scholarly journals Atmospheric CO<sub>2</sub> inversions at the mesoscale using data driven prior uncertainties. Part 1: Methodology and system evaluation

2016 ◽  
Author(s):  
Panagiotis Kountouris ◽  
Christoph Gerbig ◽  
Christian Rödenbeck ◽  
Ute Karstens ◽  
Thomas F. Koch ◽  
...  
Keyword(s):  
Information Gain ◽  
Spatial Scales ◽  
Regional Scale ◽  
Mean Value ◽  
Residual Analysis ◽  
Synthetic Experiment ◽  
Model Mismatch ◽  
Temporal And Spatial ◽  
Using Data ◽  
Good Agreement

Abstract. Atmospheric inversions are widely used in the optimization of surface carbon fluxes at regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not reflect directly the true flux uncertainties but it is used in such a way to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model – data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, where the flux constraint is derived following the same residual analysis but applied to the model-model mismatch. The synthetic study showed a quite good agreement between posterior and “true” fluxes at European/Country and annual/monthly scales. Posterior monthly and country aggregated fluxes improved their correlation coefficient with the “known truth” by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. Respectively, the ratio of the standard deviation between posterior/reference and prior/reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales where the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

scholarly journals Technical Note: Atmospheric CO<sub>2</sub> inversions on the mesoscale using data-driven prior uncertainties: methodology and system evaluation

2018 ◽  
Vol 18 (4) ◽  
pp. 3027-3045 ◽  
Author(s):  
Panagiotis Kountouris ◽  
Christoph Gerbig ◽  
Christian Rödenbeck ◽  
Ute Karstens ◽  
Thomas Frank Koch ◽  
...  
Keyword(s):  
Information Gain ◽  
Spatial Scales ◽  
Regional Scale ◽  
Mean Value ◽  
Residual Analysis ◽  
Technical Note ◽  
Model Mismatch ◽  
Temporal And Spatial ◽  
Using Data ◽  
Good Agreement

Abstract. Atmospheric inversions are widely used in the optimization of surface carbon fluxes on a regional scale using information from atmospheric CO2 dry mole fractions. In many studies the prior flux uncertainty applied to the inversion schemes does not directly reflect the true flux uncertainties but is used to regularize the inverse problem. Here, we aim to implement an inversion scheme using the Jena inversion system and applying a prior flux error structure derived from a model–data residual analysis using high spatial and temporal resolution over a full year period in the European domain. We analyzed the performance of the inversion system with a synthetic experiment, in which the flux constraint is derived following the same residual analysis but applied to the model–model mismatch. The synthetic study showed a quite good agreement between posterior and true fluxes on European, country, annual and monthly scales. Posterior monthly and country-aggregated fluxes improved their correlation coefficient with the known truth by 7 % compared to the prior estimates when compared to the reference, with a mean correlation of 0.92. The ratio of the SD between the posterior and reference and between the prior and reference was also reduced by 33 % with a mean value of 1.15. We identified temporal and spatial scales on which the inversion system maximizes the derived information; monthly temporal scales at around 200 km spatial resolution seem to maximize the information gain.

scholarly journals Interpolation of LES Cloud Surfaces for Use in Direct Calculations of Entrainment and Detrainment

2011 ◽  
Vol 139 (2) ◽  
pp. 444-456 ◽  
Author(s):  
Jordan T. Dawe ◽  
Philip H. Austin
Keyword(s):  
Numerical Model ◽  
Spatial Scales ◽  
Grid Cell ◽  
Time Step ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Temporal And Spatial ◽  
Les Model ◽  
Direct Calculations ◽  
Good Agreement

Abstract Direct calculations of the entrainment and detrainment of air into and out of clouds require knowledge of the relative velocity difference between the air and the cloud surface. However, a discrete numerical model grid forces the distance moved by a cloud surface over a time step to be either zero or the width of a model grid cell. Here a method for the subgrid interpolation of a cloud surface on a discrete numerical model grid is presented. This method is used to calculate entrainment and detrainment rates for a large-eddy simulation (LES) model, which are compared with rates calculated via the direct flux method of Romps. The comparison shows good agreement between the two methods as long as the model clouds are well resolved by the model grid spacing. This limitation of this technique is offset by the ability to resolve fluxes on much finer temporal and spatial scales, making it suitable for calculating entrainment and detrainment profiles for individual clouds.

