean Currents in the Temperature Selection of Plankton: Insights from an Individual-Based Model

PubMed Central

Hellweger, Ferdi L.; van Sebille, Erik; Calfee, Benjamin C.; Chandler, Jeremy W.; Zinser, Erik R.; Swan, Brandon K.; Fredrick, Neil D.

2016-01-01

Biogeography studies that correlate the observed distribution of organisms to environmental variables are typically based on local conditions. However, in cases with substantial translocation, like planktonic organisms carried by ocean currents, selection may happen upstream and local environmental factors may not be representative of those that shaped the local population. Here we use an individual-based model of microbes in the global surface ocean to explore this effect for temperature. We simulate up to 25 million individual cells belonging to up to 50 species with different temperature optima. Microbes are moved around the globe based on a hydrodynamic model, and grow and die based on local temperature. We quantify the role of currents using the “advective temperature differential” metric, which is the optimum temperature of the most abundant species from the model with advection minus that from the model without advection. This differential depends on the location and can be up to 4°C. Poleward-flowing currents, like the Gulf Stream, generally experience cooling and the differential is positive. We apply our results to three global datasets. For observations of optimum growth temperature of phytoplankton, accounting for the effect of currents leads to a slightly better agreement with observations, but there is large variability and the improvement is not statistically significant. For observed Prochlorococcus ecotype ratios and metagenome nucleotide divergence, accounting for advection improves the correlation significantly, especially in areas with relatively strong poleward or equatorward currents. PMID:27907181

Global DEM Errors Underpredict Coastal Vulnerability to Sea Level Rise and Flooding

NASA Astrophysics Data System (ADS)

Kulp, Scott; Strauss, Benjamin

2016-04-01

Elevation data based on NASA's Shuttle Radar Topography Mission (SRTM) have been widely used to evaluate threats from global sea level rise, storm surge, and coastal floods. However, SRTM data are known to include large vertical errors in densely urban or densely vegetated areas. The errors may propagate to derived land and population exposure assessments. We compare assessments based on SRTM data against references employing high-accuracy bare-earth elevation data generated from lidar data available for coastal areas of the United States. We find that both 1-arcsecond and 3-arcsecond horizontal resolution SRTM data systemically underestimate exposure across all assessed spatial scales and up to at least 10m above the high tide line. At 3m, 1-arcsecond SRTM underestimates U.S. population exposure by more than 60%, and under-predicts population exposure in 90% of coastal states, 87% of counties, and 83% of municipalities. These fractions increase with elevation, but error medians and variability fall to lower levels, with national exposure underestimated by just 24% at 10m. Results using 3-arcsecond SRTM are extremely similar. Coastal analyses based on SRTM data thus appear to greatly underestimate sea level and flood threats, especially at lower elevations. However, SRTM-based estimates may usefully be regarded as providing lower bounds to actual threats. We additionally assess the performance of NOAA's Global Land One-km Base Elevation Project (GLOBE), another publicly-available global DEM, but do not reach any definitive conclusion because of the spatial heterogeneity in its quality.

The Wmo Global Atmosphere Watch Programme: Global Framework for Atmospheric Composition Observations and Analysis

NASA Astrophysics Data System (ADS)

Tarasova, O. A.; Jalkanen, L.

2010-12-01

The WMO Global Atmosphere Watch (GAW) Programme is the only existing long-term international global programme providing an international coordinated framework for observations and analysis of the chemical composition of the atmosphere. GAW is a partnership involving contributors from about 80 countries. It includes a coordinated global network of observing stations along with supporting facilities (Central Facilities) and expert groups (Scientific Advisory Groups, SAGs and Expert Teams, ETs). Currently GAW coordinates activities and data from 27 Global Stations and a substantial number of Regional and Contributing Stations. Station information is available through the GAW Station Information System GAWSIS (http://gaw.empa.ch/gawsis/). There are six key groups of variables which are addressed by the GAW Programme, namely: ozone, reactive gases, greenhouse gases, aerosols, UV radiation and precipitation chemistry. GAW works to implement integrated observations unifying measurements from different platforms (ground based in situ and remote, balloons, aircraft and satellite) supported by modeling activities. GAW provides data for ozone assessments, Greenhouse Gas Bulletins, Ozone Bulletins and precipitation chemistry assessments published on a regular basis and for early warnings of changes in the chemical composition and related physical characteristics of the atmosphere. To ensure that observations can be used for global assessments, the GAW Programme has developed a Quality Assurance system. Five types of Central Facilities dedicated to the six groups of measurement variables are operated by WMO Members and form the basis of quality assurance and data archiving for the GAW global monitoring network. They include Central Calibration Laboratories (CCLs) that host primary standards (PS), Quality Assurance/Science Activity Centres (QA/SACs), World Calibration Centers (WCCs), Regional Calibration Centers (RCCs), and World Data Centers (WDCs) with responsibility for archiving and access to GAW data. Education, training, workshops, comparison campaigns, station audits/visits and twinning are also provided to build capacities in atmospheric sciences in Member countries.

Interannual Variability in Global Soil Respiration on a 0.5 Degree Grid Cell Basis (1980-1994)

DOE Data Explorer

Raich, James W. [Iowa State University, Ames, IA (USA); Potter, Christopher S. [NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States); Bhagawat, Dwipen [Iowa State Univ., Ames, IA (United States); Olson, L. M. [CDIAC, Oak Ridge National Laboratory, Oak Ridge, TN

2003-08-01

The Principal Investigators used a climate-driven regression model to develop spatially resolved estimates of soil-CO2 emissions from the terrestrial land surface for each month from January 1980 to December 1994, to evaluate the effects of interannual variations in climate on global soil-to-atmosphere CO2 fluxes. The mean annual global soil-CO2 flux over this 15-y period was estimated to be 80.4 (range 79.3-81.8) Pg C. Monthly variations in global soil-CO2 emissions followed closely the mean temperature cycle of the Northern Hemisphere. Globally, soil-CO2 emissions reached their minima in February and peaked in July and August. Tropical and subtropical evergreen broad-leaved forests contributed more soil-derived CO2 to the atmosphere than did any other vegetation type (~30% of the total) and exhibited a biannual cycle in their emissions. Soil-CO2 emissions in other biomes exhibited a single annual cycle that paralleled the seasonal temperature cycle. Interannual variability in estimated global soil-CO2 production is substantially less than is variability in net carbon uptake by plants (i.e., net primary productivity). Thus, soils appear to buffer atmospheric CO2 concentrations against far more dramatic seasonal and interannual differences in plant growth. Within seasonally dry biomes (savannas, bushlands, and deserts), interannual variability in soil-CO2 emmissions correlated significantly with interannual differences in precipitation. At the global scale, however, annual soil-CO2 fluxes correlated with mean annual temperature, with a slope of 3.3 PgCY-1 per degree Celsius. Although the distribution of precipitation influences seasonal and spatial patterns of soil-CO2 emissions, global warming is likely to stimulate CO2 emissions from soils.

Estimation of the global burden of mesothelioma deaths from incomplete national mortality data.

PubMed

Odgerel, Chimed-Ochir; Takahashi, Ken; Sorahan, Tom; Driscoll, Tim; Fitzmaurice, Christina; Yoko-O, Makoto; Sawanyawisuth, Kittisak; Furuya, Sugio; Tanaka, Fumihiro; Horie, Seichi; Zandwijk, Nico van; Takala, Jukka

2017-12-01

Mesothelioma is increasingly recognised as a global health issue and the assessment of its global burden is warranted. To descriptively analyse national mortality data and to use reported and estimated data to calculate the global burden of mesothelioma deaths. For the study period of 1994 to 2014, we grouped 230 countries into 59 countries with quality mesothelioma mortality data suitable to be used for reference rates, 45 countries with poor quality data and 126 countries with no data, based on the availability of data in the WHO Mortality Database. To estimate global deaths, we extrapolated the gender-specific and age-specific mortality rates of the countries with quality data to all other countries. The global numbers and rates of mesothelioma deaths have increased over time. The 59 countries with quality data recorded 15 011 mesothelioma deaths per year over the 3 most recent years with available data (equivalent to 9.9 deaths per million per year). From these reference data, we extrapolated the global mesothelioma deaths to be 38 400 per year, based on extrapolations for asbestos use. Although the validity of our extrapolation method depends on the adequate identification of quality mesothelioma data and appropriate adjustment for other variables, our estimates can be updated, refined and verified because they are based on commonly accessible data and are derived using a straightforward algorithm. Our estimates are within the range of previously reported values but higher than the most recently reported values. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Global variation of carbon use efficiency in terrestrial ecosystems

NASA Astrophysics Data System (ADS)

Tang, Xiaolu; Carvalhais, Nuno; Moura, Catarina; Reichstein, Markus

2017-04-01

Carbon use efficiency (CUE), defined as the ratio between net primary production (NPP) and gross primary production (GPP), is an emergent property of vegetation that describes its effectiveness in storing carbon (C) and is of significance for understanding C biosphere-atmosphere exchange dynamics. A constant CUE value of 0.5 has been widely used in terrestrial C-cycle models, such as the Carnegie-Ames-Stanford-Approach model, or the Marine Biological Laboratory/Soil Plant-Atmosphere Canopy Model, for regional or global modeling purposes. However, increasing evidence argues that CUE is not constant, but varies with ecosystem types, site fertility, climate, site management and forest age. Hence, the assumption of a constant CUE of 0.5 can produce great uncertainty in estimating global carbon dynamics between terrestrial ecosystems and the atmosphere. Here, in order to analyze the global variations in CUE and understand how CUE varies with environmental variables, a global database was constructed based on published data for crops, forests, grasslands, wetlands and tundra ecosystems. In addition to CUE data, were also collected: GPP and NPP; site variables (e.g. climate zone, site management and plant function type); climate variables (e.g. temperature and precipitation); additional carbon fluxes (e.g. soil respiration, autotrophic respiration and heterotrophic respiration); and carbon pools (e.g. stem, leaf and root biomass). Different climate metrics were derived to diagnose seasonal temperature (mean annual temperature, MAT, and maximum temperature, Tmax) and water availability proxies (mean annual precipitation, MAP, and Palmer Drought Severity Index), in order to improve the local representation of environmental variables. Additionally were also included vegetation phenology dynamics as observed by different vegetation indices from the MODIS satellite. The mean CUE of all terrestrial ecosystems was 0.45, 10% lower than the previous assumed constant CUE of 0.50. CUE varied significantly between sites - from 0.13 to 0.93 - and between ecosystem types, ranging between 0.41 and 0.60, decreasing from wetlands, to tundra, to croplands, to grasslands until the lower CUE found on average for forested ecosystems. Our analysis shows that ecosystem type was the most important predictor of CUE in terrestrial ecosystems, immediately followed by Tmax; MAT and management practices. For crop, forest and wetland ecosystems CUE varied with climate zones and a strong linear negative correlation was found between CUE and MAT and MAP for grassland ecosystems. Overall, the interaction between different environmental variables showed significant effects on CUE for all ecosystem types. Our results challenge the consideration of a constant value of 0.5 for modeling global purposes, and argue for a deeper understanding of environmental controls on CUE for different ecosystem types.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence.

PubMed

Alemohammad, Seyed Hamed; Fang, Bin; Konings, Alexandra G; Aires, Filipe; Green, Julia K; Kolassa, Jana; Miralles, Diego; Prigent, Catherine; Gentine, Pierre

2017-01-01

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H and GPP from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Use of a process-based model for assessing the methane budgets of global terrestrial ecosystems and evaluation of uncertainty

NASA Astrophysics Data System (ADS)

Ito, A.; Inatomi, M.

