scholarly journals Comparison of the data‐driven top‐down and bottom‐up global terrestrial CO 2 exchanges: GOSAT CO 2 inversion and empirical eddy flux upscaling

2015 ◽  
Vol 120 (7) ◽  
pp. 1226-1245 ◽  
Author(s):  
Masayuki Kondo ◽  
Kazuhito Ichii ◽  
Hiroshi Takagi ◽  
Motoki Sasakawa
Keyword(s):  
Data Driven ◽  
Eddy Flux ◽  
Top Down ◽  
Bottom Up

scholarly journals Variability and quasi-decadal changes in the methane budget over the period 2000–2012

2017 ◽  
Author(s):  
Marielle Saunois ◽  
Philippe Bousquet ◽  
Benjamin Poulter ◽  
Anna Peregon ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Methane Emissions ◽  
Data Driven ◽  
Atmospheric Methane ◽  
Top Down ◽  
Bottom Up ◽  
Ch4 Emissions ◽  
Ensemble Mean ◽  
The Mean ◽  
The Tropics

Abstract. Following the recent Global Carbon project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling frameworks) and bottom-up models, inventories, and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seems to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the EDGARv4.2 inventory, which should be revised to smaller values in a near future. Though the sectorial partitioning of six individual top-down studies out of eight are not consistent with the observed change in atmospheric 13CH4, the partitioning derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that, the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. Besides, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. The methane loss (in particular through OH oxidation) has not been investigated in detail in this study, although it may play a significant role in the recent atmospheric methane changes.

Are You Being Served?

2013 ◽  
pp. 294-312
Author(s):  
Jeremy Millard
Keyword(s):  
Public Services ◽  
Smart Phones ◽  
Data Driven ◽  
Top Down ◽  
Local Data ◽  
Bottom Up ◽  
Current Trends ◽  
Huge Data ◽  
Service Environments ◽  
The One

In terms of public services, governments do not yet know how to treat users as different and unique individuals. At worst, users are still considered an undifferentiated mass, or at best as segments. However, the benefits of universal personalisation in public services are within reach technologically through e-government developments. Universal personalisation will involve achieving a balance between top-down government- and data-driven services, on the one hand, and bottom-up self-directed and user-driven services on the other. There are at least three main technological, organisational and societal drivers. First, top-down data-driven, often automatic, services based on the huge data resources available in the cloud and the technologies enabling the systematic exploitation of these by governments. Second, increasing opportunities for users themselves or their intermediaries to select or create their own service environments, bottom-up, through ‘user-driven’ services, drawing directly on the data cloud. Third, a move to ‘everyday’, location-driven e-government based largely on mobile smart phones using GPS and local data clouds, where public services are offered depending on where people are as well as who they are and what they are doing. This paper examines practitioners and researchers and describes model current trends based on secondary research and literature review.

scholarly journals A systematic approach to the implementation of attribute grammars with conventional compiler construction tools

2012 ◽  
Vol 9 (3) ◽  
pp. 983-1017
Author(s):  
Daniel Rodríguez-Cerezo ◽  
Antonio Sarasa-Cabezuelo ◽  
José-Luis Sierra
Keyword(s):  
Systematic Approach ◽  
Point Of View ◽  
Data Driven ◽  
Attribute Grammars ◽  
Top Down ◽  
Attribute Grammar ◽  
Bottom Up ◽  
Compiler Construction ◽  
Evaluation Strategies ◽  
Parser Generation

This article describes structure-preserving coding patterns to code arbitrary non-circular attribute grammars as syntax-directed translation schemes for bottom-up and top-down parser generation tools. In these translation schemes, semantic actions are written in terms of a small repertory of primitive attribution operations. By providing alternative implementations for these attribution operations, it is possible to plug in different semantic evaluation strategies in a seamlessly way (e.g., a demand-driven strategy, or a data-driven one). The pattern makes possible the direct implementation of attribute grammar-based specifications with widely-used translation schemedriven tools for the development of both bottom-up (e.g. YACC, BISON, CUP) and top-down (e.g., JavaCC, ANTLR) language translators. As a consequence, initial translation schemes can be successively refined to yield final efficient implementations. Since these implementations still preserve the ability to be extended with new features described at the attribute grammar level, the advantages from the point of view of development and maintenance become apparent.

scholarly journals Indicators on the Impacts of Climate Change on Biodiversity in Germany—Data Driven or Meeting Political Needs?

