1,721,003 research outputs found
Analyzing the effects of unforced natural variability and anthropogenic forcing on ENSO variability using CESM
Get PDFThe El Niño-Southern Oscillation (ENSO) is an important contributor to Earth’s inter-annual climate variability, with worldwide weather effects (Whetton and Rutherfurd, 1994; Hoerling and Zhong, 1997; Dai and Wigley, 2000). Understanding how ENSO may change with climate is a major challenge, given the internal variability of the system and relatively short observational record (Wittenberg, 2009). Much recent research has used multi-model ensembles to address the effects of climate change on ENSO (Stevenson, 2012; Cai et al., 2014; Kim et al., 2014). Here we analyze ENSO in a Community Earth System Model (CESM) ensemble that samples internal variability of the coupled Earth system. We present results from a 50-member climate change ensemble experiment, using historical climate forcings (1850-2005) and projections to 2100 following the representative concentration pathway 8.5 (RCP8.5). With this ensemble, and a ~5000 year control run with constant pre-industrial conditions, we examine ENSO variability under different forcing regimes. We compare the effects of anthropogenic climate change with the effects of natural modulations on ENSO sea surface temperature (SST). We find that any changes in ENSO SST due to climate change are secondary to natural modulations.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-12-01The student, Benjamin Vega-Westhoff, accepted the attached license on 2016-12-02 at 15:57.The student, Benjamin Vega-Westhoff, submitted this Thesis for approval on 2016-12-02 at 16:08.This Thesis was approved for publication on 2016-12-05 at 14:54.DSpace SAF Submission Ingestion Package generated from Vireo submission #10423 on 2017-02-28 at 14:37:14Made available in DSpace on 2017-03-01T16:37:05Z (GMT). No. of bitstreams: 3
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Previous issue date: 2016-12-05Embargo set by: Seth Robbins for item 98623
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Modeled sensitivity of the northwestern Pacific upper-ocean responses to tropical cyclones in a fully coupled climate model with varying ocean grid resolution
Get PDFTropical cyclones (TCs) actively contribute to Earth's climate, but TC-climate interactions are largely unexplored in fully-coupled models. Here we analyze the upper-ocean response to TCs using a high resolution Earth system model, in which a 0.5° atmosphere is coupled to an ocean with two different horizontal resolutions: 1° and 0.1°. The model produces realistic TCs up to category 3 in both versions of the model, and, in the northwestern Pacific region, the transient surface ocean temperature response is consistent with observations. We estimate the model’s flux-adjusted TC-induced ocean heat convergence in the northwestern Pacific is ~0.26 PW and ~0.30 PW for the low and high resolution configurations, respectively, which is within the range of previous observation-based estimates. Results point to the importance of coupled modeling approaches that account for ocean-atmosphere feedbacks, in order to develop a complete understanding of the relationship between TCs and climate.Item withdrawn by Laura Spradlin ([email protected]) on 2014-10-28T13:49:32Z
Item was in collections:
University of Illinois Theses & Dissertations (ID: 1)
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Analyzing el nino southern oscillation predictions from long-short-merm-memory models
Get PDFEl Nino Southern Oscillation (ENSO) can have global impacts across the world. Because of its prevalence, scientists run models to forecast its next move. Here, long-short-term-memory models (LSTM) were compared to linear regression models (LR) as first steps to explore the potential benefits of simple deep neural networks for predicting ENSO. Each model’s prediction capabilities were tested with sea surface temperatures (SST), warm water volumes, and zonal winds as predictors, individually and in combinations, utilizing both monthly and daily resolution data, across a total of 11 leads. By utilizing these three variables, we examine different forms of climate variability within the coupled system (SST), the subsurface ocean (warm water volume), and the atmosphere (zonal winds), and we quantify the relative importance of each of these processes for ENSO predictability through two statistical modeling approaches: LSTM and LR. Results show that when using monthly data as predictors, predictions from LSTM were similar to predictions from LR. However, with daily data, LSTM exhibited some advantage over LR in terms of the correlation coefficient, especially with daily resolution SST as a predictor and at longer leads. This can be appealing because once the computationally expensive training of LSTM is complete, the predictions employing the trained model can be relatively cheap to perform thereafter.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo termsThe student, Andrew Huang, accepted the attached license on 2018-04-18 at 10:06.The student, Andrew Huang, submitted this Thesis for approval on 2018-04-18 at 10:21.This Thesis was approved for publication on 2018-04-18 at 10:56.DSpace SAF Submission Ingestion Package generated from Vireo submission #12337 on 2018-08-31 at 17:13:30Made available in DSpace on 2018-09-04T20:27:16Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-04-1
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Data-driven spatial modeling of historic and future land change at global scale
Get PDFAssessing the historic and future impacts of land-use and land-cover change (LULCC) on climate requires spatially and temporally explicit data sets on LULCC spanning several decades to centuries, because climate change is a long-term problem. Though remote sensing data provides a globally consistent picture of land cover, these data are only available from the past four decades. Therefore, existing LULCC reconstructions are modeled estimates that combine remote sensing data with relatively coarser-resolution inventory statistics that covers longer historical period. The uncertainties in modeling assumptions, and limited availability and inconsistencies across inventory datasets among other reasons introduce uncertainties in LULCC reconstructions. These uncertainties not only limit our ability to model future LULCC, but also translate as uncertainties in both historic and future environmental assessments.