2012-02-01

We assessed the global terrestrial budget of methane (CH4) by using a process-based biogeochemical model (VISIT) and inventory data for components of the budget that were not included in the model. Emissions from wetlands, paddy fields, biomass burning, and plants, as well as oxidative consumption by upland soils, were simulated by the model. Emissions from ruminant livestock and termites were evaluated by using an inventory approach. These CH4 flows were estimated for each of the model's 0.5° × 0.5° grid cells from 1901 to 2009, while accounting for atmospheric composition, meteorological factors, and land-use changes. Estimation uncertainties were examined through ensemble simulations using different parameterization schemes and input data (e.g., different wetland maps and emission factors). From 1996 to 2005, the average global terrestrial CH4 budget was estimated on the basis of 1152 simulations, and terrestrial ecosystems were found to be a net source of 308.3 ± 20.7 Tg CH4 yr-1. Wetland and livestock ruminant emissions were the primary sources. The results of our simulations indicate that sources and sinks are distributed highly heterogeneously over the Earth's land surface. Seasonal and interannual variability in the terrestrial budget was also assessed. The trend of increasing net emission from terrestrial sources and its relationship with temperature variability imply that terrestrial CH4 feedbacks will play an increasingly important role as a result of future climatic change.

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Fang, Bin; Konings, Alexandra G.; Aires, Filipe; Green, Julia K.; Kolassa, Jana; Miralles, Diego; Prigent, Catherine; Gentine, Pierre

2017-09-01

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.

Leaf optical properties shed light on foliar trait variability at individual to global scales

NASA Astrophysics Data System (ADS)

Shiklomanov, A. N.; Serbin, S.; Dietze, M.

2016-12-01

Recent syntheses of large trait databases have contributed immensely to our understanding of drivers of plant function at the global scale. However, the global trade-offs revealed by such syntheses, such as the trade-off between leaf productivity and resilience (i.e. "leaf economics spectrum"), are often absent at smaller scales and fail to correlate with actual functional limitations. An improved understanding of how traits vary within communities, species, and individuals is critical to accurate representations of vegetation ecophysiology and ecological dynamics in ecosystem models. Spectral data from both field observations and remote sensing platforms present a potentially rich and widely available source of information on plant traits. In particular, the inversion of physically-based radiative transfer models (RTMs) is an effective and general method for estimating plant traits from spectral measurements. Here, we apply Bayesian inversion of the PROSPECT leaf RTM to a large database of field spectra and plant traits spanning tropical, temperate, and boreal forests, agricultural plots, arid shrublands, and tundra to identify dominant sources of variability and characterize trade-offs in plant functional traits. By leveraging such a large and diverse dataset, we re-calibrate the empirical absorption coefficients underlying the PROSPECT model and expand its scope to include additional leaf biochemical components, namely leaf nitrogen content. Our work provides a key methodological contribution as a physically-based retrieval of leaf nitrogen from remote sensing observations, and provides substantial insights about trait trade-offs related to plant acclimation, adaptation, and community assembly.

Detection and Distribution of Natural Gaps in Tropical Rainforest

NASA Astrophysics Data System (ADS)

Goulamoussène, Y.; Linguet, L.; Hérault, B.

2014-12-01

Forest management is important to assess biodiversity and ecological processes. Requirements for disturbance information have also been motivated by the scientific community. Therefore, understanding and monitoring the distribution frequencies of treefall gaps is relevant to better understanding and predicting the carbon budget in response to global change and land use change. In this work we characterize and quantify the frequency distribution of natural canopy gaps. We observe then interaction between environment variables and gap formation across tropical rainforest of the French Guiana region by using high resolution airborne Light Detection and Ranging (LiDAR). We mapped gaps with canopy model distribution on 40000 ha of forest. We used a Bayesian modelling framework to estimate and select useful covariate model parameters. Topographic variables are included in a model to predict gap size distribution. We discuss results from the interaction between environment and gap size distribution, mainly topographic indexes. The use of both airborne and space-based techniques has improved our ability to supply needed disturbance information. This work is an approach at plot scale. The use of satellite data will allow us to work at forest scale. The inclusion of climate variables in our model will let us assess the impact of global change on tropical rainforest.

Bio-Optical Measurements at Ocean Boundaries in Support of SIMBIOS. Chapter 7

NASA Technical Reports Server (NTRS)

Chavez, Francisco P.; Strutton, Peter G.; Schlining, Brian M.

2001-01-01

The equatorial Pacific is a major component of global biogeochemical cycles, due to upwelling that occurs from the coast of South America to beyond 180 deg. This upwelling has significant implications for global CO2 fluxes, as well as primary and secondary production. In addition, this region of the world's oceans represents a large oceanic province over which validation data for Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) are necessary. This project consists of a mooring program and supporting cruise-based measurements aimed at quantifying the spectrum of biological and chemical variability in the equatorial Pacific and obtaining validation data for SeaWiFS. The project has the following general objectives: (1) to understand the relationships between physical forcing, primary production, nutrient supply and the exchange of carbon dioxide between ocean and atmosphere in the equatorial Pacific; (2) to describe the biological and chemical responses to climate and ocean variability; (3) to describe the spatial, seasonal and inter-annual variability in near surface plant pigments, primary production, carbon dioxide and nutrient distributions; and (4) to obtain near real-time bio-optical measurements for validation of SeaWiFS and subsequent ocean color sensors.

Estimation of global soil respiration by accounting for land-use changes derived from remote sensing data.

PubMed

Adachi, Minaco; Ito, Akihiko; Yonemura, Seiichiro; Takeuchi, Wataru

2017-09-15

Soil respiration is one of the largest carbon fluxes from terrestrial ecosystems. Estimating global soil respiration is difficult because of its high spatiotemporal variability and sensitivity to land-use change. Satellite monitoring provides useful data for estimating the global carbon budget, but few studies have estimated global soil respiration using satellite data. We provide preliminary insights into the estimation of global soil respiration in 2001 and 2009 using empirically derived soil temperature equations for 17 ecosystems obtained by field studies, as well as MODIS climate data and land-use maps at a 4-km resolution. The daytime surface temperature from winter to early summer based on the MODIS data tended to be higher than the field-observed soil temperatures in subarctic and temperate ecosystems. The estimated global soil respiration was 94.8 and 93.8 Pg C yr -1 in 2001 and 2009, respectively. However, the MODIS land-use maps had insufficient spatial resolution to evaluate the effect of land-use change on soil respiration. The spatial variation of soil respiration (Q 10 ) values was higher but its spatial variation was lower in high-latitude areas than in other areas. However, Q 10 in tropical areas was more variable and was not accurately estimated (the values were >7.5 or <1.0) because of the low seasonal variation in soil respiration in tropical ecosystems. To solve these problems, it will be necessary to validate our results using a combination of remote sensing data at higher spatial resolution and field observations for many different ecosystems, and it will be necessary to account for the effects of more soil factors in the predictive equations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Native temperature regime influences soil response to simulated warming

Treesearch

Timothy G. Whitby; Michael D. Madritch

2013-01-01

Anthropogenic climate change is expected to increase global temperatures and potentially increase soil carbon (C) mineralization, which could lead to a positive feedback between global warming and soil respiration. However the magnitude and spatial variability of belowground responses to warming are not yet fully understood. Some of the variability may depend...

Hierarchical Letters in ASD: High Stimulus Variability under Different Attentional Modes

ERIC Educational Resources Information Center

Van der Hallen, Ruth; Vanmarcke, Steven; Noens, Ilse; Wagemans, Johan

2017-01-01

Studies using hierarchical patterns to test global precedence and local-global interference in individuals with ASD have produced mixed results. The current study focused on stimulus variability and locational uncertainty, while using different attentional modes. Two groups of 44 children with and without ASD completed a divided attention task as…

Predicting coral bleaching in response to environmental stressors using 8 years of global-scale data.

PubMed

Yee, Susan Harrell; Barron, Mace G

2010-02-01

Coral reefs have experienced extensive mortality over the past few decades as a result of temperature-induced mass bleaching events. There is an increasing realization that other environmental factors, including water mixing, solar radiation, water depth, and water clarity, interact with temperature to either exacerbate bleaching or protect coral from mass bleaching. The relative contribution of these factors to variability in mass bleaching at a global scale has not been quantified, but can provide insights when making large-scale predictions of mass bleaching events. Using data from 708 bleaching surveys across the globe, a framework was developed to predict the probability of moderate or severe bleaching as a function of key environmental variables derived from global-scale remote-sensing data. The ability of models to explain spatial and temporal variability in mass bleaching events was quantified. Results indicated approximately 20% improved accuracy of predictions of bleaching when solar radiation and water mixing, in addition to elevated temperature, were incorporated into models, but predictive accuracy was variable among regions. Results provide insights into the effects of environmental parameters on bleaching at a global scale.

A Multilayer Dataset of SSM/I-Derived Global Ocean Surface Turbulent Fluxes

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Shie, Chung-Lin; Atlas, Robert M.; Ardizzone, Joe; Nelkin, Eric; Einaud, Franco (Technical Monitor)

2001-01-01

A dataset including daily- and monthly-mean turbulent fluxes (momentum, latent heat, and sensible heat) and some relevant parameters over global oceans, derived from the Special Sensor Microwave/Imager (SSM/I) data, for the period July 1987-December 1994 and the 1988-94 annual and monthly-mean climatologies of the same variables is created. It has a spatial resolution of 2.0deg x 2.5deg latitude-longitude. The retrieved surface air humidity is found to be generally accurate as compared to the collocated radiosonde observations over global oceans. The retrieved wind stress and latent heat flux show useful accuracy as verified against research quality measurements of ship and buoy in the western equatorial Pacific. The 1988-94 seasonal-mean wind stress and latent heat flux show reasonable patterns related to seasonal variations of the atmospheric general circulation. The patterns of 1990-93 annual-mean turbulent fluxes and input variables are generally in good agreement with one of the best global analyzed flux datasets that based on COADS (comprehensive ocean-atmosphere data set) with corrections on wind speeds and covered the same period. The retrieved wind speed is generally within +/-1 m/s of the COADS-based, but is stronger by approx. 1-2 m/s in the northern extratropical oceans. The discrepancy is suggested to be mainly due to higher COADS-modified wind speeds resulting from underestimation of anemometer heights. Compared to the COADS-based, the retrieved latent heat flux and sea-air humidity difference are generally larger with significant differences in the trade wind zones and the ocean south of 40degS (up to approx. 40-60 W/sq m and approx. 1-1.5 g/kg). The discrepancy is believed to be mainly caused by higher COADS-based surface air humidity arising from the overestimation of dew point temperatures and from the extrapolation of observed high humidity southward into data-void regions south of 40degS. The retrieved sensible heat flux is generally within +/-5 W/sq m of UWM/COADS, except for some areas in the extratropical oceans, where the differences in wind speed have large impact on the difference in sensible heat flux. The dataset of SSM/I-derived turbulent fluxes is useful for climate studies, forcing of ocean models, and validation of coupled ocean-atmosphere global models.

Characterizing CDOM Spectral Variability Across Diverse Regions and Spectral Ranges

NASA Astrophysics Data System (ADS)

Grunert, Brice K.; Mouw, Colleen B.; Ciochetto, Audrey B.

