A5074500868- Climate variability and models
- Oceanographic and Atmospheric Processes
- Geology and Paleoclimatology Research
- Meteorological Phenomena and Simulations
- Marine and coastal ecosystems
- Atmospheric and Environmental Gas Dynamics
- Spectroscopy and Chemometric Analyses
- Remote Sensing in Agriculture
- Solar Radiation and Photovoltaics
- Sustainability and Ecological Systems Analysis
- Geophysics and Gravity Measurements
- Mental Health Research Topics
- Complex Systems and Time Series Analysis
- Pacific and Southeast Asian Studies
- Spectroscopy and Laser Applications
- Stellar, planetary, and galactic studies
- Flood Risk Assessment and Management
- Fluid Dynamics and Turbulent Flows
- Boron and Carbon Nanomaterials Research
- Complex Network Analysis Techniques
- Tree-ring climate responses
- Ecosystem dynamics and resilience
- Marine Biology and Ecology Research
- Inorganic Fluorides and Related Compounds
- Geophysical and Geoelectrical Methods
Courant Institute of Mathematical Sciences
2021-2024
New York University
2021-2024
Georgia Institute of Technology
2017-2022
Institute of Atmospheric Sciences and Climate
2016
Revealing the 3D dynamics of HII regions and their associated molecular clouds is important for understanding longstanding problem as to how stellar feedback affects density structure kinematics interstellar medium. We employed observations region RCW 120 in [CII], observed within SOFIA legacy program FEEDBACK, $^{12}$CO $^{13}$CO (3$\to$2) lines, obtained with APEX. In addition we used HI data from Southern Galactic Plane Survey. Two radiative transfer models were fit data. A line profile...
The threat of global warming and the demand for reliable climate predictions pose a formidable challenge because system is multiscale, high-dimensional nonlinear. Spatiotemporal recurrences hint to presence low-dimensional manifold containing trajectory that could make problem more tractable. Here we argue reproducing geometrical topological properties attractor should be key target models used in projections. In doing so, propose general data-driven framework characterize showcase it...
We propose a data-driven framework to describe spatiotemporal climate variability in terms of few entities and their causal linkages. Given high-dimensional field, the methodology first reduces its dimensionality into set regionally constrained patterns. Causal relations among such patterns are then inferred interventional sense through fluctuation-response formalism. To distinguish between true spurious responses, we an analytical null model for fluctuation-dissipation relation, therefore...
One of the greatest sources uncertainty in future climate projections comes from limitations modelling clouds and understanding how different cloud types interact with system. A key first step reducing this is to accurately classify at high spatial temporal resolution. In paper, we introduce Cumulo, a benchmark dataset for training evaluating global classification models. It consists one year 1km resolution MODIS hyperspectral imagery merged pixel-width 'tracks' CloudSat labels. Bringing...
Abstract A framework for analyzing and benchmarking climate model outputs is built upon δ ‐MAPS, a recently developed complex network analysis method. The allows the possibility of highlighting quantifiable topological differences across data sets, capturing magnitude interactions including lagged relationships quantifying modeled internal variability, changes in domains properties their connections over space time. set four metrics proposed to assess compare shapes, strengths, connectivity...
Abstract Global climate change represents one of the greatest challenges facing society and ecosystems today. It impacts key aspects everyday life disrupts ecosystem integrity function. The exponential growth data combined with Knowledge-Discovery through Data-mining (KDD) promises an unparalleled level understanding how system responds to anthropogenic forcing. To date, however, this potential has not been fully realized, in stark contrast seminal KDD other fields such as health...
A foundational paradigm in marine ecology is that Oceans are divided into distinct ecoregions demarking unique assemblages of species where the characteristics water masses, and quantity quality environmental resources generally similar. In most world Ocean, defining these complicated by data sparseness away coastal areas large-scale dispersal potential ocean currents. Furthermore, currents change space time on scales pertinent to transitions biological communities, predictions community...
Global warming is posed to modify the modes of variability that control much climate predictability at seasonal interannual scales. The quantification changes in over any given amount time, however, remains challenging. Here we build upon recent advances non-linear dynamical systems theory and introduce community an information entropy quantifier based on recurrence. entropy, or complexity a system associated with microstates recur time time-series define system, therefore its potential. A...
Abstract Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st acceleration satellite record. At regional scale, evolution of probability distributions is often assumed to be dominated by changes mean. However, quantification distributional shapes changing climate currently missing. To this end, we propose novel framework quantifying from time series data. The first quantifies linear trends quantiles through quantile regression. Quantile slopes are...
Abstract The Gulf of Mexico circulation is modulated by a mesoscale current, the Loop Current (LC), and large anticyclonic eddies that detach from it. LC dynamics are recurrent, its evolution in few preferential states. This observation points to existence low‐dimensional dynamical attractor. Building upon advancements system theory, this work characterizes average instantaneous dimensions such an dimension time compared among altimeter‐based data set, ocean reanalysis operational hindcast....
Abstract The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity GHG levels is central for formulating effective policies, yet its exact value shroud in uncertainty. Climate quantitatively expressed terms Equilibrium Sensitivity (ECS) and Transient Response (TCR), estimating temperature responses after an abrupt or transient doubling CO 2 . Here, we represent the complex...
The energy surplus resulting from radiative forcing causes warming of the Earth system. This initial drives a myriad changes including in sea surface temperatures (SSTs), leading to different feedbacks. relationship between feedbacks and pattern SST is referred as "pattern effect". current approach study effect relies on diagnosing response atmosphere-only models perturbations boundary condition. Here, we argue that fluctuation-dissipation relation (FDR), together with coarse-graining...
<title>Abstract</title> The 2015 Paris agreement was established to limit Greenhouse gas global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity greenhouse levels is central for formulating effective policies, yet its exact value shroud in uncertainty. Climate quantitatively expressed terms Equilibrium Sensitivity (ECS) and Transient Response (TCR), estimating temperature responses after an abrupt or transient doubling CO2. Here, we use a holistic approach...
Abstract We explore the potential of Gaussian mixture model (GMM), an unsupervised machine-learning method, to identify coherent physical structures in interstellar medium. The implementation we present can be used on any kind spatially and spectrally resolved data set. provide a step-by-step guide use these models different sources sets. Following guide, run NGC 1977, RCW 120, 49 using [C ii ] 158 μ m mapping observations from SOFIA telescope. find that identified six, four, five velocity...
Most climate models show a precipitation increase with warming that is smaller than the in moisture, which requires weakening of convective mass flux and slowing overturning circulation. In this study we use global-storm resolving (DYAMOND models) to identify systematic relationships between precipitation, vertical velocity circulation tropics. The cloud-resolving simulations are 40-day long winter allow us dynamical response over wide range spatial scales. A data reduction inference method,...
We propose a data-driven framework to simplify the description of spatiotemporal climate variability into few entities and their causal linkages. Given high-dimensional field, methodology first reduces its dimensionality set regionally constrained patterns. Time-dependent links are then inferred in interventional sense through fluctuation-response formalism, as shown Baldovin et al. (2020). These two steps allow explore how regional can influence remote locations. To distinguish between true...
We explore the potential of Gaussian Mixture Model (GMM), an unsupervised machine learning method, to identify coherent physical structures in ISM. The implementation we present can be used on any kind spatially and spectrally resolved data set. provide a step-by-step guide use these models different sources sets. Following guide, run NGC 1977, RCW 120 49 using [CII] 158 $\mu$m mapping observations from SOFIA telescope. find that identified 6, 4 5 velocity 49, respectively, which are...