Highlights of Physics of Climate (GPC) Talks @ APS 2019 March Meeting
To help the community quickly catch up on the work presented in this meting, Paper Digest Team processed all talk abstracts, and generated one highlight sentence (typically the main topic) for each. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each talk.
This article is on the talks related to Physics of Climate (GPC).
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Paper Digest Team
TABLE : Physics of Climate (GPC)
|1||Arctic cloud and sea ice feedbacks from satellite observations and a global climate model||Morrison, Ariel||Here, present and future Arctic cloud-sea ice relationships are assessed using spaceborne lidar observations and a fully-coupled global climate model that incorporates a lidar simulator.||Session 1: Climate Physics: Feedbacks in the Earth System|
|2||Spatiotemporal Dynamics of Lake Patterns in a Changing Arctic Tundra Landscape||Nguyen, Thao; Sudakov, Ivan||On account of similarities in geographical characteristics of the Arctic tundra landscape and that of slow immiscible fluid invasion processes in porous media, we developed our model based on a standard invasion percolation model under the influence of changing temperature.||Session 1: Climate Physics: Feedbacks in the Earth System|
|3||Homotopy Importance Sampler For Noisy Dynamics||Restrepo, Juan; Jensen, Andrew; Miller, Robert||We propose a Bayesian estimation method for moments of a state vector that obeys stochastic nonlinear dynamics and its observations.||Session 1: Climate Physics: Feedbacks in the Earth System|
|4||Application of the Rayleigh-Debye-Gans (RDG)theory for determining optical properties of biomass burning aerosols.||Sarpong, Emmanuel; Smith, Damon; Bililign, Solomon||We report use of the RDG theory to fit experimentally measured optical properties to extract the refractive indices of biomass burning aerosols.||Session 1: Climate Physics: Feedbacks in the Earth System|
|5||A theory for global precipitation change||Jeevanjee, Nadir; Romps, David||Mean precipitation is constrained by radiative cooling, however, and we demonstrate here that radiative cooling profiles exhibit a certain invariance under warming when plotted in temperature coordinates.||Session 1: Climate Physics: Feedbacks in the Earth System|
|6||Estimation of Hourly and Daily Clearness Indices and Diffuse Fraction, over Port Harcourt and Kano using National Centre for Environmental Prediction and National Centre for Atmospheric Research Satellite Data||Omole, Opeyemi; Adeyemi, Babatunde||Estimation of Hourly and Daily Clearness Indices and Diffuse Fraction, over Port Harcourt and Kano using National Centre for Environmental Prediction and National Centre for Atmospheric Research Satellite Data||Session 1: Climate Physics: Feedbacks in the Earth System|
|7||Threshold dependence in the flip-flop model||Kurtze, Douglas||We show, however, that the amplitude and period of oscillations in the model depend strongly on this parameter, being proportional to its value and its square root, respectively.||Session 1: Climate Physics: Feedbacks in the Earth System|
|8||Threshold phenomena in the marine carbon cycle||Rothman, Daniel||Here I show, via construction and analysis of a 2-dimensional dynamical system, how carbon-cycle disruptions can instead result from nonlinear amplification of relatively small perturbations that exceed a threshold.||Session 1: Climate Physics: Feedbacks in the Earth System|
|9||Implications of Lorenz-Mie scattering by cloud droplets in an absorbing atmosphere for cloud feedbacks||Collins, William; Feldman, Daniel; Kuo, Chaincy||Implications of Lorenz-Mie scattering by cloud droplets in an absorbing atmosphere for cloud feedbacks||Session 1: Climate Physics: Feedbacks in the Earth System|
|10||Insights Regarding Observational Requirements For Climate Change Signal Detection||Wielicki, Bruce||Design principles are provided, and examples are given of how more accurate observations can narrow key scientific uncertainties in climate change. Examples will also be presented of observations that can meet these much more challenging climate change requirements.||Session 2: Detecting Signals in a Noisy Climate System|
|11||ENSO Change in Climate Projections: Forced Response or Internal Variability?||Maher, Nicola; Matei, Daniela; Milinski, Sebastian; Marotzke, Jochem||We find that ENSO has high internal variability and that single realizations of a model can produce very different results to the ensemble mean response.||Session 2: Detecting Signals in a Noisy Climate System|
|12||From months to Milankovitch: how timescale-dependent interactions in the coupled Earth system determine the spectrum of climate variability and response.||Proistosescu, Cristian||I will show how the frequency spectrum of Earth’s temperature variability is determined by – and informs on – the climate system’s radiative damping efficiency.||Session 2: Detecting Signals in a Noisy Climate System|
|13||The climate change signal in hurricanes||Lee, Chia-Ying; Sobel, Adam; Tippett, Michael; Camargo, Suzana||In this presentation, I will discuss results from our ongoing research on detecting climate change signal in hurricane activity in the recent history.||Session 2: Detecting Signals in a Noisy Climate System|
|14||Detecting changes in marine ecosystems and ocean colour||Dutkiewicz, Stephanie; Jahn, Oliver; Hickman, Anna; Henson, Stephanie; Beaulieu, Claudie; Monier, Erwan||We use a unique ocean physics, biogeochemistry and ecosystem model that explicitly includes a representation of the ocean’s optical properties to explore how climate change signals are manifested in Chl-a, phytoplankton communities, and ocean colour over the course of the 21 st century.||Session 2: Detecting Signals in a Noisy Climate System|
|15||GPC Business Meeting||GPC Business Meeting||Session 3: GPC Business Meeting|