2018-01-01

Satellite remote sensing of colored dissolved organic matter (CDOM) has focused on CDOM absorption (aCDOM) at a reference wavelength, as its magnitude provides insight into the underwater light field and large-scale biogeochemical processes. CDOM spectral slope, SCDOM, has been treated as a constant or semiconstant parameter in satellite retrievals of aCDOM despite significant regional and temporal variabilities. SCDOM and other optical metrics provide insights into CDOM composition, processing, food web dynamics, and carbon cycling. To date, much of this work relies on fluorescence techniques or aCDOM in spectral ranges unavailable to current and planned satellite sensors (e.g., <300 nm). In preparation for anticipated future hyperspectral satellite missions, we take the first step here of exploring global variability in SCDOM and fit deviations in the aCDOM spectra using the recently proposed Gaussian decomposition method. From this, we investigate if global variability in retrieved SCDOM and Gaussian components is significant and regionally distinct. We iteratively decreased the spectral range considered and analyzed the number, location, and magnitude of fitted Gaussian components to understand if a reduced spectral range impacts information obtained within a common spectral window. We compared the fitted slope from the Gaussian decomposition method to absorption-based indices that indicate CDOM composition to determine the ability of satellite-derived slope to inform the analysis and modeling of large-scale biogeochemical processes. Finally, we present implications of the observed variability for remote sensing of CDOM characteristics via SCDOM.

Extreme Events in China under Climate Change: Uncertainty and related impacts (CSSP-FOREX)

NASA Astrophysics Data System (ADS)

Leckebusch, Gregor C.; Befort, Daniel J.; Hodges, Kevin I.

2016-04-01

Suitable adaptation strategies or the timely initiation of related mitigation efforts in East Asia will strongly depend on robust and comprehensive information about future near-term as well as long-term potential changes in the climate system. Therefore, understanding the driving mechanisms associated with the East Asian climate is of major importance. The FOREX project (Fostering Regional Decision Making by the Assessment of Uncertainties of Future Regional Extremes and their Linkage to Global Climate System Variability for China and East Asia) focuses on the investigation of extreme wind and rainfall related events over Eastern Asia and their possible future changes. Here, analyses focus on the link between local extreme events and their driving weather systems. This includes the coupling between local rainfall extremes and tropical cyclones, the Meiyu frontal system, extra-tropical teleconnections and monsoonal activity. Furthermore, the relation between these driving weather systems and large-scale variability modes, e.g. NAO, PDO, ENSO is analysed. Thus, beside analysing future changes of local extreme events, the temporal variability of their driving weather systems and related large-scale variability modes will be assessed in current CMIP5 global model simulations to obtain more robust results. Beyond an overview of FOREX itself, first results regarding the link between local extremes and their steering weather systems based on observational and reanalysis data are shown. Special focus is laid on the contribution of monsoonal activity, tropical cyclones and the Meiyu frontal system on the inter-annual variability of the East Asian summer rainfall.

A Skilful Marine Sclerochronological Network Based Reconstruction of North Atlantic Subpolar Gyre Dynamics

NASA Astrophysics Data System (ADS)

Reynolds, D.; Hall, I. R.; Slater, S. M.; Scourse, J. D.; Wanamaker, A. D.; Halloran, P. R.; Garry, F. K.

2017-12-01

Spatial network analyses of precisely dated, and annually resolved, tree-ring proxy records have facilitated robust reconstructions of past atmospheric climate variability and the associated mechanisms and forcings that drive it. In contrast, a lack of similarly dated marine archives has constrained the use of such techniques in the marine realm, despite the potential for developing a more robust understanding of the role basin scale ocean dynamics play in the global climate system. Here we show that a spatial network of marine molluscan sclerochronological oxygen isotope (δ18Oshell) series spanning the North Atlantic region provides a skilful reconstruction of basin scale North Atlantic sea surface temperatures (SSTs). Our analyses demonstrate that the composite marine series (referred to as δ18Oproxy_PC1) is significantly sensitive to inter-annual variability in North Atlantic SSTs (R=-0.61 P<0.01) and surface air temperatures (SATs; R=-0.67, P<0.01) over the 20th century. Subpolar gyre (SPG) SSTs dominates variability in the δ18Oproxy_PC1 series at sub-centennial frequencies (R=-0.51, P<0.01). Comparison of the δ18Oproxy_PC1 series against variability in the strength of the European Slope Current and maximum North Atlantic meridional overturning circulation derived from numeric climate models (CMIP5), indicates that variability in the SPG region, associated with the strength of the surface currents of the North Atlantic, are playing a significant role in shaping the multi-decadal scale SST variability over the industrial era. These analyses demonstrate that spatial networks developed from sclerochronological archives can provide powerful baseline archives of past ocean variability that can facilitate the development of a quantitative understanding for the role the oceans play in the global climate systems and constraining uncertainties in numeric climate models.

Constraining land carbon cycle process understanding with observations of atmospheric CO2 variability

NASA Astrophysics Data System (ADS)

Collatz, G. J.; Kawa, S. R.; Liu, Y.; Zeng, F.; Ivanoff, A.

2013-12-01

We evaluate our understanding of the land biospheric carbon cycle by benchmarking a model and its variants to atmospheric CO2 observations and to an atmospheric CO2 inversion. Though the seasonal cycle in CO2 observations is well simulated by the model (RMSE/standard deviation of observations <0.5 at most sites north of 15N and <1 for Southern Hemisphere sites) different model setups suggest that the CO2 seasonal cycle provides some constraint on gross photosynthesis, respiration, and fire fluxes revealed in the amplitude and phase at northern latitude sites. CarbonTracker inversions (CT) and model show similar phasing of the seasonal fluxes but agreement in the amplitude varies by region. We also evaluate interannual variability (IAV) in the measured atmospheric CO2 which, in contrast to the seasonal cycle, is not well represented by the model. We estimate the contributions of biospheric and fire fluxes, and atmospheric transport variability to explaining observed variability in measured CO2. Comparisons with CT show that modeled IAV has some correspondence to the inversion results >40N though fluxes match poorly at regional to continental scales. Regional and global fire emissions are strongly correlated with variability observed at northern flask sample sites and in the global atmospheric CO2 growth rate though in the latter case fire emissions anomalies are not large enough to account fully for the observed variability. We discuss remaining unexplained variability in CO2 observations in terms of the representation of fluxes by the model. This work also demonstrates the limitations of the current network of CO2 observations and the potential of new denser surface measurements and space based column measurements for constraining carbon cycle processes in models.

Predicting Clinical Gains and Side Effects of Stimulant Medication in Pediatric Attention-Deficit/Hyperactivity Disorder by Combining Measures From qEEG and ERPs in a Cued GO/NOGO Task.

PubMed

Ogrim, Geir; Kropotov, Juri D

2018-06-01

The study aim was to develop 2 scales: predicting clinical gains and risk of acute side effects of stimulant medication in pediatric attention-deficit/hyperactivity disorder (ADHD), combining measures from EEG spectra, event-related potentials (ERPs), and a cued visual GO/NOGO task. Based on 4-week systematic medication trials, 87 ADHD patients aged 8 to 17 years were classified as responders (REs, n = 62) or non-REs (n = 25), and belonging to the side effects (SEs, n = 42) or no-SEs (n = 45) groups. Before starting the trial, a 19-channel EEG was registered twice: Test 1 (T1) without medication and T2 on a single dose of stimulant medication a few days before the trial. EEG was registered T1 and T2: 3 minutes eyes-closed, 3 minutes eyes-open, and 20 minutes cued GO/NOGO. EEG spectra, ERPs, omissions, commissions, reaction time (RT), and RT variability were computed. Groups were compared at T1 and T2 on quantitative EEG (qEEG), ERPs and behavioral parameters; effect sizes ( d) were estimated. Variables with d > 0.5 were converted to quartiles, multiplied by corresponding d, and summed to obtain 2 global scales. Six variables differed significantly between REs and non-REs (T1: theta/alpha ratio, P3NOGO amplitude. Differences T2-T1: Omissions, RT variability, P3NOGO, contingent negative variation [CNV]). The global scale d was 1.86. Accuracy (receiver operating characteristic) was 0.92. SEs and no-SEs differed significantly on 4 variables. (T1: RT, T2: novelty component and alpha peak frequency, and RT changes. Global scale d = 1.08 and accuracy = 0.78. Gains and side effects of stimulants in pediatric ADHD can be predicted with high accuracy by combining EEG spectra, ERPs, and behavior from baseline and single-dose tests. ClinicalTrials.gov identifier: NCT02695355.

Variable reactivity of particulate organic matter in a global ocean biogeochemical model

NASA Astrophysics Data System (ADS)

Aumont, Olivier; van Hulten, Marco; Roy-Barman, Matthieu; Dutay, Jean-Claude; Éthé, Christian; Gehlen, Marion

2017-05-01

The marine biological carbon pump is dominated by the vertical transfer of particulate organic carbon (POC) from the surface ocean to its interior. The efficiency of this transfer plays an important role in controlling the amount of atmospheric carbon that is sequestered in the ocean. Furthermore, the abundance and composition of POC is critical for the removal of numerous trace elements by scavenging, a number of which, such as iron, are essential for the growth of marine organisms, including phytoplankton. Observations and laboratory experiments have shown that POC is composed of numerous organic compounds that can have very different reactivities. However, this variable reactivity of POC has never been extensively considered, especially in modelling studies. Here, we introduced in the global ocean biogeochemical model NEMO-PISCES a description of the variable composition of POC based on the theoretical reactivity continuum model proposed by Boudreau and Ruddick (1991). Our model experiments show that accounting for a variable lability of POC increases POC concentrations in the ocean's interior by 1 to 2 orders of magnitude. This increase is mainly the consequence of a better preservation of small particles that sink slowly from the surface. Comparison with observations is significantly improved both in abundance and in size distribution. Furthermore, the amount of carbon that reaches the sediments is increased by more than a factor of 2, which is in better agreement with global estimates of the sediment oxygen demand. The impact on the major macronutrients (nitrate and phosphate) remains modest. However, iron (Fe) distribution is strongly altered, especially in the upper mesopelagic zone as a result of more intense scavenging: vertical gradients in Fe are milder in the upper ocean, which appears to be closer to observations. Thus, our study shows that the variable lability of POC can play a critical role in the marine biogeochemical cycles which advocates for more dedicated in situ and laboratory experiments.

Ad hoc committee on global climate issues: Annual report

USGS Publications Warehouse

Gerhard, L.C.; Hanson, B.M.B.

2000-01-01

The AAPG Ad Hoc Committee on Global Climate Issues has studied the supposition of human-induced climate change since the committee's inception in January 1998. This paper details the progress and findings of the committee through June 1999. At that time there had been essentially no geologic input into the global climate change debate. The following statements reflect the current state of climate knowledge from the geologic perspective as interpreted by the majority of the committee membership. The committee recognizes that new data could change its conclusions. The earth's climate is constantly changing owing to natural variability in earth processes. Natural climate variability over recent geological time is greater than reasonable estimates of potential human-induced greenhouse gas changes. Because no tool is available to test the supposition of human-induced climate change and the range of natural variability is so great, there is no discernible human influence on global climate at this time.

Multidecadal climate variability of global lands and oceans

USGS Publications Warehouse

McCabe, G.J.; Palecki, M.A.

2006-01-01

Principal components analysis (PCA) and singular value decomposition (SVD) are used to identify the primary modes of decadal and multidecadal variability in annual global Palmer Drought Severity Index (PDSI) values and sea-surface temperature (SSTs). The PDSI and SST data for 1925-2003 were detrended and smoothed (with a 10-year moving average) to isolate the decadal and multidecadal variability. The first two principal components (PCs) of the PDSI PCA explained almost 38% of the decadal and multidecadal variance in the detrended and smoothed global annual PDSI data. The first two PCs of detrended and smoothed global annual SSTs explained nearly 56% of the decadal variability in global SSTs. The PDSI PCs and the SST PCs are directly correlated in a pairwise fashion. The first PDSI and SST PCs reflect variability of the detrended and smoothed annual Pacific Decadal Oscillation (PDO), as well as detrended and smoothed annual Indian Ocean SSTs. The second set of PCs is strongly associated with the Atlantic Multidecadal Oscillation (AMO). The SVD analysis of the cross-covariance of the PDSI and SST data confirmed the close link between the PDSI and SST modes of decadal and multidecadal variation and provided a verification of the PCA results. These findings indicate that the major modes of multidecadal variations in SSTs and land-surface climate conditions are highly interrelated through a small number of spatially complex but slowly varying teleconnections. Therefore, these relations may be adaptable to providing improved baseline conditions for seasonal climate forecasting. Published in 2006 by John Wiley & Sons, Ltd.

Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis

USGS Publications Warehouse

Bartlein, P.J.; Harrison, S.P.; Brewer, Sandra; Connor, S.; Davis, B.A.S.; Gajewski, K.; Guiot, J.; Harrison-Prentice, T. I.; Henderson, A.; Peyron, O.; Prentice, I.C.; Scholze, M.; Seppa, H.; Shuman, B.; Sugita, S.; Thompson, R.S.; Viau, A.E.; Williams, J.; Wu, H.

2010-01-01

Subfossil pollen and plant macrofossil data derived from 14C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing-season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern-analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance.

Joint spatiotemporal variability of global sea surface temperatures and global Palmer drought severity index values

USGS Publications Warehouse

Apipattanavis, S.; McCabe, G.J.; Rajagopalan, B.; Gangopadhyay, S.

2009-01-01

Dominant modes of individual and joint variability in global sea surface temperatures (SST) and global Palmer drought severity index (PDSI) values for the twentieth century are identified through a multivariate frequency domain singular value decomposition. This analysis indicates that a secular trend and variability related to the El Niño–Southern Oscillation (ENSO) are the dominant modes of variance shared among the global datasets. For the SST data the secular trend corresponds to a positive trend in Indian Ocean and South Atlantic SSTs, and a negative trend in North Pacific and North Atlantic SSTs. The ENSO reconstruction shows a strong signal in the tropical Pacific, North Pacific, and Indian Ocean regions. For the PDSI data, the secular trend reconstruction shows high amplitudes over central Africa including the Sahel, whereas the regions with strong ENSO amplitudes in PDSI are the southwestern and northwestern United States, South Africa, northeastern Brazil, central Africa, the Indian subcontinent, and Australia. An additional significant frequency, multidecadal variability, is identified for the Northern Hemisphere. This multidecadal frequency appears to be related to the Atlantic multidecadal oscillation (AMO). The multidecadal frequency is statistically significant in the Northern Hemisphere SST data, but is statistically nonsignificant in the PDSI data.

Using atmospheric 14CO to constrain OH variability: concept and potential for future measurements

NASA Astrophysics Data System (ADS)

Petrenko, V. V.; Murray, L. T.; Smith, A. W.

2017-12-01

The primary source of 14C-containing carbon monoxide (14CO) in the atmosphere is via 14C production from 14N by secondary cosmic rays, and the primary sink is removal by OH. Variations in the global abundance of 14CO that are not explained by variations in 14C production are mainly driven by variations in the global abundance of OH. Monitoring OH variability via methyl chloroform is becoming increasingly difficult as methyl chloroform abundance is continuing to decline. Measurements of atmospheric 14CO have previously been successfully used to infer OH variability. However, these measurements are currently only continuing at one location (Baring Head, New Zealand), which is insufficient to infer global trends. We propose to restart global 14CO monitoring with the aim of providing another constraint on OH variability. A new analytical system for 14CO sampling and measurements is in development, which will allow to strongly reduce the required sample air volumes (previously ≥ 400 L) and simplify field logistics. A set of test measurements is planned, with sampling at the Mauna Loa Observatory. Preliminary work with a state-of-the-art chemical transport model is identifying the most promising locations for global 14CO sampling.

A global dataset of sub-daily rainfall indices

NASA Astrophysics Data System (ADS)

Fowler, H. J.; Lewis, E.; Blenkinsop, S.; Guerreiro, S.; Li, X.; Barbero, R.; Chan, S.; Lenderink, G.; Westra, S.

2017-12-01

It is still uncertain how hydrological extremes will change with global warming as we do not fully understand the processes that cause extreme precipitation under current climate variability. The INTENSE project is using a novel and fully-integrated data-modelling approach to provide a step-change in our understanding of the nature and drivers of global precipitation extremes and change on societally relevant timescales, leading to improved high-resolution climate model representation of extreme rainfall processes. The INTENSE project is in conjunction with the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes' and the Global Water and Energy Exchanges Project (GEWEX) Science questions. A new global sub-daily precipitation dataset has been constructed (data collection is ongoing). Metadata for each station has been calculated, detailing record lengths, missing data, station locations. A set of global hydroclimatic indices have been produced based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. This will provide a unique global data resource on sub-daily precipitation whose derived indices will be freely available to the wider scientific community.

Water management in the Roman world

NASA Astrophysics Data System (ADS)

Dermody, Brian J.; van Beek, Rens L. P. H.; Meeks, Elijah; Klein Goldewijk, Kees; Bierkens, Marc F. P.; Scheidel, Walter; Wassen, Martin J.; van der Velde, Ype; Dekker, Stefan C.

2014-05-01

Climate variability can have extreme impacts on societies in regions that are water-limited for agriculture. A society's ability to manage its water resources in such environments is critical to its long-term viability. Water management can involve improving agricultural yields through in-situ irrigation or redistributing water resources through trade in food. Here, we explore how such water management strategies affected the resilience of the Roman Empire to climate variability in the water-limited region of the Mediterranean. Using the large-scale hydrological model PCR-GLOBWB and estimates of landcover based on the Historical Database of the Global Environment (HYDE) we generate potential agricultural yield maps under variable climate. HYDE maps of population density in conjunction with potential yield estimates are used to develop maps of agricultural surplus and deficit. The surplus and deficit regions are abstracted to nodes on a water redistribution network based on the Stanford Geospatial Network Model of the Roman World (ORBIS). This demand-driven, water redistribution network allows us to quantitatively explore how water management strategies such as irrigation and food trade improved the resilience of the Roman Empire to climate variability.

The Global Integrated Drought Monitoring and Prediction System (GIDMaPS): Overview and Capabilities

NASA Astrophysics Data System (ADS)

AghaKouchak, A.; Hao, Z.; Farahmand, A.; Nakhjiri, N.

2013-12-01

Development of reliable monitoring and prediction indices and tools are fundamental to drought preparedness and management. Motivated by the Global Drought Information Systems (GDIS) activities, this paper presents the Global Integrated Drought Monitoring and Prediction System (GIDMaPS) which provides near real-time drought information using both remote sensing observations and model simulations. The monthly data from the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-Land), North American Land Data Assimilation System (NLDAS), and remotely sensed precipitation data are used as input to GIDMaPS. Numerous indices have been developed for drought monitoring based on various indicator variables (e.g., precipitation, soil moisture, water storage). Defining droughts based on a single variable (e.g., precipitation, soil moisture or runoff) may not be sufficient for reliable risk assessment and decision making. GIDMaPS provides drought information based on multiple indices including Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI) and the Multivariate Standardized Drought Index (MSDI) which combines SPI and SSI probabilistically. In other words, MSDI incorporates the meteorological and agricultural drought conditions for overall characterization of droughts. The seasonal prediction component of GIDMaPS is based on a persistence model which requires historical data and near-past observations. The seasonal drought prediction component is based on two input data sets (MERRA and NLDAS) and three drought indicators (SPI, SSI and MSDI). The drought prediction model provides the empirical probability of drought for different severity levels. In this presentation, both monitoring and prediction components of GIDMaPS will be discussed, and the results from several major droughts including the 2013 Namibia, 2012-2013 United States, 2011-2012 Horn of Africa, and 2010 Amazon Droughts will be presented. The results indicate that GIDMaPS advances our drought monitoring and prediction capabilities through integration of multiple data and indicators.

Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays.

PubMed

Wang, Leimin; Zeng, Zhigang; Ge, Ming-Feng; Hu, Junhao

2018-05-02

This paper deals with the stabilization problem of memristive recurrent neural networks with inertial items, discrete delays, bounded and unbounded distributed delays. First, for inertial memristive recurrent neural networks (IMRNNs) with second-order derivatives of states, an appropriate variable substitution method is invoked to transfer IMRNNs into a first-order differential form. Then, based on nonsmooth analysis theory, several algebraic criteria are established for the global stabilizability of IMRNNs under proposed feedback control, where the cases with both bounded and unbounded distributed delays are successfully addressed. Finally, the theoretical results are illustrated via the numerical simulations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modified GMDH-NN algorithm and its application for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Song, Shufang; Wang, Lu

2017-11-01

Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages.

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

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.

A particle swarm optimization variant with an inner variable learning strategy.

PubMed

Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

2014-01-01

Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

Welcome to NASA's Earth Science Enterprise. Version 3

NASA Technical Reports Server (NTRS)

2001-01-01

There are strong scientific indications that natural change in the Earth system is being accelerated by human intervention. As a result, planet Earth faces the possibility of rapid environmental changes that would have a profound impact on all nations. However, we do not fully understand either the short-term effects of our activities, or their long-term implications - many important scientific questions remain unanswered. The National Aeronautics and Space Administration (NASA) is working with the national and international scientific communities to establish a sound scientific basis for addressing these critical issues through research efforts coordinated under the U.S. Global Change Research Program, the International Geosphere-Biosphere Program, and the World Climate Research Program. The Earth Science Enterprise is NASA's contribution to the U.S. Global Change Research Program. NASA's Earth Science Enterprise will use space- and surface-based measurement systems to provide the scientific basis for understanding global change. The space-based components will provide a constellation of satellites to monitor the Earth from space. A major component of the Earth Science Enterprise is the Earth Observing System (EOS). The overall objective of the EOS Program is to determine the extent, causes, and regional consequences of global climate change. EOS will provide sustained space-based observations that will allow researchers to monitor climate variables over time to determine trends. A constellation of EOS satellites will acquire global data, beginning in 1998 and extending well into the 21st century.

Future of endemic flora of biodiversity hotspots in India.

PubMed

Chitale, Vishwas Sudhir; Behera, Mukund Dev; Roy, Partha Sarthi

2014-01-01

India is one of the 12 mega biodiversity countries of the world, which represents 11% of world's flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models.

Future of Endemic Flora of Biodiversity Hotspots in India

PubMed Central

Chitale, Vishwas Sudhir; Behera, Mukund Dev; Roy, Partha Sarthi

2014-01-01

India is one of the 12 mega biodiversity countries of the world, which represents 11% of world's flora in about 2.4% of global land mass. Approximately 28% of the total Indian flora and 33% of angiosperms occurring in India are endemic. Higher human population density in biodiversity hotspots in India puts undue pressure on these sensitive eco-regions. In the present study, we predict the future distribution of 637 endemic plant species from three biodiversity hotspots in India; Himalaya, Western Ghats, Indo-Burma, based on A1B scenario for year 2050 and 2080. We develop individual variable based models as well as mixed models in MaxEnt by combining ten least co-related bioclimatic variables, two disturbance variables and one physiography variable as predictor variables. The projected changes suggest that the endemic flora will be adversely impacted, even under such a moderate climate scenario. The future distribution is predicted to shift in northern and north-eastern direction in Himalaya and Indo-Burma, while in southern and south-western direction in Western Ghats, due to cooler climatic conditions in these regions. In the future distribution of endemic plants, we observe a significant shift and reduction in the distribution range compared to the present distribution. The model predicts a 23.99% range reduction and a 7.70% range expansion in future distribution by 2050, while a 41.34% range reduction and a 24.10% range expansion by 2080. Integration of disturbance and physiography variables along with bioclimatic variables in the models improved the prediction accuracy. Mixed models provide most accurate results for most of the combinations of climatic and non-climatic variables as compared to individual variable based models. We conclude that a) regions with cooler climates and higher moisture availability could serve as refugia for endemic plants in future climatic conditions; b) mixed models provide more accurate results, compared to single variable based models. PMID:25501852

Tropical rainforests dominate multi-decadal variability of the global carbon cycle

NASA Astrophysics Data System (ADS)

Zhang, X.; Wang, Y. P.; Peng, S.; Rayner, P. J.; Silver, J.; Ciais, P.; Piao, S.; Zhu, Z.; Lu, X.; Zheng, X.