scholarly journals How precipitation intermittency sets an optimal sampling distance for temperature reconstructions from Antarctic ice cores

2020 ◽  
Author(s):  
Thomas Münch ◽  
Martin Werner ◽  
Thomas Laepple
Keyword(s):  
Climate Model ◽  
Model Simulation ◽  
Spatial Scales ◽  
Ice Core ◽  
Regional Scale ◽  
Ice Cores ◽  
Sampling Strategy ◽  
Temperature Reconstruction ◽  
Optimal Sampling ◽  
Using Data

Abstract. Many palaeoclimate proxies share one challenging property: they are not only driven by the climatic variable of interest, e.g., temperature, but they are also influenced by secondary effects which cause, among other things, increased variability, frequently termed noise. Noise in individual proxy records can be reduced by averaging the records, but the effectiveness of this approach depends on the correlation of the noise between the records and therefore on the spatial scales of the noise-generating processes. Here, we review and apply this concept in the context of Antarctic ice-core isotope records to determine which core locations are best suited to reconstruct local-to-regional-scale temperatures. Using data from a past-millennium climate model simulation equipped with stable isotope diagnostics we intriguingly find that even for a local temperature reconstruction the optimal sampling strategy is to combine a local ice core with a more distant core ~ 500–1000 km away. A similarly large distance between cores is also optimal for reconstructions that average more than two isotope records. We show that these findings result from the interplay of the two spatial scales of the correlation structures associated with the temperature field and with the noise generated by precipitation intermittency. Our study helps to maximise the usability of existing Antarctic ice cores and to optimally plan future drilling campaigns. It also broadens our knowledge on the processes that shape the isotopic record and their typical correlation scales. Finally, the presented method can be directly extended to determine optimal sampling strategies for other palaeoclimate reconstruction problems.

scholarly journals Maize yield and rainfall on different spatial and temporal scales in Southern Brazil

2007 ◽  
Vol 42 (5) ◽  
pp. 603-613 ◽  
Author(s):  
Homero Bergamaschi ◽  
Timothy Robert Wheeler ◽  
Andrew Juan Challinor ◽  
Flávia Comiran ◽  
Bruna Maria Machado Heckler
Keyword(s):  
Spatial Scales ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Grain Filling ◽  
Maize Yield ◽  
Reproductive Period ◽  
Spatial And Temporal Scales ◽  
Crop Monitoring ◽  
Crop Cycle ◽  
Temporal And Spatial

This study aimed to establish relationships between maize yield and rainfall on different temporal and spatial scales, in order to provide a basis for crop monitoring and modelling. A 16-year series of maize yield and daily rainfall from 11 municipalities and micro-regions of Rio Grande do Sul State was used. Correlation and regression analyses were used to determine associations between crop yield and rainfall for the entire crop cycle, from tasseling to 30 days after, and from 5 days before tasseling to 40 days after. Close relationships between maize yield and rainfall were found, particularly during the reproductive period (45-day period comprising the flowering and grain filling). Relationships were closer on a regional scale than at smaller scales. Implications of the crop-rainfall relationships for crop modelling are discussed.

scholarly journals A novel framework for modeling the evolution of cross-scale ecological assembly

2020 ◽  
Author(s):  
Amanda S. Gallinat ◽  
William D. Pearse
Keyword(s):  
Community Assembly ◽  
Evolutionary Dynamics ◽  
Environmental Gradients ◽  
Spatial Scales ◽  
Regional Scale ◽  
Species Traits ◽  
Temporal And Spatial ◽  
Environmental Responses ◽  
Conceptual Overview ◽  
Species Assembly

