Visual Analysis of Variability and Features of Climate Simulation Ensembles

This PhD thesis is concerned with the visual analysis of time-dependent scalar field ensembles as occur in climate simulations. Modern climate projections consist of multiple simulation runs (ensemble members) that vary in parameter settings and/or initial values, which leads to variations in the resulting simulation data. The goal of ensemble simulations is to sample the space of possible futures under the given climate model and provide quantitative information about uncertainty in the results. The analysis of such data is challenging because apart from the spatiotemporal data, also variability has to be analyzed and communicated. This thesis presents novel techniques to analyze climate simulation ensembles visually. A central question is how the data can be aggregated under minimized information loss. To address this question, a key technique applied in several places in this work is clustering. The first part of the thesis addresses the challenge of finding clusters in the ensemble simulation data. Various distance metrics lend themselves for the comparison of scalar fields which are explored theoretically and practically. A visual analytics interface allows the user to interactively explore and compare multiple parameter settings for the clustering and investigate the resulting clusters, i.e. prototypical climate phenomena. A central contribution here is the development of design principles for analyzing variability in decadal climate simulations, which has lead to a visualization system centered around the new Clustering Timeline. This is a variant of a Sankey diagram that utilizes clustering results to communicate climatic states over time coupled with ensemble member agreement. It can reveal several interesting properties of the dataset, such as: into how many inherently similar groups the ensemble can be divided at any given time, whether the ensemble diverges in general, whether there are different phases in the time lapse, maybe periodicity, or outliers. The Clustering Timeline is also used to compare multiple climate simulation models and assess their performance. The Hierarchical Clustering Timeline is an advanced version of the above. It introduces the concept of a cluster hierarchy that may group the whole dataset down to the individual static scalar fields into clusters of various sizes and densities recording the nesting relationship between them. One more contribution of this work in terms of visualization research is, that ways are investigated how to practically utilize a hierarchical clustering of time-dependent scalar fields to analyze the data. To this end, a system of different views is proposed which are linked through various interaction possibilities. The main advantage of the system is that a dataset can now be inspected at an arbitrary level of detail without having to recompute a clustering with different parameters. Interesting branches of the simulation can be expanded to reveal smaller differences in critical clusters or folded to show only a coarse representation of the less interesting parts of the dataset. The last building block of the suit of visual analysis methods developed for this thesis aims at a robust, (largely) automatic detection and tracking of certain features in a scalar field ensemble. Techniques are presented that I found can identify and track super- and sub-levelsets. And I derive “centers of action” from these sets which mark the location of extremal climate phenomena that govern the weather (e.g. Icelandic Low and Azores High). The thesis also presents visual and quantitative techniques to evaluate the temporal change of the positions of these centers; such a displacement would be likely to manifest in changes in weather. In a preliminary analysis with my collaborators, we indeed observed changes in the loci of the centers of action in a simulation with increased greenhouse gas concentration as compared to pre-industrial concentration levels

Proceedings of the 2011 New York Workshop on Computer, Earth and Space Science

The purpose of the New York Workshop on Computer, Earth and Space Sciences is to bring together the New York area's finest Astronomers, Statisticians, Computer Scientists, Space and Earth Scientists to explore potential synergies between their respective fields. The 2011 edition (CESS2011) was a great success, and we would like to thank all of the presenters and participants for attending. This year was also special as it included authors from the upcoming book titled "Advances in Machine Learning and Data Mining for Astronomy". Over two days, the latest advanced techniques used to analyze the vast amounts of information now available for the understanding of our universe and our planet were presented. These proceedings attempt to provide a small window into what the current state of research is in this vast interdisciplinary field and we'd like to thank the speakers who spent the time to contribute to this volume.Comment: Author lists modified. 82 pages. Workshop Proceedings from CESS 2011 in New York City, Goddard Institute for Space Studie

Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional method

A global classification of coastal flood hazard climates associated with large-scale oceanographic forcing