2017-12-01

Recent studies find that inter-annual variability of global atmosphere-to-land CO2 uptake (NBP) is dominated by semi-arid ecosystems. However, the NBP variations at decadal to multi-decadal timescales are still not known. By developing a basic theory for the role of net primary production (NPP) and heterotrophic respiration (Rh) on NBP and applying it to 100-year simulations of terrestrial ecosystem models forced by observational climate, we find that tropical rainforests dominate the multi-decadal variability of global NBP (48%) rather than the semi-arid lands (35%). The NBP variation at inter-annual timescales is almost 90% contributed by NPP, but across longer timescales is progressively controlled by Rh that constitutes the response from the NPP-derived soil carbon input (40%) and the response of soil carbon turnover rates to climate variability (60%). The NBP variations of tropical rainforests is modulated by the ENSO and the PDO through their significant influences on temperature and precipitation at timescales of 2.5-7 and 25-50 years, respectively. This study highlights the importance of tropical rainforests on the multi-decadal variability of global carbon cycle, suggesting that we need to carefully differentiate the effect of NBP long-term fluctuations associated with ocean-related climate modes on the long-term trend in land sink.

Examining the last few decades of global hydroclimate for evidence of anthropogenic change amidst natural variability

NASA Astrophysics Data System (ADS)

Seager, R.; Naik, N.; Ting, M.; Kushnir, Y.; Kelley, C. P.

2011-12-01

Climate models robustly predict that the deep tropics and mid-latitude-to-subpolar regions will moisten, and the subtropical dry zones both dry and expand, as a consequence of global warming driven by rising greenhouse gases. The models also predict that this transition to a more extreme climatological mean global hydroclimate should already be underway. Given the importance of these predictions it is an imperative that the climate science community assess whether there is evidence within the observational record that they are correct. This task is made difficult by the tremendous natural variability of the hydrological cycle on seasonal to multidecadal timescales. Here we will use instrumental observations, reanalyses, sea surface temperature forced atmosphere models and coupled model simulations, and a variety of methodologies, to attempt to separate global radiatively-forced hydroclimate change from ongoing natural variability. The results will be applied to explain trends and recent events in key regions such as Mexico, the United States and the Mediterranean. It is concluded that the signal of anthropogenic change is small compared to the amplitude of natural variability but that it is a discernible contributor. Globally the evidence reveals that radiatively-forced hydroclimate change is occurring with an amplitude and spatial pattern largely consistent with the predictions by IPCC AR4 models of hydroclimate change to date. However it will also be shown that the radiatively-forced component does not in and of itself provide a useful prediction of near term hydroclimate change because for many regions the amplitude of natural decadal variability is as large or larger. Useful predictions need to account for how natural variability may evolve as well as forced change.

Estimating geocenter motion and barystatic sea-level variability from GRACE observations with explicit consideration of self-attraction and loading effects

NASA Astrophysics Data System (ADS)

Bergmann-Wolf, I.; Dobslaw, H.

2015-12-01

Estimating global barystatic sea-level variations from monthly mean gravity fields delivered by the Gravity Recovery and Climate Experiment (GRACE) satellite mission requires additional information about geocenter motion. These variations are not available directly due to the mission implementation in the CM-frame and are represented by the degree-1 terms of the spherical harmonics expansion. Global degree-1 estimates can be determined with the method of Swenson et al. (2008) from ocean mass variability, the geometry of the global land-sea distribution, and GRACE data of higher degrees and orders. Consequently, a recursive relation between the derivation of ocean mass variations from GRACE data and the introduction of geocenter motion into GRACE data exists.In this contribution, we will present a recent improvement to the processing strategy described in Bergmann-Wolf et al. (2014) by introducing a non-homogeneous distribution of global ocean mass variations in the geocenter motion determination strategy, which is due to the effects of loading and self-attraction induced by mass redistributions at the surface. A comparison of different GRACE-based oceanographic products (barystatic signal for both the global oceans and individual basins; barotropic transport variations of major ocean currents) with degree-1 terms estimated with a homogeneous and non-homogeneous ocean mass representation will be discussed, and differences in noise levels in most recent GRACE solutions from GFZ (RL05a), CSR, and JPL (both RL05) and their consequences for the application of this method will be discussed.

Linkages Between Multiscale Global Sea Surface Temperature Change and Precipitation Variabilities in the US

NASA Technical Reports Server (NTRS)

Lau, K. M.; Weng, Heng-Yi

1999-01-01

A growing number of evidence indicates that there are coherent patterns of variability in sea surface temperature (SST) anomaly not only at interannual timescales, but also at decadal-to-inter-decadal timescale and beyond. The multi-scale variabilities of SST anomaly have shown great impacts on climate. In this work, we analyze multiple timescales contained in the globally averaged SST anomaly with and their possible relationship with the summer and winter rainfall in the United States over the past four decades.

Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Pincus, R.

2016-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation and cloudiness. Unlike other similar methods, only one new prognostic variable, turbulent kinetic energy (TKE), needs to be intoduced, making the technique computationally efficient.SHOC is now incorporated into a version of GFS, as well as into the next generation of the NCEP global model - NOAA Environmental Modeling System (NEMS). Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these variables. Radiative transfer parameterization uses cloudiness computed by SHOC.Outstanding problems include high level tropical cloud fraction being too high in SHOC runs, possibly related to the interaction of SHOC with condensate detrained from deep convection.Future work will consist of evaluating model performance and tuning the physics if necessary, by performing medium-range NWP forecasts with prescribed initial conditions, and AMIP-type climate tests with prescribed SSTs. Depending on the results, the model will be tuned or parameterizations modified. Next, SHOC will be implemented in the NCEP CFS, and tuned and evaluated for climate applications - seasonal prediction and long coupled climate runs. Impact of new physics on ENSO, MJO, ISO, monsoon variability, etc will be examined.

Vulnerability to global environmental changes in Argentina: opportunities for upgrading regional water resources management strategies.

PubMed

Bereciartua, P J

2005-01-01

There is evidence of the increasing economic losses from extreme natural events during the last decades. These facts, thought to be triggered by environmental changes coupled with inefficient management and policies, highlight particularly exposed and vulnerable regions worldwide. Argentina faces several challenges associated with global environmental change and climate variability, especially related to water resources management including extreme floods and droughts. At the same time, the country's production capacity (i.e. natural resource-based commodities) and future development opportunities are closely tied to the sustainable development of its natural resource endowments. Given that vulnerability is registered not only by exposure to hazards (perturbations and stresses), but also resides in the sensitivity and resilience of the system experiencing such hazards, Argentina will need to improve its water management capacities to reduce its vulnerability to climate variability and change. This paper presents the basic components of the vulnerability analysis and suggests how it can be used to define efficient water management options.

"Control-alt-delete": rebooting solutions for the E-waste problem.

PubMed

Li, Jinhui; Zeng, Xianlai; Chen, Mengjun; Ogunseitan, Oladele A; Stevels, Ab

2015-06-16

A number of efforts have been launched to solve the global electronic waste (e-waste) problem. The efficiency of e-waste recycling is subject to variable national legislation, technical capacity, consumer participation, and even detoxification. E-waste management activities result in procedural irregularities and risk disparities across national boundaries. We review these variables to reveal opportunities for research and policy to reduce the risks from accumulating e-waste and ineffective recycling. Full regulation and consumer participation should be controlled and reinforced to improve local e-waste system. Aiming at standardizing best practice, we alter and identify modular recycling process and infrastructure in eco-industrial parks that will be expectantly effective in countries and regions to handle the similar e-waste stream. Toxicity can be deleted through material substitution and detoxification during the life cycle of electronics. Based on the idea of "Control-Alt-Delete", four patterns of the way forward for global e-waste recycling are proposed to meet a variety of local situations.

Diagnosis of Swallowing Disorders: How We Interpret Pharyngeal Manometry.

PubMed

Cock, Charles; Omari, Taher

2017-03-01

We provide an overview of the clinical application of novel pharyngeal high-resolution impedance manometry (HRIM) with pressure flow analysis (PFA) in our hands with example cases. In our Centre, we base our interpretation of HRIM recordings upon a qualitative assessment of pressure-impedance waveforms during individual swallows, as well as a quantitative assessment of averaged PFA swallow function variables. We provide a description of two global swallowing efficacy measures, the swallow risk index (SRI), reflecting global swallowing dysfunction (higher SRI = greater aspiration risk) and the post-swallow impedance ratio (PSIR) detecting significant post-swallow bolus residue. We describe a further eight swallow function variables specific to the hypopharynx and upper esophageal sphincter (UES), assessing hypo-pharyngeal distension pressure, contractility, bolus presence and flow timing, and UES basal tone, relaxation, opening and contractility. Pharyngeal HRIM has now come of age, being applicable for routine clinical practice to assess the biomechanics of oropharyngeal swallowing dysfunction. In the future, it may guide treatment strategies and allow more objective longitudinal follow-up on clinical outcomes.

The GISS global climate-middle atmosphere model. II - Model variability due to interactions between planetary waves, the mean circulation and gravity wave drag

NASA Technical Reports Server (NTRS)

Rind, D.; Suozzo, R.; Balachandran, N. K.

1988-01-01

The variability which arises in the GISS Global Climate-Middle Atmosphere Model on two time scales is reviewed: interannual standard deviations, derived from the five-year control run, and intraseasonal variability as exemplified by statospheric warnings. The model's extratropical variability for both mean fields and eddy statistics appears reasonable when compared with observations, while the tropical wind variability near the stratopause may be excessive possibly, due to inertial oscillations. Both wave 1 and wave 2 warmings develop, with connections to tropospheric forcing. Variability on both time scales results from a complex set of interactions among planetary waves, the mean circulation, and gravity wave drag. Specific examples of these interactions are presented, which imply that variability in gravity wave forcing and drag may be an important component of the variability of the middle atmosphere.

Children who screen positive for autism at 2.5 years and receive early intervention: a prospective naturalistic 2-year outcome study

PubMed Central

Spjut Jansson, Birgitta; Miniscalco, Carmela; Westerlund, Joakim; Kantzer, Anne-Katrin; Fernell, Elisabeth; Gillberg, Christopher

2016-01-01

Background Previous research has stressed the importance of early identification and intervention for children with autism spectrum disorders. Methods Children who had screened positive for autism at the age of 2.5 years in a general population screening and then received a diagnosis of autism spectrum disorder were enrolled in an intervention program provided by Swedish habilitation services. The following interventions were available: a comprehensive intervention based on Applied Behavior Analysis – Intensive Learning (IL) – in two settings, which included home- and preschool-based (IL Regular) and only home-based (IL Modified) and eclectic interventions. Results There was considerable variability in terms of outcome, but intervention group status was not associated with any of the chosen outcome variables. Conclusion The main finding was that the type of intervention was not critical for outcome of adaptive or global functioning. The variability in outcome demonstrates the need for continuous assessments and evaluation of the child’s function and behavior throughout the intervention period. PMID:27621636

Constrained variability of modeled T:ET ratio across biomes

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Pappas, Christoforos

2017-07-01

A large variability (35-90%) in the ratio of transpiration to total evapotranspiration (referred here as T:ET) across biomes or even at the global scale has been documented by a number of studies carried out with different methodologies. Previous empirical results also suggest that T:ET does not covary with mean precipitation and has a positive dependence on leaf area index (LAI). Here we use a mechanistic ecohydrological model, with a refined process-based description of evaporation from the soil surface, to investigate the variability of T:ET across biomes. Numerical results reveal a more constrained range and higher mean of T:ET (70 ± 9%, mean ± standard deviation) when compared to observation-based estimates. T:ET is confirmed to be independent from mean precipitation, while it is found to be correlated with LAI seasonally but uncorrelated across multiple sites. Larger LAI increases evaporation from interception but diminishes ground evaporation with the two effects largely compensating each other. These results offer mechanistic model-based evidence to the ongoing research about the patterns of T:ET and the factors influencing its magnitude across biomes.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Advances In Understanding Global Water Cycle With Advent of GPM Mission

NASA Technical Reports Server (NTRS)

Smith, Eric A.

