Results 11 to 20 of about 190 (113)

Multiple‐Instance Superparameterization: 2. The Effects of Stochastic Convection on the Simulated Climate [PDF]

Journal of Advances in Modeling Earth Systems, 2019
The cloud‐permitting model (CPM) of the superparameterized Community Atmosphere Model (SP‐CAM) is a stochastic parameterization. As reported in a companion paper, we have created a variant of SP‐CAM, called MP‐CAM, that uses the averaged feedback of 10 ...
Todd R. Jones, David A. Randall, Mark D. Branson   +2 more
doaj   +14 more sources

High-resolution superparameterization of OpenIFS with DALES [PDF]

, 2019
Poster about superparameterization of clouds, convection and turbulence in the global atmospheric model OpenIFS, using DALES, a high-resolution, three-dimensional large-eddy simulation ...
Jansson, Fredrik, Crommelin, Daan (7119932), Jansson, Fredrik (7119923), Chertova, Maria, Siebesma, Pier (7119935), Chertova, Maria (7119926), Pelupessy, Inti (6885077), Grönqvist, Johanna, Siebesma, Pier, Pelupessy, Inti, Crommelin, Daan, van den Oord, Gijs (5898431), van den Oord, Gijs, Grönqvist, Johanna (7119929)   +13 more
core   +3 more sources

Global Microphysical Sensitivity of Superparameterized Precipitation Extremes

Earth and Space Science, 2021
A recent study found statistically significant differences in extreme precipitation distributions over the contiguous United States (CONUS) when changing the microphysics scheme in a superparameterized global climate model.
Alexander B. Charn, William D. Collins, Hossein Parishani, Mark D. Risser   +3 more
doaj   +4 more sources

Representing cloud mesoscale variability in superparameterized climate models [PDF]

Journal of Advances in Modeling Earth Systems, 2022
In atmospheric modeling, superparameterization (SP) has gained popularity as a technique to improve cloud and convection representations in large-scale models by coupling them locally to cloud-resolving models.
Oord, G. (Gijs) van den, Siebesma, A.P. (Pier), Crommelin, D.T. (Daan), Grönqvist, J.H. (Johanna), Jansson, F.R. (Fredrik), Pelupessy, F.I. (Inti), Chertova, M. (Maria)   +6 more
core   +7 more sources

Regional superparameterization of the OpenIFS atmosphere model by nesting 3D LES models

, 2019
We present a superparameterization of the ECMWF global weather forecasting model OpenIFS with a local, cloud-resolving model. Superparameterization is a multiscale modeling approach used in atmospheric science in which conventional parameterizations of ...
Chertova, Maria (7119926), Crommelin, Daan (7119932), Pelupessy, Inti (6885077), Jansson, Fredrik (7119923), Siebesma, Pier (7119935), van den Oord, Gijs (5898431)   +5 more
core   +4 more sources

Mesoscale Convective Systems in a Superparameterized E3SM Simulation at High Resolution [PDF]

Journal of Advances in Modeling Earth Systems, 2022
Accurately representing mesoscale convective systems (MCSs) is crucial to simulating the energy and water cycles in global climate models. Using a novel MCS identification and tracking algorithm applied to observations and model simulations, we evaluate ...
Guangxing Lin, Christopher R. Jones, L. Ruby Leung, Zhe Feng, Mikhail Ovchinnikov   +4 more
doaj   +2 more sources

Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification

Entropy, 2018
A conditional Gaussian framework for understanding and predicting complex multiscale nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such systems are nevertheless highly nonlinear and are able to capture the non-Gaussian ...
Nan Chen, Andrew J. Majda
doaj   +2 more sources

Superparameterization of Oceanic Boundary Layer Transport

, 2006
Abstract : LONG-TERM GOALS. To achieve deeper understanding of physical processes in turbulent planetary boundary layers and apply it to the development of more physically accurate modeling of coherent vortices to assess the oceanic boundary layer parameterizations used in high-resolution ocean models, specifically with respect to their impact on ...
Georgi Sutyrin, Isaac Ginis
core   +2 more sources

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations [PDF]

Journal of Advances in Modeling Earth Systems, Volume 17, Issue 4, April 2025.
Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes
Gunnar Behrens, Tom Beucler, Fernando Iglesias‐Suarez, Sungduk Yu, Pierre Gentine, Michael Pritchard, Mierk Schwabe, Veronika Eyring   +7 more
wiley   +2 more sources

Multiple‐instance superparameterization, part 1: concept, and predictability of precipitation [PDF]

, 2019
We have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model, SP-CAM. The new configuration, called the multiple-instance superparameterized-CAM, or MP-CAM, uses the average heating
Jones, Todd R., Branson, Mark D., Randall, David A.   +2 more
core   +4 more sources