Monday, 11 November 2024
Using results from numerical simulations and simplified models, we will see how turbulence in a cloud can be different from "normal" turbulence. We will discuss what effect this can have on raindrop growth.
-
A brief tour of Chaos in 1-dimensional maps followed by a quick primer on Machine Learning. This will help researchers in Climate Science as there is an increasing use of AI/ML methods in this domain.
-
The research aimed at wind-wave interaction study in a big range of wind speeds, from light and moderate at a regional scale up to high wind speeds at hurricane wind conditions is performed. The regional model based on the WAVEWATCH III spectral wave model is adapted to the conditions of an inland water body using the WRF atmospheric model. Adaptation and verification of the models was carried out on the basis of the results of a series of field experiments to study the wind-wave regime of the Gorky reservoir performed in 2012–2019 using an autonomous buoy station based on the Froude oceanographic buoy. Within the framework of the WAVEWATCH III model, an analysis of the influence on the simulation result and subsequent adjustment of the parameters of the WAM 3 wind input parameterization was made, as well as the scheme for the approximate calculation of the Boltzmann integral Discrete Interaction Approximation (DIA) is adapted. Within the framework of the WRF model, calculations were carried out using various parameterizations of the planetary boundary layer and the near-surface layer of the atmosphere, and the advantage of using the Large Eddy Simulation (LES) method was shown.
At high wind speeds, the influence of small-scale processes at the ocean-atmosphere boundary layer such as spray and foam on the surface waves prediction is studied. Estimates of the effect of including the exact number of specific fragmentation "parachute" type in the spray on the resulting drag coefficient is shown. For the estimates, the numerical simulations within WAVEWATCH III wave model are performed. The importance of small-scale processes for waves at hurricane winds prediction and the prospects for their inclusion in modern numerical wave models is shown.
The new method for retrieving the parameters of the atmospheric boundary layer in hurricanes is proposed. It is based on the approximating of the upper parabolic part of the wind speed profile and the retrieval of the lower logarithmic part. Based on the logarithmic part, the friction velocity, near-surface wind speed and the aerodynamic drag coefficient are obtained. The obtained data is used for the verification of the modeling data in WRF-ARW model. The case of the Irma hurricane is studied. Different configurations of the model are tested, which differ in the use of physical parameterizations. The difference of wind profiles in various sectors of the hurricane is studied.
Acknowledgements. The work was supported by FSTP project “Study of processes in the boundary layers of the atmosphere, ocean and terrestrial waters and their parameterization in Earth system models” within the project “Improvement of the global world-level Earth system model for research purposes and scenarios forecasting of climate change”
Climate networks defined on a regular grid of geographic locations (nodes) covering the Earth's surface, built from the analysis of statistical interdependencies of climate time series, can provide useful information on large-scale patterns of climate variability. In this talk, I will discuss climate networks constructed from surface air temperature time series, using different methods such as Hilbert analysis, mutual information and Granger causality.
Tuesday, 12 November 2024
Linear decomposition techniques, such as Empirical Orthogonal Functions (EOFs) and Maximum Covariance Analysis (MCAs), have long been used to reveal spatial and temporal climate patterns, capturing relationships between climatic variables and large-scale drivers like the El Niño-Southern Oscillation (ENSO). However, these approaches often overlook complex, nonlinear interactions essential for understanding extreme events’ interconnectedness. To address these limitations, our work integrates a complex network framework capable of analyzing high-dimensional data without the assumptions inherent in traditional linear decomposition. Climate networks can reveal intricate interdependencies in the climate system, identifying nodes and links that represent statistically relevant associations. This network-based approach provides insights into synchronous extremes by quantifying linear and nonlinear relationships, enabling us to explore the dynamic nature of extreme event synchrony. We present findings from our recent work, which reveal central India as a key hub for synchronous extreme rainfall events during the Indian Summer Monsoon, characterized by persistent yet geographically localized connections. This “geographical trapping” of extremes is modulated by ENSO phases, with stronger localized synchronicity in El Niño periods and broader linkages in La Niña years. On a global scale, our drought analysis shows that temperature trends drive drought synchrony, while sea surface temperature variability imposes limits, maintaining drought clustering within certain bounds and safeguarding against widespread synchrony across key agricultural regions. Our approach underscores the critical role of synchrony for disaster preparedness and food security. By bridging linear and non-linear techniques, this framework provides actionable insights into extreme events' interconnected patterns, informing strategies for resilience and proactive risk management across multiple scales
-
I present the recurrence analysis of temperature and relative humidity data from various locations spread over India, including the mountainous region, coastal region, and central and north eastern parts of India. This study reveals the spatiotemporal pattern underlying the climate dynamics and captures the variations in the complexity of the dynamics over the period 1948 to 2022. By reconstructing the dynamics from data, the recurrence pattern is studied using recurrence networks and the measures of the networks computed using a sliding window analysis on the data sets. This brings out the climate variability in different spatial locations and the heterogeneity across the locations chosen. The variations observed in dynamics can be correlated with reported shifts in the climate related to strong and moderate El Niño–Southern Oscillation events.