2018 ◽  
Vol 10 (11) ◽  
pp. 3959
Author(s):  
Rainer Schliep ◽  
Ulrich Walz ◽  
Ulrich Sukopp ◽  
Stefan Heiland
Keyword(s):  
Climate Change ◽  
Indicator System ◽  
Policy Advice ◽  
Data Driven ◽  
Top Down ◽  
Bottom Up ◽  
Ongoing Development ◽  
Scientific Accuracy ◽  
Indicator Systems ◽  
Impacts Of Climate Change

When developing new indicators for policy advice, two different approaches exist and may be combined with each other. First, a data-driven, bottom-up approach determines indicators primarily by the availability of suitable data. Second, indicators can be developed by a top-down approach, on the basis of political fields of action and related normative goals. While the bottom-up approach might not meet the needs of an up-to-date policy advice, the top-down approach might lack the necessary data. To discuss these problems and possible solutions, we refer to the ongoing development of an indicator system on impacts of climate change on biodiversity in Germany, where a combination of both approaches has been successfully applied. We describe suitable indicators of this system and discuss the reasons for the remaining gaps. Both approaches, mentioned above, have advantages, constraints, and shortcomings. The scientific accuracy of the indicators, the availability of data and the purpose of policy advice have to be well-balanced while developing such indicator systems.

scholarly journals Global satellite-driven estimates of heterotrophic respiration

2019 ◽  
Vol 16 (11) ◽  
pp. 2269-2284 ◽  
Author(s):  
Alexandra G. Konings ◽  
A. Anthony Bloom ◽  
Junjie Liu ◽  
Nicholas C. Parazoo ◽  
David S. Schimel ◽  
...  
Keyword(s):  
Ecosystem Respiration ◽  
Carbon Fluxes ◽  
Heterotrophic Respiration ◽  
Data Driven ◽  
Terrestrial Carbon ◽  
Top Down ◽  
Annual Average ◽  
Bottom Up ◽  
Total Ecosystem Respiration ◽  
Use Efficiency

Abstract. While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4∘×5∘ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6±19.3 Pg C yr−1. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1). The uncertainty ranges of both methods are comparable, except poleward of 45∘ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

scholarly journals A decade of Semantic Web research through the lenses of a mixed methods approach

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 979-1005
Author(s):  
Sabrina Kirrane ◽  
Marta Sabou ◽  
Javier D. Fernández ◽  
Francesco Osborne ◽  
Cécile Robin ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Semantic Web ◽  
Expert Knowledge ◽  
Data Driven ◽  
Trend Detection ◽  
Future Research ◽  
Top Down ◽  
Bottom Up ◽  
Research Fields ◽  
Web Research

The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily performed through qualitative, top-down style approaches, that rely on expert knowledge. More recently, data-driven, bottom-up approaches have been proposed that offer a quantitative analysis of the evolution of a research domain. In this paper, we aim to provide a broader and more complete picture of Semantic Web topics and trends by adopting a mixed methods methodology, which allows for the combined use of both qualitative and quantitative approaches. Concretely, we build on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches (Rexplore, Saffron, PoolParty), on a corpus of Semantic Web papers published between 2006 and 2015. In this process, we both use the latter for “fact-checking” on the former and also to derive key findings in relation to the strengths and weaknesses of top-down and bottom-up approaches to research topic identification. Although we provide a detailed study on the past decade of Semantic Web research, the findings and the methodology are relevant not only for our community but beyond the area of the Semantic Web to other research fields as well.

Are You Being Served?

2011 ◽  
Vol 7 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Jeremy Millard
Keyword(s):  
Public Services ◽  
Data Driven ◽  
Top Down ◽  
Local Data ◽  
Bottom Up ◽  
Know How ◽  
Current Trends ◽  
Huge Data ◽  
Service Environments ◽  
The One

In terms of public services, governments do not yet know how to treat users as different and unique individuals. At worst, users are still considered an undifferentiated mass, or at best as segments. However, the benefits of universal personalisation in public services are within reach technologically through e-government developments. Universal personalisation will involve achieving a balance between top-down government- and data-driven services, on the one hand, and bottom-up self-directed and user-driven services on the other. There are at least three main technological, organisational and societal drivers. First, top-down data-driven, often automatic, services based on the huge data resources available in the cloud and the technologies enabling the systematic exploitation of these by governments. Second, increasing opportunities for users themselves or their intermediaries to select or create their own service environments, bottom-up, through ‘user-driven’ services, drawing directly on the data cloud. Third, a move to ‘everyday’, location-driven e-government based largely on mobile smart phones using GPS and local data clouds, where public services are offered depending on where people are as well as who they are and what they are doing. This paper examines practitioners and researchers and describes model current trends based on secondary research and literature review.

scholarly journals Global, Satellite-Driven Estimates of Heterotrophic Respiration

2018 ◽  
Author(s):  
Alexandra G. Konings ◽  
A. Anthony Bloom ◽  
Junjie Liu ◽  
Nicholas C. Parazoo ◽  
David S. Schimel ◽  
...  
Keyword(s):  
Ecosystem Respiration ◽  
Carbon Fluxes ◽  
Heterotrophic Respiration ◽  
Data Driven ◽  
Terrestrial Carbon ◽  
Top Down ◽  
Annual Average ◽  
Bottom Up ◽  
Uncertainty Estimates ◽  
Total Ecosystem Respiration

Abstract. While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least observed carbon fluxes, particularly outside the mid-latitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4 x 5° spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System- Flux (CMS-Flux) with an optimally-scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially-varying carbon use efficiency retrieved in a model-data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6 ± 19.3 Pg C/yr. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, with new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes, and are 30 % greater overall (43.6 Pg C/yr vs. 33.4 Pg C/yr). The uncertainty ranges of both methods are comparable, except poleward of 45 degrees North, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

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