2002-01-01

During the coming decade, the internationally organized Global Precipitation Measurement (GPM) Mission will take an important step in creating a global precipitation observing system from space based on an international fleet of satellites operated as a constellation. One perspective for understanding the nature of GPM is that it will be a hierarchical system of datastreams beginning with very high caliber combined dual frequency radar/passive microwave (PMW) rain-radiometer retrievals, to high caliber PMW rain-radiometer only retrievals, and then on to blends of the former datastreams with additional lower-caliber PMW-based and IR-based rain retrievals. Within the context of the now emerging global water & energy cycle (GWEC) programs of a number of research agencies throughout the world, GPM serves as a centerpiece space mission for improving our understanding of the Earth's water cycle from a global measurement perspective and on down to regional scales and below. One of the salient problems within our current understanding of the global water and energy cycle is determining whether a change in the rate of the water cycle is accompanying changes in climate, e.g., climate warming. As there are a number of ways in which to define a rate-change of the global water cycle, it is not entirely clear as to what constitutes such a determination. This paper first presents an overview of the GPM Mission and how its overriding scientific objectives for climate, weather, and hydrology flow from the anticipated improvements that are being planned for the constellation-based measuring system. Next, the paper shows how the GPM observations can be used within the framework of the oceanic and continental water budget equations to determine whether a given perturbation in precipitation is indicative of an actual rate change in the water cycle, consistent with required responses in water storage and/or water flux transport processes, or whether it is simply part of the natural variability of a fixed rate cycle.

Evaluating the Contribution of Natural Variability and Climate Model Response to Uncertainty in Projections of Climate Change Impacts on U.S. Air Quality

EPA Science Inventory

We examine the effects of internal variability and model response in projections of climate impacts on U.S. ground-level ozone across the 21st century using integrated global system modeling and global atmospheric chemistry simulations. The impact of climate change on air polluti...

GEWEX Water and Energy Budget Study

NASA Technical Reports Server (NTRS)

Roads, J.; Bainto, E.; Masuda, K.; Rodell, Matthew; Rossow, W. B.

2008-01-01

Closing the global water and energy budgets has been an elusive Global Energy and Water-cycle Experiment (GEWEX) goal. It has been difficult to gather many of the needed global water and energy variables and processes, although, because of GEWEX, we now have globally gridded observational estimates for precipitation and radiation and many other relevant variables such as clouds and aerosols. Still, constrained models are required to fill in many of the process and variable gaps. At least there are now several atmospheric reanalyses ranging from the early National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and NCEP/Department of Energy (DOE) reanalyses to the more recent ERA40 and JRA-25 reanalyses. Atmospheric constraints include requirements that the models state variables remain close to in situ observations or observed satellite radiances. This is usually done by making short-term forecasts from an analyzed initial state; these short-term forecasts provide the next guess, which is corrected by comparison to available observations. While this analysis procedure is likely to result in useful global descriptions of atmospheric temperature, wind and humidity, there is no guarantee that relevant hydroclimate processes like precipitation, which we can observe and evaluate, and evaporation over land, which we cannot, have similar verisimilitude. Alternatively, the Global Land Data Assimilation System (GLDAS), drives uncoupled land surface models with precipitation, surface solar radiation, and surface meteorology (from bias-corrected reanalyses during the study period) to simulate terrestrial states and surface fluxes. Further constraints are made when a tuned water balance model is used to characterize the global runoff observational estimates. We use this disparate mix of observational estimates, reanalyses, GLDAS and calibrated water balance simulations to try to characterize and close global and terrestrial atmospheric and surface water and energy budgets to within 10-20% for long term (1986-1995), large-scale global to regional annual means.

How Accurate is Land/Ocean Moisture Transport Variability in Reanalyses?

NASA Technical Reports Server (NTRS)

Robertson, F. R.; Bosilovich, M. G.

2014-01-01

Quantifying the global hydrological cycle and its variability across various time scales remains a challenge to the climate community. Direct measurements of evaporation (E), evapotranspiration (ET), and precipitation (P) are not feasible on a global scale, nor is the transport of water vapor over the global oceans and sparsely populated land areas. Expanding satellite data streams have enabled development of various water (and energy) flux products, complementing reanalyses and facilitating observationally constrained modeling. But the evolution of the global observing system has produced additional complications--improvements in satellite sensor resolution and accuracy have resulted in "epochs" of observational quasi-uniformity that can adversely affect reanalysis trends. In this work we focus on vertically integrated moisture flux convergence (VMFC) variations within the period 1979 - present integrated over global land. We show that VMFC in recent reanalyses (e.g. ERA-I, NASA MERRA, NOAA CFSR and JRA55) suffers from observing system changes, though differently in each product. Land Surface Models (LSMs) forced with observations-based precipitation, radiation and near-surface meteorology share closely the interannual P-ET variations of the reanalyses associated with ENSO events. (VMFC over land and P-ET estimates are equivalent quantities since atmospheric storage changes are small on these scales.) But the long-term LSM trend over the period since 1979 is approximately one-fourth that of the reanalyses. Additional reduced observation reanalyses assimilating only surface pressure and /or specifying seasurface temperature also have a much smaller trend in P-ET like the LSMs. We explore the regional manifestation of the reanalysis P-ET / VMFC problems, particularly over land. Both principal component analysis and a simple time series changepoint analysis highlight problems associated with data poor regions such as Equatorial Africa and, for one reanalysis, the Equatorial Andes region. Onset of the availability of passive microwave Special Sensor Microwave Imager (SSMI) moisture data in July 1987 and the transition from the Microwave Sounder Unit (MSU) to an advanced version (AMSU) have significant impacts on VMFC variability. Simple accounting for these errors of leading importance results in modified reanalysis VMFC estimates that agree much better with the LSM results. Regional details of the modified reanalysis VMFC and LSM P-ET are related to changes in Pacific Decadal Variability as manifest in SST changes after the late 1990s.

The Tc1/mariner transposable element family shapes genetic variation and gene expression in the protist Trichomonas vaginalis

PubMed Central

2014-01-01

Background Trichomonas vaginalis is the most prevalent non-viral sexually transmitted parasite. Although the protist is presumed to reproduce asexually, 60% of its haploid genome contains transposable elements (TEs), known contributors to genome variability. The availability of a draft genome sequence and our collection of >200 global isolates of T. vaginalis facilitate the study and analysis of TE population dynamics and their contribution to genomic variability in this protist. Results We present here a pilot study of a subset of class II Tc1/mariner TEs that belong to the T. vaginalis Tvmar1 family. We report the genetic structure of 19 Tvmar1 loci, their ability to encode a full-length transposase protein, and their insertion frequencies in 94 global isolates from seven regions of the world. While most of the Tvmar1 elements studied exhibited low insertion frequencies, two of the 19 loci (locus 1 and locus 9) show high insertion frequencies of 1.00 and 0.96, respectively. The genetic structuring of the global populations identified by principal component analysis (PCA) of the Tvmar1 loci is in general agreement with published data based on genotyping, showing that Tvmar1 polymorphisms are a robust indicator of T. vaginalis genetic history. Analysis of expression of 22 genes flanking 13 Tvmar1 loci indicated significantly altered expression of six of the genes next to five Tvmar1 insertions, suggesting that the insertions have functional implications for T. vaginalis gene expression. Conclusions Our study is the first in T. vaginalis to describe Tvmar1 population dynamics and its contribution to genetic variability of the parasite. We show that a majority of our studied Tvmar1 insertion loci exist at very low frequencies in the global population, and insertions are variable between geographical isolates. In addition, we observe that low frequency insertion is related to reduced or abolished expression of flanking genes. While low insertion frequencies might be expected, we identified two Tvmar1 insertion loci that are fixed across global populations. This observation indicates that Tvmar1 insertion may have differing impacts and fitness costs in the host genome and may play varying roles in the adaptive evolution of T. vaginalis. PMID:24834134

The Effect of the Interannual Variability of the OH Sink on the Interannual Variability of the Atmospheric Methane Mixing Ratio and Carbon Stable Isotope Composition

NASA Astrophysics Data System (ADS)

Guillermo Nuñez Ramirez, Tonatiuh; Houweling, Sander; Marshall, Julia; Williams, Jason; Brailsford, Gordon; Schneising, Oliver; Heimann, Martin

2013-04-01

The atmospheric hydroxyl radical concentration (OH) varies due to changes in the incoming UV radiation, in the abundance of atmospheric species involved in the production, recycling and destruction of OH molecules and due to climate variability. Variability in carbon monoxide emissions from biomass burning induced by El Niño Southern Oscillation are particularly important. Although the OH sink accounts for the oxidation of approximately 90% of atmospheric CH4, the effect of the variability in the distribution and strength of the OH sink on the interannual variability of atmospheric methane (CH4) mixing ratio and stable carbon isotope composition (δ13C-CH4) has often been ignored. To show this effect we simulated the atmospheric signals of CH4 in a three-dimensional atmospheric transport model (TM3). ERA Interim reanalysis data provided the atmospheric transport and temperature variability from 1990 to 2010. We performed simulations using time dependent OH concentration estimations from an atmospheric chemistry transport model and an atmospheric chemistry climate model. The models assumed a different set of reactions and algorithms which caused a very different strength and distribution of the OH concentration. Methane emissions were based on published bottom-up estimates including inventories, upscaled estimations and modeled fluxes. The simulations also included modeled concentrations of atomic chlorine (Cl) and excited oxygen atoms (O(1D)). The isotopic signal of the sources and the fractionation factors of the sinks were based on literature values, however the isotopic signal from wetlands and enteric fermentation processes followed a linear relationship with a map of C4 plant fraction. The same set of CH4emissions and stratospheric reactants was used in all simulations. Two simulations were done per OH field: one in which the CH4 sources were allowed to vary interannually, and a second where the sources were climatological. The simulated mixing ratios and isotopic compositions at global reference stations were used to construct more robust indicators such as global and zonal means and interhemispheric differences. We also compared the model CH4 mixing ratio to satellite observations, for the period 2003 to 2004 with SCIAMACHY and from 2009 to 2010 with GOSAT. The interannual variability of the different OH fields imprinted an interannual variation of the atmospheric CH4 mixing ratio with a magnitude of ±10 ppb, which is comparable to the effect of all sources combined. Meanwhile its effect on the interannual variability of δ13C-CH4 was minor (< 10%). The interannual variability of the mixing ratio interhemispheric difference is dominated by the sources because the OH sink is concentrated in the tropics, thus its interannual variability affects both hemispheres. Meanwhile, although the OH plays an important role in the establishment of an interhemispheric gradient of δ13C-CH4, the interannual variation of this gradient is negligibly affected by the choice of OH field. Overall the study showed that the variability of the OH sink plays a significant role in the interannual variability of the atmospheric methane mixing ratio, and must be considered to improve our understanding of the recent trends in the global methane budget.