-
Complex networks have revolutionised the way non-linear dynamical (deterministic and stochastic) systems are represented and analysed. This paradigm shift owes itself to the ability to encode non-linear relationships in a hierarchical manner from the skeletal structure to deeper and subtle spatio-temporal dependencies. This talk aims to provide an overview of a class of complex networks known as causal networks that draw ideas from various fields including econometrics, social sciences, neuroscience, sciences, ecology and engineering. Of specific interest and relevance are the causal climate networks. The first half of the talk shall be devoted the overview and mathematical formalism of different types of (climate) causal networks with focus on Granger causal and convergent cross-mapping (CCM) class of networks, both of which are constructed from time-series data. The second part of this talk is devoted to a presentation of applications to reconstructing climate networks from data and their analysis, which will include results from our cross-disciplinary research and glimpses from existing literature.
Critical transitions, relatively sudden transitions between qualitatively different dynamics, are due to various distinct mechanisms. So far, bifurcation induced, noise- induced, shock-induced or rate-induced transitions have been studied extensively. In complex systems like the climate system or ecosystems, particularly in coupled versions of them, the dynamics of different components or different subsystems is characterized by different timescales. One simple example are ecosystems exhibiting allometric slowing down, that means that the duration of lifecycles increases with the trophic level. Coupling different compartments of the climate system involves also different timescales as the intrinsic timescales of flow patterns in the atmosphere are much faster than in the ocean. To study the dynamics of such systems requires the use of the methodology of slow-fast systems to account properly for such timescale separation. We will discuss the concept of critical manifolds in slow-fast systems and its impact on critical transitions. Specifically, we discuss the impact of timescale separation on noise-induced and rate-induced transitions and explain the methodology using simple conceptual models.
Wednesday, 13 November 2024
Over the past decade, climate networks have emerged as a powerful tool to characterise high dimensional weather and climate datasets. Climate networks are a sparse representation of the dynamical similarities between weather time series from different geographical locations. Nodes represent the locations themselves, and network edges represent high dynamical similarity between pairs of locations. The topology of the resulting complex network encodes information about how atmospheric and oceanic dynamics “connect” different locations. For instance, strong monsoon years might yield a different network structure than weak monsoon years. With the tools of graph theory and complex networks at our disposal, we can characterise climate dynamics in novel and interesting ways, which yield, in part, results that corroborate what meteorologists already know, and, in part, results that generate new hypotheses about how atmospheric and oceanic processes influence different weather patterns. In this talk, I will present a brief overview of how we estimate climate networks from data, the challenges involved in the estimation process, and finally a few examples of how we obtain new data-driven hypotheses about weather patterns using this tool.
-
-
Oceanic flows stir and mix tracers such heat, salt, carbon, and plankton and understanding the details of the tracer dispersion is key to developing effective parameterizations for large climate-scale models. Unfortunately, the flow structure in the ocean is highly variable as a function of spatial scales. For instance O(100 km) mesoscale flows are significantly different from O(10 km) submesoscale flows. In this talk I'll use results from a recent study to explain how tracer dispersion characteristics change as we move from large mesoscales to small submesoscales in the oceans.
The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.
-
Thursday, 14 November 2024
-
The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.
Climate change is often related to the temporal variation of external driving forces, following a certain arbitrary trend. This poses difficulties to the analysis of complex dynamical systems under the impact of climate change, since all the analysis tools of nonlinear dynamics work only for autonomous systems or systems with periodic driving. To study the impact of climate change characterized by arbitrary time- dependence requires new methods to still use ideas of attractors, basins of attraction and bifurcations in the non-autonomous case. We discuss approaches which allow to study non-autonomous systems in the spirit of nonlinear dynamics: snapshot/pullback attractors, non-autonomous basins of attractions and bifurcations in non-autonomous systems like rate-induced transitions and basin boundary crossings. We use simple conceptual models of climate and ecosystem dynamics to illustrate these concepts.