The objectives of my PhD work are as follows: (1) systematically investigate the causes of uncertainties in existing historical LULCC datasets, (2) test the sensitivity of LULCC quantification uncertainty in estimating CO2 emissions from LULCC (historic and future) using a process-based land-surface model, the Integrated Science Assessment Model (ISAM), (3) compare the relative uncertainties from various drivers (e.g. LULCC datasets, model processes e.g. nitrogen cycle, environmental factors such as climate) in estimating historic and future LULCC emissions, and (4) explore statistical techniques to model future LULCC that takes into account the uncertainties in quantifying the spatial and temporal patterns of LULCC, and (5) as a case-study, identify a key regional hotspot of historic LULCC quantification uncertainty (here, India), and reduce uncertainty through improved understanding of the dynamics and drivers of land change in the case-study region. I address the above goals by integrating land-surface modeling (ISAM), remote sensing and GIS, data collected through ground transects, and geospatial data on socioeconomics.
ISAM simulations show that the estimated net global emissions from LULCC (mean and range) across three different historical LULCC reconstructions are 1.88 (1.7 to 2.21) GtC/yr for the 1980’s, 1.66 (1.48 to 1.83) GtC/yr for the 1990's, and 1.44 (1.22 to 1.65) for the 2000's. The estimates are higher than other published estimates that range from 0.80 to 1.5 GtC/yr for the 1990’s and 1.1 GtC/yr for the 2000’s. These results are higher than other published estimates because they include the effects of nitrogen limitation on regrowth of forests following wood harvest and agricultural abandonment. The estimated LULUC emissions for the tropics are 0.79±0.25 for the 1980’s, 0.78±0.29 for the 1990’s and 0.71±0.33 GtC/yr for the 2000’s, and for the non-tropics regions are 1.08±0.52, 0.90±0.19 and 0.69±0.12 GtC/yr for the three decades. The model results indicate that failing to account for the nitrogen cycle underestimates LULCC emissions by about 40% globally (0.66 GtC/yr), 10% in the tropics (0.07 GtC/yr) and 70% in the non-tropics (0.59 GtC/yr). If LULCC emissions are higher than assessed, it means fossil fuel emissions would have to be even lower to meet the same mitigation target.
Extending ISAM simulations to the 21st century resulted in two key insights. First, nitrogen limitation of CO2 uptake is substantial and sensitive to nitrogen inputs. In ISAM, excluding nitrogen limitation underestimated global total LULUC emissions by 34-52 PgC (~21-29%) during the 20th century and by 128-187 PgC (90-150%) during the 21st century. The difference increases with time because nitrogen limitation will progressively down-regulate the magnitude of CO2 fertilization effect on regrowing forests, due to decreasing supply of plant-usable mineral nitrogen. Second, historically, the indirect effects of anthropogenic activity through environmental changes in land experiencing LULCC (indirect emissions) are small compared to direct effects of anthropogenic LULCC activity (direct emissions). As a result, including or excluding indirect emissions had a minor influence on the estimated total LULUC emissions historically. In contrast, the indirect LULCC emissions for the 21st century are a much larger source to the atmosphere, in simulations with nitrogen limitation. This is because of the gradual weakening of the photosynthetic response to elevated (CO2) caused by nitrogen limitation. Therefore, what fluxes are including in LULCC emissions across different models is a crucial source of uncertainty in future LULCC emissions estimates.