Coastal communities throughout the world are exposed to numerous and increasing threats, such as coastal flooding and erosion, saltwater intrusion and wetland degradation. Here, we present the first global-scale analysis of the main drivers of coastal flooding due to large-scale oceanographic factors. Given the large dimensionality of the problem (e.g. spatiotemporal variability in flood magnitude and the relative influence of waves, tides and surge levels), we have performed a computer-based classification to identify geographical areas with homogeneous climates. Results show that 75% of coastal regions around the globe have the potential for very large flooding events with low probabilities (unbounded tails), 82% are tide-dominated, and almost 49% are highly susceptible to increases in flooding frequency due to sea-level rise.A.R., F.J.M. and P.C. acknowledge the support of the Spanish ‘Ministerio de Economia y Competitividad’ under Grants BIA2014-59643-R and BIA2015-70644-R. This work was critically supported by the US Geological Survey under Grant/Cooperative Agreement G15AC00426 and from the US DOD Strategic Environmental Research and Development Program (SERDP Project RC-2644) through the NOAA National Centers for Environmental Information (NCEI). Dynamic atmospheric corrections (storm surge) are produced by CLS Space Oceanography Division using the Mog2D model from Legos and distributed by Aviso, with support from CNES (http://www.aviso.altimetry.fr/). Marine data from global reanalysis are provided by IHCantabria and are available for research purposes upon request at [email protected]

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP

Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force

Explainable deep learning for insights in El Ni\~no and river flows

The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections

Internal variability and potential predictability of the global carbon cycle in a perfect-model framework

