Results 31 to 40 of about 169 (61)

Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks

open access: yes, 2023
Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data.
Adcroft, Alistair   +3 more
core  

Increasing the Earth's Albedo: The K\"ohler Equation at Sea

open access: yes
Increasing marine haze and clouds has been considered as a possible means of increasing the Earth's albedo. This would reduce Solar heating and global warming, counteracting the effects of the anthropogenic increase in greenhouse gases.
Katz, J. I.
core  

An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data

open access: yes, 2023
Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Every year, globally an average of 90 TCs occur over tropical waters, and global warming is making them stronger, larger and more destructive.
Accarino, Gabriele   +4 more
core  

Tipping points in overturning circulation mediated by ocean mixing and the configuration and magnitude of the hydrological cycle: A simple model

open access: yes, 2023
The current configuration of the ocean overturning involves upwelling predominantly in the Southern Ocean and sinking predominantly in the Atlantic basin.
Brett, G. Jay   +9 more
core  

Glacial abrupt climate change as a multi-scale phenomenon resulting from monostable excitable dynamics

open access: yes
Paleoclimate proxies reveal abrupt transitions of the North Atlantic climate during past glacial intervals known as Dansgaard--Oeschger (DO) events.
Boers, Niklas   +2 more
core  

Using Machine Learning for Model Physics: an Overview

open access: yes, 2020
In the overview, a generic mathematical object (mapping) is introduced, and its relation to model physics parameterization is explained. Machine learning (ML) tools that can be used to emulate and/or approximate mappings are introduced.
Krasnopolsky, Vladimir
core  

Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)

open access: yes
PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to real-time conditions.
Alexe, Mihai   +5 more
core  

Building Ocean Climate Emulators

open access: yes
The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate ...
Subel, Adam, Zanna, Laure
core  

Substantial Risk of 21st Century AMOC Tipping even under Moderate Climate Change

open access: yes
The Atlantic Meridional Overturning Circulation (AMOC) is a key component of the climate system and considered to be a tipping element. There is still a large uncertainty on the critical global warming level at which the AMOC will start to collapse. Here
Dijkstra, Henk A.   +3 more
core  

Embedding machine-learnt sub-grid variability improves climate model biases

open access: yes
The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases.
Briant, James   +3 more
core  

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