A detailed investigation of the sensitivity of different global-scale LULCC modeling techniques show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factor), or those based on mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, I show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. In contrast to previous approaches, I present a statistical land use downscaling model and show that the model can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. Therefore, the modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Prasanth Meiyappan, accepted the attached license on 2016-04-13 at 20:33.The student, Prasanth Meiyappan, submitted this Dissertation for approval on 2016-04-13 at 20:39.This Dissertation was approved for publication on 2016-04-18 at 14:23.DSpace SAF Submission Ingestion Package generated from Vireo submission #9216 on 2016-07-07 at 14:16:41Made available in DSpace on 2016-07-07T21:14:41Z (GMT). No. of bitstreams: 3
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Previous issue date: 2016-04-18Embargo set by: Seth Robbins for item 93250
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 93250
Lift date: 2018-07-07T21:18:16Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 93250 on 2018-07-08T09:15:33Z
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The importance of ocean internal variability for coupled climate modeling in the community earth system model
Get PDFConsiderable efforts have been made in recent decades to diagnose how the climate of our planet is changing in response to anthropogenic greenhouse gas forcing. There are considerable risks associated with a warming climate, with possible hazards to life, property, economy, and the environment. These changes and the risks associated with them are inherently uncertain, and scientists use tools such as global coupled climate model ensembles to attempt to quantify these uncertainties. Quantifying different types of uncertainties involved in modeling the Earth’s climate system is of high importance as processes within the climate system are chaotic and challenging to predict.
This dissertation contains a comprehensive quantification of climate uncertainty, focusing primarily on the uncertainty due to coupled atmosphere-ocean internal variability, utilizing a global coupled climate model ensemble (the Community Earth System Model; CESM). Here, this work poses key science questions related to quantifying internal variability in three different model variables, all of which are important in the context of a changing climate.
Firstly, uncertainties surrounding decadal trends in depth-integrated, drift-removed global steric sea-level rise are evaluated. Results show that the effects of both internal variability and structural model differences contribute substantially to uncertainties in modeled steric sea-level trends for recent decades and the magnitude of these effects vary with depth. Uncertainties are amplified for regional assessments, highlighting the importance of both sources of variability when considering uncertainties surrounding modeled sea-level trends. Results can provide useful constraints on estimations of global and regional sea-level variability, in particular for areas with few observations such as the deep ocean and the Southern Hemisphere.
Secondly, a statistical framework using a block-maxima approach is used to analyze the representation of warm temperature extremes in global climate model ensembles. Uncertainties due to structural model differences, grid resolution and internal variability are characterized and discussed. Results show that models and ensembles differ greatly in the representation of extreme temperature over the United States, but that there is overwhelming evidence suggesting variability in tail events is dependent on time and anthropogenic warming. These sources of variability can considerably influence the uncertainty of modeled extremes. Several idealized regional applications are highlighted for evaluating ensemble skill, based on quantile analysis and root mean square errors in the overall sample and the upper tail. Results are relevant to regional climate assessments that use global model outputs and that are sensitive to extreme temperatures.
Lastly, this dissertation evaluates internal variability in ocean adjustment in the low-resolution CESM ensembles, by assessing ocean temperature. Uncertainty due to internal variability is used as a proxy to quantify the timescales on which different ocean depths and basins equilibrate in CESM. These results go beyond implications for CESM, reflecting timescales of internal variability in global coupled models. Results are discussed in the context of the global climate hiatus, of which internal variability is thought to be a predominant cause. Timescales for equilibration are longer in the deep ocean than the upper ocean, where ocean mixing enhances the speed of ensemble spread. The Atlantic equilibrates on shorter timescales relative to the Pacific, as North Atlantic Deep Water formation due to differential solar heating between high and low latitudes spurs the overturning circulation, whereas the Pacific has slower dynamics. Results have implications on the choice of climate model initialization method and imply that ensembles sampling the initial conditions of the atmosphere only may be appropriate for the evaluation of internal variability of the atmosphere and upper ocean, but not for the deep ocean. Additionally, the issue of accounting for deep ocean drift is of concern, when considering the production of large climate ensemble projects, such as the upcoming Coupled Model Intercomparison Project Phase 6 (CMIP6).Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01The student, Emily Hogan, accepted the attached license on 2018-04-13 at 12:38.The student, Emily Hogan, submitted this Dissertation for approval on 2018-04-13 at 12:45.This Dissertation was approved for publication on 2018-04-13 at 17:04.DSpace SAF Submission Ingestion Package generated from Vireo submission #12126 on 2018-08-31 at 17:25:58Made available in DSpace on 2018-09-04T20:46:55Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-04-13Embargo set by: Seth Robbins for item 107365
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 107365
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 107365 on 2020-09-05T09:15:26Z
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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