Reductions of anthropogenic CO2 emissions are required to stabilize and reduce atmospheric CO2 concentrations. But variations in the land and ocean carbon sinks, which are triggered by inherent climate vari- ability, disguise CO2 emission reductions in global atmospheric CO2 in the near-term. Therefore, an independent verification of CO2 emission reductions in atmospheric CO2 needs to take internal variability into account. Initialized predictions of the evolution of atmospheric CO2 have the potential to constrain this internal variability and enable an ahead-of-time estimate under different proposed CO2 emission reductions pathways. To that end, this dissertation focuses on near- term variability in the global carbon cycle, and how initialized CO2 predictions can guide policy-makers onto a pathway of limiting global warming below 2°C. First, I motivate initialized CO2 predictions by showcasing the large envelope of internal variability of the global carbon cycle, which ini- tialized prediction can partly constrain. I deduce time-scales in the near-term on which CO2 emission reductions cause atmospheric CO2 growth to decelerate. I find that CO2 emission reductions compatible with the Paris Agreement only cause a deceleration of atmospheric CO2 after a decade. Particularly on the five-year scale, on which the global stocktake assesses the efficacy of CO2 emission reductions, inter- nal variability disguises certainty in causation. This study shows how expectations about the near-term efficacy of CO2 emission reductions need to consider internal variability. Then, I evaluate initialized predictive skill of prognostic atmospheric CO2 and its drivers, which were previously unknown, by perform- ing initialized ensemble Earth System Model (ESM) simulations in a perfect-model predictability framework. I show that internal variabil- ity of prognostic global atmospheric CO2 is predictable up to three years in advance. ESM-based predictions are feasible and surpass a statistical regression forecast in scope and accuracy. Finally, I test the realism of state-of-the-art carbon cycle predictions, which initialize the carbon cycle only indirectly via the reconstruction of the physical climate dynamics. In a perfect-model reconstruction framework, I test the commonly stated assumption that direct carbon cycle reconstruction improves its predictability. I find that indirect re- construction tracks the target reasonably well. While direct reconstruc- tion improves global carbon cycle initial conditions and predictability slightly, a mean bias reduction achieves similar improvements. This adds confidence to the current practice of indirect carbon cycle recon- struction and refutes the need for direct carbon cycle reconstruction. My results demonstrate that internal variability of global carbon cycle can disguise CO2 emission reductions and thereby mislead the evaluation on mitigation efficacy. As a partial solution, initialized predictions can constrain this internal variability for up to three years into the future, which can guide policy-makers navigating on the path towards well below 2°C global warming.Um die Konzentration von CO2 in der Atmosphäre zu stabilisieren und zu reduzieren, müssen die menschengemachten CO2-Emissionen gesenkt werden. Schwankung der Land- und Ozeankohlenstoffsenken, die durch die inhärente Klimavariabilität ausgelöst werden, verdecken allerdings kurzfristig die CO2-Emissionsreduktionen im globalen at- mosphärischen CO2. Daher muss für eine unabhängige Verifizierung der CO2-Emissionsminderungen im atmosphärischen CO2 die inter- ne Variabilität berücksichtigt werden. Initialisierte Vorhersagen von atmosphärischem CO2 haben das Potenzial, diese interne Variabili- tät einzugrenzen und damit eine vorausschauende Abschätzung der Wirkung von verschiedenen Szenarien der CO2-Emissionsreduktion zu ermöglichen. Daher konzentriert sich diese Dissertation auf die kurzfristige Variabilität im globalen Kohlenstoffkreislauf und darauf, wie initialisierte CO2-Vorhersagen politischen Entscheidungsträgern helfen können, einen Kurs zur Begrenzung der globalen Erwärmung auf unter 2°C zu finden und zu halten. Zunächst rechtfertige ich initialisierte CO2-Vorhersagen, indem ich die große Bandbreite an interner Variabilität des globalen Kohlen- stoffkreislaufs aufzeige, die durch initialisierte Vorhersagen zum Teil eingegrenzt werden kann. Ich leite Zeitskalen in der nahen Zukunft ab, auf denen CO2-Emissionsreduktionen eine Verlangsamung des atmosphärischen CO2 verursachen. Eine Erkenntnis ist, dass CO2- Emissionsminderungen, die mit dem Pariser Klimaabkommen kom- patibel sind, erst nach einem Jahrzehnt das Abbremsen des atmo- sphärisches CO2 Wachstums mit Sicherheit verursachen. Insbesondere im Zeitraum von fünf Jahren, in dem die Wirksamkeit von CO2- Emissionsreduktionen in Folge des Pariser Klimaabkommens bewertet wird, kann die Kausalität wegen der internen Variabilität nicht klar nachgewiesen werden. Diese Studie zeigt, dass Erwartungen an die kurzfristige Wirksamkeit von CO2-Emissionsminderungen die interne Variabilität berücksichtigen müssen. Anschließend untersuche ich initialisierte Vorhersagbarkeit von at- mosphärischem CO2 und dessen Treibern, indem ich Simulationen in einem idealisierten, initialisierten Erdsystemmodell (ESM) durchführe. Darin zeige ich, dass die interne Variabilität des prognostizierten glo- balen atmosphärischen CO2 bis zu drei Jahre im Voraus vorhersagbar ist. ESM-basierte Vorhersagen sind praktikabel und übertreffen eine statistische Regressionsvorhersage in Reichweite und Genauigkeit. Schließlich teste ich die Realitätsnähe von neuartigen Kohlenstoff- kreislaufvorhersagen, die den Kohlenstoffkreislauf nur indirekt über die Rekonstruktion der physikalischen Klimadynamik initialisieren. In idealisierten Rekonstruktionen teste ich die häufig geäußerte An- nahme, dass die direkte Rekonstruktion des Kohlenstoffkreislaufs dessen Vorhersagbarkeit verbessert. Ich kann zeigen, dass die in- direkte Rekonstruktion die eigentlichen Startbedingungen recht gut wiederherstellt. Während die direkte Rekonstruktion die Startbedin- gungen und die Vorhersagbarkeit des globalen Kohlenstoffkreislaufs leicht verbessert, erzielt eine Angleichung der mittleren Abweichung ähnliche Verbesserungen. Dies stärkt die Zuversicht in die derzeitige Praxis der indirekten Rekonstruktion des Kohlenstoffkreislaufes und widerlegt die Notwendigkeit von dessen direkter Rekonstruktion. Meine Ergebnisse zeigen, dass die interne Variabilität des globalen Kohlenstoffkreislaufs die globalen CO2-Emissionsreduktionen ver- schleiern und damit die Bewertung von dessen Wirksamkeit in die Irre führen kann. Als Teillösung können initialisierte Vorhersagen die- se interne Variabilität für bis zu drei Jahre in die Zukunft begrenzen. Diese Vorhersagen können politische Entscheidungsträger leiten, die globale Erwärmung auf deutlich unter 2°C zu begrenzen