1,721,256 research outputs found
Improvements in the terrestrial carbon cycle in CMIP models evaluated with satellite observations
Full text linkWith impacts of climate change already noticeable in every region of the world, understanding and accurately simulating the drivers of climate change is crucial. In particular, the global carbon cycle and its responses to changing carbon dioxide (CO2) emissions plays an important role. This thesis aims to identify important improvements and key processes relevant to accurately simulate the carbon cycle under climate change and to provide recommendations for further model developments. This is achieved by a comprehensive evaluation of historical simulations from earth system models (ESMs) participating in the last two phases of the Coupled Model Intercomparison Project (CMIP) with satellite observations. In a first study of this thesis, column-average CO2 mole fraction (XCO2) from CMIP5 and CMIP6 emission-driven ESM simulations are compared to satellite observations. A previously found discrepancy between a negative trend of the seasonal cycle amplitude (SCA) with increasing XCO2 in the northern midlatitudes shown by the observations with models showing an insignificant trend could be attributed to spatial sampling. Furthermore, while ESMs overestimate mean and growth rate of XCO2 while underestimating the SCA, the CMIP6 ensemble performs better than CMIP5 ensemble. In a second study, the present-day land carbon cycle is evaluated. While some long-standing biases could be resolved in CMIP6, such as the photosynthesis overestimation which was resolved through the inclusion of the interactive nitrogen cycle, other biases remain. Despite the increased process complexity in emission-driven simulations that fully account for the influence of climate-carbon feedbacks on atmospheric CO2, they perform just as well as CO2 concentration-driven simulations. Therefore, both the use of emission-driven over concentration-driven simulations, as well as the inclusion of interactive nitrogen cycles are recommended as a default setting for future CMIP phases
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Constraining Uncertainties in Multi-Model Projections of the Future Climate with Observations
Full text linkEarth system models (ESMs) are common tools to project climate change. The main focus of this thesis is the analysis of climate projections from ESMs participating in the Coupled Model Intercomparison Project (CMIP) with the aim to reduce uncertainties in climate projections with observations. In a first step, climate sensitivity is evaluated in CMIP6 models. For the effective climate sensitivity (ECS), a multi-model range of 1.8-5.6 K is found. This range is higher than in any previous CMIP ensemble before. Possible reasons for this are changes in cloud parameterizations. To reduce uncertainties in the ECS of the CMIP6 models, 11 published emergent constraints on ECS are analyzed. Emergent constraints are approaches to reduce uncertainties in climate projections by combining observations and ESM output. The application of the emergent constraints to CMIP6 data shows a decrease in the skill of the emergent relationships. This is likely related to the increased multi-model spread of ECS in CMIP6, but may in some cases also be due to spurious statistical relationships. The results support previous studies concluding that emergent constraints should be based on independently verifiable physical mechanisms. To overcome these issues of emergent constraints, an alternative approach based on machine learning (ML) is introduced. As target variable, gross primary production (GPP) is studied. In a first step, an existing emergent constraint is used to constrain the global mean GPP at the end of the 21st century in Representative Concentration Pathway (RCP) 8.5 simulations with CMIP5 ESMs to (171 ± 12) GtC yr-1. In a second step, an ML model is used to constrain gridded future absolute GPP. For this, observational data is fed into the ML algorithm that has been trained on CMIP5 data to learn relationships between present-day physically relevant diagnostics and the target variable. In a perfect model setup, the ML model shows superior performance
Data-Driven Cloud Cover Parameterizations for the ICON Earth System Model Using Deep Learning and Symbolic Regression
Full text linkThis thesis delves into the improvement of cloud parameterizations in climate models through machine learning trained on coarse-grained output from high-resolution simulations. Utilizing the ICOsahedral Non-hydrostatic (ICON) modeling framework, it specifically targets the enhancement of cloud cover parameterization within the ICON Earth System Model. Three types of neural networks (NNs) differing in vertical locality are developed to estimate cloud cover, with globally trained NNs even applicable to distinct regional simulations. Interpretability analysis exposes model-specific biases and local relationships with the thermodynamic environment. Despite achieving high predictive performance, NNs necessitate post-hoc interpretation tools. To tackle this issue, a combined hierarchical modeling framework incorporating symbolic regression, feature selection, and physical constraints is proposed. The resulting equations, characterized by simplicity and physical consistency, attain performance comparable to NNs while demonstrating superior transferability to other realistic datasets. Our best equation adeptly captures cloud cover distributions across various regimes, notably excelling in representing marine stratocumulus clouds by learning to utilize the vertical relative humidity gradient. This research underscores the potential of deep learning in achieving accurate cloud parameterizations and emphasizes the effective role of symbolic regression in deriving interpretable, consistent equations for cloud cover