Modeling contemporary climate profiles of whitebark pine (Pinus albicaulis) and predicting responses to global warming

Treesearch

Marcus V. Warwell; Gerald E. Rehfeldt; Nicholas L. Crookston

2006-01-01

The Random Forests multiple regression tree was used to develop an empirically-based bioclimate model for the distribution of Pinus albicaulis (whitebark pine) in western North America, latitudes 31° to 51° N and longitudes 102° to 125° W. Independent variables included 35 simple expressions of temperature and precipitation and their interactions....

Climate Change, Growth, and Poverty in Ethiopia

DTIC Science & Technology

2013-06-01

agricultural effects of global warming, reflecting their disadvantaged geographic location Higher evaporation and reduced soil moisture can damage crops...Ringler (2007) 5 Temperature, radiation, rainfall, soil moisture , and carbon dioxide (CO2) concentration are important variables that can proxy...iii) rainfall can affect other proxies of climate change in the literature such as soil moisture 6 This is based on FAOstat database 7 According to

Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements

NASA Astrophysics Data System (ADS)

Damiani, Alessandro; Irie, Hitoshi; Horio, Takashi; Takamura, Tamio; Khatri, Pradeep; Takenaka, Hideaki; Nagao, Takashi; Nakajima, Takashi Y.; Cordero, Raul R.

2018-04-01

Observations from the new Japanese geostationary satellite Himawari-8 permit quasi-real-time estimation of global shortwave radiation at an unprecedented temporal resolution. However, accurate comparisons with ground-truthing observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 global radiation product AMATERASS using observations recorded at four SKYNET stations in Japan and, for certain analyses, from the surface network of the Japanese Meteorological Agency in 2016. We found that the spatiotemporal variability of the satellite estimates was smaller than that of the ground observations; variability decreased with increases in the time step and spatial domain. Cloud variability was the main source of uncertainty in the satellite radiation estimates, followed by direct effects caused by aerosols and bright albedo. Under all-sky conditions, good agreement was found between satellite and ground-based data, with a mean bias in the range of 20-30 W m-2 (i.e., AMATERASS overestimated ground observations) and a root mean square error (RMSE) of approximately 70-80 W m-2. However, results depended on the time step used in the validation exercise, on the spatial domain, and on the different climatological regions. In particular, the validation performed at 2.5 min showed largest deviations and RMSE values ranging from about 110 W m-2 for the mainland to a maximum of 150 W m-2 in the subtropical region. We also detected a limited overestimation in the number of clear-sky episodes, particularly at the pixel level. Overall, satellite-based estimates were higher under overcast conditions, whereas frequent episodes of cloud-induced enhanced surface radiation (i.e., measured radiation was greater than expected clear-sky radiation) tended to reduce this difference. Finally, the total mean bias was approximately 10-15 W m-2 under clear-sky conditions, mainly because of overall instantaneous direct aerosol forcing efficiency in the range of 120-150 W m-2 per unit of aerosol optical depth (AOD). A seasonal anticorrelation between AOD and global radiation differences was evident at all stations and was also observed within the diurnal cycle.

A virtual water network of the Roman world

NASA Astrophysics Data System (ADS)

Dermody, B. J.; van Beek, R. P. H.; Meeks, E.; Klein Goldewijk, K.; Scheidel, W.; van der Velde, Y.; Bierkens, M. F. P.; Wassen, M. J.; Dekker, S. C.

2014-12-01

The Romans were perhaps the most impressive exponents of water resource management in preindustrial times with irrigation and virtual water trade facilitating unprecedented urbanization and socioeconomic stability for hundreds of years in a region of highly variable climate. To understand Roman water resource management in response to urbanization and climate variability, a Virtual Water Network of the Roman World was developed. Using this network we find that irrigation and virtual water trade increased Roman resilience to interannual climate variability. However, urbanization arising from virtual water trade likely pushed the Empire closer to the boundary of its water resources, led to an increase in import costs, and eroded its resilience to climate variability in the long term. In addition to improving our understanding of Roman water resource management, our cost-distance-based analysis illuminates how increases in import costs arising from climatic and population pressures are likely to be distributed in the future global virtual water network.

A virtual water network of the Roman world

NASA Astrophysics Data System (ADS)

Dermody, B. J.; van Beek, R. P. H.; Meeks, E.; Klein Goldewijk, K.; Scheidel, W.; van der Velde, Y.; Bierkens, M. F. P.; Wassen, M. J.; Dekker, S. C.

2014-06-01

The Romans were perhaps the most impressive exponents of water resource management in preindustrial times with irrigation and virtual water trade facilitating unprecedented urbanisation and socioeconomic stability for hundreds of years in a region of highly variable climate. To understand Roman water resource management in response to urbanisation and climate variability, a Virtual Water Network of the Roman World was developed. Using this network we find that irrigation and virtual water trade increased Roman resilience to climate variability in the short term. However, urbanisation arising from virtual water trade likely pushed the Empire closer to the boundary of its water resources, led to an increase in import costs, and reduced its resilience to climate variability in the long-term. In addition to improving our understanding of Roman water resource management, our cost-distance based analysis illuminates how increases in import costs arising from climatic and population pressures are likely to be distributed in the future global virtual water network.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

Lateglacial climate reconstruction on the Bolivian Altiplano inferred from paleoglaciers and paleolakes

NASA Astrophysics Data System (ADS)

Martin, Léo; Blard, Pierre-Henri; Lavé, Jérôme; Prémaillon, Mélody; Jomelli, Vincent; Brunstein, Daniel; Lupker, Maarten; Charreau, Julien; Mariotti, Véronique; Condom, Thomas; Bourles, Didier

2016-04-01

Recent insights shed light on the global mechanisms involved in the abrupt oscillations of the Earth climate for the Late Glacial Maximum (LGM) to Holocene period (Zhang et al., 2014; Banderas et al., 2015). Yet the concomitant patterns of regional climate reorganization on continental areas are for now poorly documented. Particularly, few attempts have been made to propose temporal reconstructions of the regional climate variables in the High Tropical Andes, a region under the influence of multiple global climate forcings (Jomelli et al., 2014). We present new glacial chronologies from four sites of the Bolivian Altiplano: the Wara-Wara valley (17.3°S - 66.1°W), the Zongo valley (16.3°S - 68.1°W), the Cerro Tunupa (19.8°S - 67.6°W) and the Nevado Sajama (18.1°S 68.9°W). These chronologies are based on Cosmic Ray Exposure dating (CRE) from an exceptional suite of recessive moraines. These new data permitted to refine existing chronologies of Smith et al., 2005; Zech et al., 2010 and Blard et al., 2009. In both sites, glaciers recorded stillstand episodes synchronous with cold events such as the Henrich 1 event, the Younger Dryas and the Antarctic Cold Reversal. Since the nearby Altiplano basin registered lake level variations over the same period, we were able to apply a joint modelling of glaciers Equilibrium Line Altitude (ELA) and lake budget. This method permits to derive a temporal evolution of temperature and precipitation for the four sites. These new reconstructions show for all sites that glaciers of the Tropical Andes were influenced by the major climatic events of the Northern and Southern Hemispheres. Furthermore, the temperature variability observed at high latitudes results in these tropical latitudes in major precipitation variability whereas the lateglacial temperature patterns remain globally monotonic. This conversion of global temperature variability into regional precipitation variability support the idea that North Hemisphere cold events are coeval with an important southward deflexion of the Intertropical Convergence Zone (ITCZ) due to the inter-hemispheric temperature gradient (Schneider et al., 2014). Such a southward shift would lead to an increased moist supply of the subequatorial Amazonian basin (Montade et al., 2015) and thus an increased easterly driven moist transport over the Altiplano.

Summer Temperature Extremes in the Northern Rockies: A Tree-Ring-Based Reconstruction (1670-2014) from the Bighorn Mountains, WY

NASA Astrophysics Data System (ADS)

Hudson, A.; Alfaro-Sanchez, R.; Belmecheri, S.; Moore, D. J.; Trouet, V.

2017-12-01

Anthropogenic climate change has caused global temperatures to rise in recent decades. Temperatures at the regional scale are influenced by various factors including topography, atmospheric circulation, and seasonality that superimpose year-to-year variability on this global warming trend. Here, we develop a tree-ring based summer temperature reconstruction for the northern Rockies in order to investigate the drivers of the year-to-year temperature variability in this region. For this purpose, we sampled 10 sites in the semi-arid Bighorn Mountains, WY and developed two tree-ring width chronologies for differing elevations. The high elevation Picea engelmannii chronology (>2,630m) is positively correlated with July temperature variability, whereas the low elevation (<2,580m) chronology - consisting of Pinus contorta, Pseudotsuga menziesii, and Pinus albicaulis - is sensitive to summer precipitation and negatively correlated with June and July temperatures. A reconstruction based on a combination of the two chronologies explains 30% of the variance in regional June and July temperatures over the instrumental period, covers the period 1670-2014, and is representative for the central United States and southern Canada region. Our reconstruction shows significantly lower summer temperatures in the year following the 16 largest tropical eruptions from 1670 to the present. The reconstruction further captures the high summer temperatures during the 1930s dust bowl era and shows a steep increase in variance in the late 20th century. Enhanced late 20th century variance has also been detected in climate and ecosystem dynamics in the Northeast Pacific, which suggests an impact of an amplified meridional flow on northern Rockies summer temperatures.

Harmonising and semantically linking key variables from in-situ observing networks of an Integrated Atlantic Ocean Observing System, AtlantOS

NASA Astrophysics Data System (ADS)

Darroch, Louise; Buck, Justin

2017-04-01

Atlantic Ocean observation is currently undertaken through loosely-coordinated, in-situ observing networks, satellite observations and data management arrangements at regional, national and international scales. The EU Horizon 2020 AtlantOS project aims to deliver an advanced framework for the development of an Integrated Atlantic Ocean Observing System that strengthens the Global Ocean Observing System (GOOS) and contributes to the aims of the Galway Statement on Atlantic Ocean Cooperation. One goal is to ensure that data from different and diverse in-situ observing networks are readily accessible and useable to a wider community, including the international ocean science community and other stakeholders in this field. To help achieve this goal, the British Oceanographic Data Centre (BODC) produced a parameter matrix to harmonise data exchange, data flow and data integration for the key variables acquired by multiple in-situ AtlantOS observing networks such as ARGO, Seafloor Mapping and OceanSITES. Our solution used semantic linking of controlled vocabularies and metadata for parameters that were "mappable" to existing EU and international standard vocabularies. An AtlantOS Essential Variables list of terms (aggregated level) based on Global Climate Observing System (GCOS) Essential Climate Variables (ECV), GOOS Essential Ocean Variables (EOV) and other key network variables was defined and published on the Natural Environment Research Council (NERC) Vocabulary Server (version 2.0) as collection A05 (http://vocab.nerc.ac.uk/collection/A05/current/). This new vocabulary was semantically linked to standardised metadata for observed properties and units that had been validated by the AtlantOS community: SeaDataNet parameters (P01), Climate and Forecast (CF) Standard Names (P07) and SeaDataNet units (P06). Observed properties were mapped to biological entities from the internationally assured AphiaID from the WOrld Register of Marine Species (WoRMS), http://www.marinespecies.org/aphia.php?p=webservice. The AtlantOS parameter matrix offers a way to harmonise the globally important variables (such as ECVs and EOVs) from in-situ observing networks that use different flavours of exchange formats based on SeaDataNet and CF parameter metadata. It also offers a way to standardise data in the wider Integrated Ocean Observing System. It uses sustainable and trusted standardised vocabularies that are governed by internationally renowned and long-standing organisations and is interoperable through the use of persistent resource identifiers, such as URNs and PURLs. It is the first step to integrating and serving data in a variety of international exchange formats using Application programming interfaces (API) improving both data discoverability and utility for users.