AbstractCommunity assembly can be driven by species’ responses to environmental gradients, and interactions within (e.g., competition) and across (e.g., herbivory) clades. These ecological dynamics are mediated by species’ traits, which are in turn shaped by past evolution. As such, identifying the drivers of species assembly is made difficult by the differing temporal and spatial scales of ecological and evolutionary dynamics. Two recent advances have emerged to address the cross-scale challenge of modeling species assembly: phylogenetic generalized linear mixed modeling (PGLMM) and earth observation networks (EONs). PGLMM integrates through time by modeling the evolution of trait-based community assembly, while EONs synthesize across space by placing standardized site-level species occurrence data within their regional context. Here we describe a framework for combining these tools to investigate the drivers of species assembly, and so address three outstanding questions: (1) Does evolution adapt or constrain regional-scale environmental responses? (2) Do evolved responses to past competition minimize or enhance present-day competition? (3) Are species’ cross-clade associations evolutionarily constrained? We provide a conceptual overview of how PGLMM and EONs can be synthesized to answer these questions, and provide exemplar Bayesian PGLMM code. Finally, we describe the capacity of these tools to aid in conservation and natural resource management, including predicting future colonization by rare and invasive species, vulnerable mutualisms, and pest and pathogen outbreaks.

Simulation of Unsteady Flow Around a Cylinder

2015 ◽  
Vol 3 (2) ◽  
pp. 28-49
Author(s):  
Ridha Alwan Ahmed
Keyword(s):  
Stokes Equations ◽  
Lift Coefficient ◽  
Reynolds Numbers ◽  
Mean Value ◽  
Navier Stokes ◽  
Cylinder Surface ◽  
Navier Stokes Equations ◽  
Flow Around A Cylinder ◽  
Numerical Grid ◽  
Good Agreement

       In this paper, the phenomena of vortex shedding from the circular cylinder surface has been studied at several Reynolds Numbers (40≤Re≤ 300).The 2D, unsteady, incompressible, Laminar flow, continuity and Navier Stokes equations have been solved numerically by using CFD Package FLUENT. In this package PISO algorithm is used in the pressure-velocity coupling.        The numerical grid is generated by using Gambit program. The velocity and pressure fields are obtained upstream and downstream of the cylinder at each time and it is also calculated the mean value of drag coefficient and value of lift coefficient .The results showed that the flow is strongly unsteady and unsymmetrical at Re>60. The results have been compared with the available experiments and a good agreement has been found between them

Larval Feeding: Mechanisms, Rates, and Performance in Nature

2018 ◽  
Keyword(s):  
Marine Invertebrates ◽  
Spatial Scales ◽  
Size Spectrum ◽  
Larval Feeding ◽  
Natural Habitats ◽  
Feeding Mechanism ◽  
Temporal And Spatial ◽  
And Performance ◽  
Bioenergetics Models ◽  
Many Particles

Larvae of many marine invertebrates must capture and ingest particulate food in order to develop to metamorphosis. These larvae use only a few physical processes to capture particles, but implement these processes using diverse morphologies and behaviors. Detailed understanding of larval feeding mechanism permits investigators to make predictions about feeding performance, including the size spectrum of particles larvae can capture and the rates at which they can capture them. In nature, larvae are immersed in complex mixtures of edible particles of varying size, density, flavor, and nutritional quality, as well as many particles that are too large to ingest. Concentrations of all of these components vary on fine temporal and spatial scales. Mechanistic models linking larval feeding mechanism to performance can be combined with data on food availability in nature and integrated into broader bioenergetics models to yield increased understanding of the biology of larvae in complex natural habitats.

Humans and the Environment

2013 ◽  
Keyword(s):  
Spatial Scales ◽  
Empirical Studies ◽  
Human Action ◽  
Cultural Practice ◽  
Environmental Archaeology ◽  
Archaeological Data ◽  
Temporal And Spatial ◽  
Global And Local ◽  
The Relationship ◽  
The Way

The environment has always been a central concept for archaeologists and, although it has been conceived in many ways, its role in archaeological explanation has fluctuated from a mere backdrop to human action, to a primary factor in the understanding of society and social change. Archaeology also has a unique position as its base of interest places it temporally between geological and ethnographic timescales, spatially between global and local dimensions, and epistemologically between empirical studies of environmental change and more heuristic studies of cultural practice. Drawing on data from across the globe at a variety of temporal and spatial scales, this volume resituates the way in which archaeologists use and apply the concept of the environment. Each chapter critically explores the potential for archaeological data and practice to contribute to modern environmental issues, including problems of climate change and environmental degradation. Overall the volume covers four basic themes: archaeological approaches to the way in which both scientists and locals conceive of the relationship between humans and their environment, applied environmental archaeology, the archaeology of disaster, and new interdisciplinary directions.The volume will be of interest to students and established archaeologists, as well as practitioners from a range of applied disciplines.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

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