Understanding and Modelling Convection with Machine Learning
Full text linkMachine Learning (ML) has demonstrated its potential to improve the performance of an Earth System Model (ESM), yet many challenges remain. ESMs are essential tools to project and understand climate change but have known biases. Convective processes are in general unresolved as their typical length scale is smaller than the grid size of ESMs. The effect of such subgrid processes is estimated with parameterizations, that are often attributed to be sources of biases in ESMs. A way to reduce these limitations of ESMs is to take advantage of ML or deep learning (DL) algorithms that learn actively on output from convection permitting high-resolution simulations which explicitly resolve convective processes. The resulting ML parameterizations are then coupled with an ESM and replace existing traditional subgrid parameterizations in hybrid (physics + ML) ESMs. This thesis presents novel approaches to transform DL algorithms from data science concepts towards an operational use in ESM simulations. First, a DL algorithm is developed that enables to better understand subgrid convective processes and interactions with the large-scale environment. Specifically, the latent space of a Variational Encoder Decoder (VED) reveals the meridional temperature differences between the tropics and poles together with the characteristics of subtropical and subpolar air masses along the mid-latitude storm tracks are key drivers of convective processes. Moreover, the VED separates key characteristics of shallow convective, cumulus, cirrus-like and deep convection regimes. Second, a novel DL algorithm ensemble approach is developed, that provides an improved representation of convective processes. Third, it is demonstrated that the more realistic uncertainty quantification of the ensembles capturing the chaotic nature of subgrid processes stabilizes hybrid simulations and reduces longstanding biases in a hybrid model run of an ESM. This thesis thus advances the modelling of convective processes with DL in Earth system sciences via enhanced representation and understanding of convective processes in ESMs. It provides ways to reduce limitations of state-of-the-art ML models and paves a way forward to the operational use of DL and ML in the next generation of ESMs
Variations on the Author
Full text link“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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe 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
Causal discovery of Atlantic-Pacific interactions in observations and CMIP6 models
Full text linkIn a world of rapid climate change, a deeper comprehension of the Earth's climate system is central to accurate climate projections. In this regard, understanding the dynamics governing the climate system is important. The changing climate processes are influenced by a variety of factors including both natural and anthropogenic forcings, which modulate the interactions between major modes of climate variability. These interactions, particularly the teleconnections between the Atlantic and Pacific oceans, have a profound impact on global and regional climate patterns, necessitating a detailed exploration to grasp the complex networks of interrelated impacts. In this thesis, causal discovery approaches are applied to unravel the causal relationships for these interactions, aiming to enhance the understanding of the processes governing the climate system.
The first part of this Ph.D. thesis delves into this complex system by applying an algorithm for causal discovery to analyze observational and reanalysis datasets, in addition to large ensemble simulations from a collection of models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP). Dependent on the phases of the Pacific Decadal Variability (PDV) and the Atlantic Multidecadal Variability (AMV), different regimes with characteristic causal relationships (fingerprints) are identified in observations (and reanalyses) as well as in CMIP6. A regime-oriented causal model evaluation is then performed to assess the capability of CMIP6 models in representing observed changing interactions between PDV, AMV, and their extratropical teleconnections. Causal networks from observations show both opposite-sign and same-sign responses between AMV and PDV under specific conditions. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns but overall perform better when PDV and AMV are out of phase. Additionally, the two largest ensembles, (in terms of number of realizations) were found to contain realizations with most similar causal fingerprints to observations. For most regimes, these same models also showed higher network similarity when compared to each other.