Multisource Estimation of Long-term Global Terrestrial Surface Radiation

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.

2017-12-01

Land surface net radiation is the essential energy source at the earth's surface. It determines the surface energy budget and its partitioning, drives the hydrological cycle by providing available energy, and offers heat, light, and energy for biological processes. Individual components in net radiation have changed historically due to natural and anthropogenic climate change and land use change. Decadal variations in radiation such as global dimming or brightening have important implications for hydrological and carbon cycles. In order to assess the trends and variability of net radiation and evapotranspiration, there is a need for accurate estimates of long-term terrestrial surface radiation. While large progress in measuring top of atmosphere energy budget has been made, huge discrepancies exist among ground observations, satellite retrievals, and reanalysis fields of surface radiation, due to the lack of observational networks, the difficulty in measuring from space, and the uncertainty in algorithm parameters. To overcome the weakness of single source datasets, we propose a multi-source merging approach to fully utilize and combine multiple datasets of radiation components separately, as they are complementary in space and time. First, we conduct diagnostic analysis of multiple satellite and reanalysis datasets based on in-situ measurements such as Global Energy Balance Archive (GEBA), existing validation studies, and other information such as network density and consistency with other meteorological variables. Then, we calculate the optimal weighted average of multiple datasets by minimizing the variance of error between in-situ measurements and other observations. Finally, we quantify the uncertainties in the estimates of surface net radiation and employ physical constraints based on the surface energy balance to reduce these uncertainties. The final dataset is evaluated in terms of the long-term variability and its attribution to changes in individual components. The goal of this study is to provide a merged observational benchmark for large-scale diagnostic analyses, remote sensing and land surface modeling.

Investigating the Control of Ocean-Atmospheric Oscillations on Global Terrestrial Evaporation

NASA Astrophysics Data System (ADS)

Martens, B.; Waegeman, W.; Dorigo, W.; Verhoest, N.; Miralles, D. G.

2017-12-01

Intra-annual and multi-decadal variability in Earth's climate is strongly driven by periodic oscillations in the coupled state of our atmosphere and ocean. These oscillations do not only impact climate in nearby regions, but can also have an effect on the climate in remote areas, a phenomenon that is often referred to as teleconnection. Because changes in local climate immediately affect terrestrial ecosystems through a series of complex processes, ocean-atmospheric oscillations are expected to influence land evaporation; i.e. the return flux of water from land into the atmosphere. In this presentation, the effects of ocean-atmospheric oscillations on global terrestrial evaporation are analysed. We use multi-decadal, satellite-based observations of different climate variables (air temperature, radiation, precipitation) in combination with a simple supervised learning method - the Least Absolute Shrinkage and Selection Operator - to detect the impact of sixteen leading ocean-atmospheric oscillations on terrestrial evaporation. The latter is retrieved using the Global Land Evaporation Amsterdam Model (GLEAM). The analysis reveals hotspot regions in which more than 30% of the inter-annual variability in terrestrial evaporation can be explained by ocean-atmospheric oscillations. The impact is different per region and season, and can typically be attributed to a small subset of oscillations. For instance, the dynamics in terrestrial evaporation over eastern Australia are substantially impacted by both the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) during Austral spring. Using the same learning method, but targeting terrestrial evaporation based on its local climatic drivers (air temperature, precipitation, and radiation), shows the dominant control of precipitation on terrestrial evaporation in Australia, suggesting that both ENSO and IOD affect the precipitation, in his turn influencing evaporation. The latter is confirmed by regressing precipitation to the ocean-atmospheric oscillations. The results of our study allow for a better understanding of the link between ocean-atmosphere dynamics and terrestrial bio-geochemical cycles, and may help improve the prediction of future changes in the water cycle over the continents.

Giovanni: The Bridge between Data and Science

NASA Technical Reports Server (NTRS)

Shen, Suhung; Lynnes, Christopher; Kempler, Steven J.

2012-01-01

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a web-based remote sensing and model data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional data sets, covering atmospheric dynamics, atmospheric chemistry, hydrology, oceanographic, and land surface. Data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. Visualization options enable comparisons of multiple variables and easier refinement. Recently, new features have been developed, such as interactive scatter plots and maps. The performance is also being improved, in some cases by an order of magnitude for certain analysis functions with optimized software. We are working toward merging current Giovanni portals into a single omnibus portal with all variables in one (virtual) location to help users find a variable easily and enhance the intercomparison capability

Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

2016-08-01

In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

Influence of anthropogenic aerosol on cloud optical depth and albedo shown by satellite measurements and chemical transport modeling.

PubMed

Schwartz, Stephen E; Harshvardhan; Benkovitz, Carmen M

2002-02-19

The Twomey effect of enhanced cloud droplet concentration, optical depth, and albedo caused by anthropogenic aerosols is thought to contribute substantially to radiative forcing of climate change over the industrial period. However, present model-based estimates of this indirect forcing are highly uncertain. Satellite-based measurements would provide global or near-global coverage of this effect, but previous efforts to identify and quantify enhancement of cloud albedo caused by anthropogenic aerosols in satellite observations have been limited, largely because of strong dependence of albedo on cloud liquid water path (LWP), which is inherently highly variable. Here we examine satellite-derived cloud radiative properties over two 1-week episodes for which a chemical transport and transformation model indicates substantial influx of sulfate aerosol from industrial regions of Europe or North America to remote areas of the North Atlantic. Despite absence of discernible dependence of optical depth or albedo on modeled sulfate loading, examination of the dependence of these quantities on LWP readily permits detection and quantification of increases correlated with sulfate loading, which are otherwise masked by variability of LWP, demonstrating brightening of clouds because of the Twomey effect on a synoptic scale. Median cloud-top spherical albedo was enhanced over these episodes, relative to the unperturbed base case for the same LWP distribution, by 0.02 to 0.15.

Analysis of watershed topography effects on summer precipitation variability in the southwestern United States

NASA Astrophysics Data System (ADS)

Sohoulande Djebou, Dagbegnon C.; Singh, Vijay P.; Frauenfeld, Oliver W.

2014-04-01

With climate change, precipitation variability is projected to increase. The present study investigates the potential interactions between watershed characteristics and precipitation variability. The watershed is considered as a functional unit that may impact seasonal precipitation. The study uses historical precipitation data from 370 meteorological stations over the last five decades, and digital elevation data from regional watersheds in the southwestern United States. This domain is part of the North American Monsoon region, and the summer period (June-July-August, JJA) was considered. Based on an initial analysis for 1895-2011, the JJA precipitation accounts, on average, for 22-43% of the total annual precipitation, with higher percentages in the arid part of the region. The unique contribution of this research is that entropy theory is used to address precipitation variability in time and space. An entropy-based disorder index was computed for each station's precipitation record. The JJA total precipitation and number of precipitation events were considered in the analysis. The precipitation variability potentially induced by watershed topography was investigated using spatial regionalization combining principal component and cluster analysis. It was found that the disorder in precipitation total and number of events tended to be higher in arid regions. The spatial pattern showed that the entropy-based variability in precipitation amount and number of events gradually increased from east to west in the southwestern United States. Regarding the watershed topography influence on summer precipitation patterns, hilly relief has a stabilizing effect on seasonal precipitation variability in time and space. The results show the necessity to include watershed topography in global and regional climate model parameterizations.

Scale-dependency of the global mean surface temperature trend and its implication for the recent hiatus of global warming.

PubMed

Lin, Yong; Franzke, Christian L E

2015-08-11

Studies of the global mean surface temperature trend are typically conducted at a single (usually annual or decadal) time scale. The used scale does not necessarily correspond to the intrinsic scales of the natural temperature variability. This scale mismatch complicates the separation of externally forced temperature trends from natural temperature fluctuations. The hiatus of global warming since 1999 has been claimed to show that human activities play only a minor role in global warming. Most likely this claim is wrong due to the inadequate consideration of the scale-dependency in the global surface temperature (GST) evolution. Here we show that the variability and trend of the global mean surface temperature anomalies (GSTA) from January 1850 to December 2013, which incorporate both land and sea surface data, is scale-dependent and that the recent hiatus of global warming is mainly related to natural long-term oscillations. These results provide a possible explanation of the recent hiatus of global warming and suggest that the hiatus is only temporary.

Modeling of Urban Heat Island at Global Scale

NASA Astrophysics Data System (ADS)

KC, B.; Ruth, M.

2015-12-01

Urban Heat Island (UHI) is the temperature difference between urban and its rural background temperature. At the local level, the choice of building materials and urban geometry are vital in determining the UHI magnitude of a city. At the city scale, economic growth, population, climate, and land use dynamics are the main drivers behind changes in UHIs. The main objective of this paper is to provide a comprehensive assessment of UHI based on these "macro variables" at regional and global scale. We based our analysis on published research for Europe, North America, and Asia, reporting data for 83 cities across the globe with unique climatic, economic, and environmental conditions. Exploratory data analysis including Pearson correlation was performed to explore the relationship between UHI and PM2.5 (particulate matter with aerodynamic diameter ≤5 microns), PM10 (particulate matter with aerodynamic diameter ≤10 microns), vegetation per capita, built area, Gross Domestic Product (GDP), population density and population. Additionally, dummy variables were used to capture potential influences of climate types (based on Koppen classifications) and the ways by which UHI was measured. We developed three linear regression models, one for each of the three continents (Asia, Europe, and North America) and one model for all the cities across these continents. This study provides a unique perspective for predicting UHI magnitudes at large scales based on economic activity and pollution levels of a city, which has important implications in urban planning.

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.

Short-term nonmigrating tide variability in the mesosphere, thermosphere, and ionosphere

NASA Astrophysics Data System (ADS)

Pedatella, N. M.; Oberheide, J.; Sutton, E. K.; Liu, H.-L.; Anderson, J. L.; Raeder, K.

2016-04-01

The intraseasonal variability of the eastward propagating nonmigrating diurnal tide with zonal wave number 3 (DE3) during 2007 in the mesosphere, ionosphere, and thermosphere is investigated using a whole atmosphere model reanalysis and satellite observations. The atmospheric reanalysis is based on implementation of data assimilation in the Whole Atmosphere Community Climate Model (WACCM) using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. The tidal variability in the WACCM+DART reanalysis is compared to the observed variability in the mesosphere and lower thermosphere (MLT) based on the Thermosphere Ionosphere Mesosphere Energetics Dynamics satellite Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER) observations, in the ionosphere based on Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) observations, and in the upper thermosphere (˜475 km) based on Gravity Recovery and Climate Experiment (GRACE) neutral density observations. To obtain the short-term DE3 variability in the MLT and upper thermosphere, we apply the method of tidal deconvolution to the TIMED/SABER observations and consider the difference in the ascending and descending longitudinal wave number 4 structure in the GRACE observations. The results reveal that tidal amplitude changes of 5-10 K regularly occur on short timescales (˜10-20 days) in the MLT. Similar variability occurs in the WACCM+DART reanalysis and TIMED/SABER observations, demonstrating that the short-term variability can be captured in whole atmosphere models that employ data assimilation and in observations by the technique of tidal deconvolution. The impact of the short-term DE3 variability in the MLT on the ionosphere and thermosphere is also clearly evident in the COSMIC and GRACE observations. Analysis of the troposphere forcing in WACCM+DART and simulations of the Global Scale Wave Model (GSWM) show that the short-term DE3 variability in the MLT is not related to a single source; rather, it is due to a combination of changes in troposphere forcing, zonal mean atmosphere, and wave-wave interactions.