The critical phenomena occur in the vicinity of the critical point. These phenomena are indicators of an impending critical transition. Earlier methods treated the critical phenomena as early warning signals. However, they do not show any example where early warning signals have been used to avert an impending transition. They have been used in models, experiments or retrospectively.
The talk will present a perspective on how to address this challenge. I will discuss important limitations that must be accepted to build the knowledge needed for better prediction. I will apply the theory of critical transitions to a prediction of the onset and withdrawal dates of the Indian summer monsoon.
The abruptness of the onset and end of the monsoon and its interannual variability within a month are key features of the phenomenon that make monsoon forecasting extremely challenging. I will describe the main principles of monsoon timing prediction and show the cases for central India. Special attention will be given to the impact of climate change and prospects for extending monsoon forecasts over India and other tropical countries.
The timing of monsoon season onset and withdrawal is of paramount importance to the population of the Indian subcontinent. Despite the rainy season occurring annually between June and September, the onset and withdrawal dates vary by up to a month from year to year, making accurate predictions a significant challenge.
However, a revolutionary approach has been developed those promises to transform our understanding of this phenomenon. By comprehending the core physical mechanisms involved in monsoon onset and withdrawal, spatial-temporal regularities have been discovered that can be used for forecasting. This approach fundamentally diverges from the traditional numerical weather and climate models by relying on the Nonlinear Dynamics and Nonlinear Phenomena in Statistical Physics.
This approach demonstrated successful results over a rigorous nine-year testing period, forecasting the onset date up to 40 days in advance and the withdrawal date up to 70 days in advance. It is also applicable to different regions of India, and other parts of the world in South Asia, Africa and South America.
This approach is the solution to mitigate the impact of climate change on human life and property in the region. The evidence is rock-solid, and it will revolutionize our understanding of monsoons, safeguarding the Indian subcontinent population.
Climate Change is one of the greatest challenges human beings will face in the 21st century. A large majority of the people do not think climate change is an urgent problem because the impact of climate change is not as dramatic as the COVID epidemic. The impact of climate change will, however, pose an existential threat to all mammals. In this lecture I will discuss the science of climate change. I will show the recent insight from the study of the natural climate change during the past million years indicates that the earth’s climate is not stable and has many tipping points. The ability of human being and other mammals to adapt to global warming beyond 2 degrees C is limited. The high impact but low-probability event like the slowing down of the Atlantic Meridional Ocean Circulation (AMOC) will alter the tropical climate dramatically.
Friday, 15 November 2024
The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.
The main focus of these pedagogical talks will be on discussing the interplay between statistics and climate science as a two-way street. On one hand, thinking about the climate helps us understand many aspects of statistics, from the fundamental to conceptual to practical. On the other, statistical thinking is crucial and indispensable in studying climate. I will also emphasize that statistics plays an important role not just in climate studies, but more generally in understanding any complex system such as those from biological and social sciences as well. Another thread will be the discussion of interplay between uncertainty and dynamics, with an emphasis on the role of dynamical instabilities.
Climate networks can be used to forecast some important climate phenomena, such as the monsoon, the North Atlantic Oscillation, El Niño events and cyclones. A percolation framework is used to study the cluster structure properties which brings out the global structural changes in the climate network.
I present the recurrence analysis of temperature and relative humidity data from various locations spread over India, including the mountainous region, coastal region, and central and north eastern parts of India. This study reveals the spatiotemporal pattern underlying the climate dynamics and captures the variations in the complexity of the dynamics over the period 1948 to 2022. By reconstructing the dynamics from data, the recurrence pattern is studied using recurrence networks and the measures of the networks computed using a sliding window analysis on the data sets. This brings out the climate variability in different spatial locations and the heterogeneity across the locations chosen. The variations observed in dynamics can be correlated with reported shifts in the climate related to strong and moderate El Niño–Southern Oscillation events.
Climate networks can be used to forecast some important climate phenomena, such as the monsoon, the North Atlantic Oscillation, El Niño events and cyclones. A percolation framework is used to study the cluster structure properties which brings out the global structural changes in the climate network.
A brief tour of Chaos in 1-dimensional maps followed by a quick primer on Machine Learning. This will help researchers in Climate Science as there is an increasing use of AI/ML methods in this domain.