In the second part of this thesis, the focus is on examining the tropical and extra-tropical routes connecting Pacific and Atlantic modes of variability on seasonal to interannual timescales. Following up on recent studies, this analysis characterizes two distinctive phases: the Pacific driven
regime (1950-1983) and the Atlantic-driven regime (1985-2014), spotlighting the varying role of El Niño-Southern Oscillation (ENSO) in shaping sea surface temperature variability in the tropical Atlantic. Guided by the results of the first study, the use of large ensemble simulations in this second study intends to separate the contributions of external forcings from natural internal variability. A comparative analysis examines results from observations (and reanalysis) in contrast to those from Pacific pacemaker simulations, unveiling effects of anthropogenic external forcing, especially in the most recent decades. Specifically, the 1985-2014 results suggest that human-induced anomalous tropical north Atlantic warming greatly contributed to La Niña-like cooling over the tropical Pacific through the strengthening of the Pacific Walker circulation. On the other hand, the causal analysis of the pre-industrial control run emphasizes the importance of natural internal variability on decadal timescales in modulating the interplay between interannual climate variability modes over the two basins.
Generally, the results presented in this thesis demonstrate the large potential of causal discovery for process-oriented model evaluation that can substantially enhance our understanding of climate variability and provide robust diagnostics for refining climate models. Furthermore, this thesis underscores the role of the intricate interplay between natural variability and external forcings in shaping climate patterns, and advocates for further research to precisely attribute the observed changes in the climate system. The insights gained are hence significant for formulating more accurate and informed climate projections as well as adaptation and mitigation strategies
Analysis of NOx ship emissions with model and satellite data
No full textNitrogen oxide and dioxide emissions (NO NO2=NOx) by international shipping account for approximately 9-15% of NOx global emission totals. As NOx participates in the catalytic production of tropospheric ozone, global chemistry climate models cannot neglect ship induced NOx in their forecast of future atmospheric development. Both the knowledge of the NOx emission flux as function of time and place and an understanding of the chemical processes transforming NOx in the atmosphere are needed. Thiswork addresses both aspects.The first part of this thesis analyses the method used by global models to include NOx ship emissions. For this purpose, the dispersion and chemical conversion of emissions in the near-field of a single ship are studied with two different modelling approaches. While both techniques use the same photochemical box model to solve the chemical equations, the dilution of the exhaust into the background air is different. One approach uses a Gaussian plume model and accounts for the expansion phase of a plume. The other one instantaneously disperses the emissions over a large gridbox, a technique commonly used by large scale models. The ozone change is overestimated by the global-model approach by up to a factor of three. One possibility to account for these sub-grid processes in global models is the use of effective emissions, i.e. actual emissions are changed and emissions of additional compounds like ozone are introduced in a way that they take sub-grid processes into account. It is shown for this case that the method is able to account for the neglect of sub-grid processes in global models for different emission times and emission strengths.In the second part of the thesis, an inventory of NOx emission from international shipping has been evaluated by comparing NO2 tropospheric columns derived from the satellite instruments GOME (January 1996 to June 2003), SCIAMACHY (January 2003 to February 2008), and GOME-2 (March 2007 to February 2008) to NO2 columns calculated with the atmospheric chemistry general circulation model ECHAM5/MESSy1 (January 2000 to October 2005). The data set from SCIAMACHY yields the first monthly analysis of shipinduced NO2 enhancements in the Indian Ocean. For both data and model consistently the tropospheric excess method was used to obtain mean NO2 columns over the shipping lane from India to Indonesia. In general, the model simulates the differences between the regions affected by ship pollution and ship free regions reasonably well. Therefore, it is concluded, that the NOx ship emission inventory used inthis study is a good approximation of NOx ship emissions inthe Indian Ocean for the years 2002 to 2007. It assumes that around 6 Tg(N)/yr are emitted by international shipping globally, resulting in 90 Gg(N)/yr in the region of interest when using Automated Mutual Assistance Vessel Rescue System (AMVER) as spatial proxy. The results do not support some previously published lower ship emissions estimates of 3-4 Tg(N)/yr globally
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