We explore the representation of the Atlantic Meridional Overturning Circulation (AMOC) in 27 models from the CMIP6 multimodel ensemble. Comparison with RAPID and SAMBA observations suggests that the ...ensemble mean represents the AMOC strength and vertical profile reasonably well. Linear trends over the entire historical period (1850–2014) are generally neutral, but many models exhibit an AMOC peak around the 1980s. Ensemble mean AMOC decline in future (SSP) scenarios is stronger in CMIP6 than CMIP5 models. In fact, AMOC decline in CMIP6 is surprisingly insensitive to the scenario at least up to 2060. We find an emergent relationship among a majority of models between AMOC strength and 21st century AMOC decline. Constraining this relationship with RAPID observations suggests that the AMOC might decline between 6 and 8 Sv (34–45%) by 2100. A smaller group of models projects much less AMOC weakening of only up to 30%.
Plain Language Summary
The Atlantic Meridional Overturning Circulation (AMOC) is a circulation pattern in the Atlantic Ocean that is an important component of the climate system, due to its ability to redistribute and sequester heat and carbon. An accurate representation of the AMOC is a critical test for climate models and essential for building confidence in their projections. Here we investigate the AMOC in 27 climate models that contributed simulations to the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that many models reproduce the observed AMOC quite well, but there are still several models in which the AMOC is too weak or too strong. Most models suggest a slight upward trend in the AMOC from 1850 to the 1980s. Simulations representing different scenarios for future socioeconomic development suggest a stronger AMOC decline compared to previous assessments. Using direct measurements of the AMOC since 2004 and an emerging across‐model relationship between AMOC decline in the 21st century and their present‐day mean state, we find that the majority of CMIP6 models point to an end of century AMOC weakening of 34–45% of its present‐day strength. A smaller group of models projects much less weakening of only up to 30% of its present state.
Key Points
AMOC mean strength is well reproduced by the CMIP6 multimodel mean, but large model spread persists
Projected AMOC decline by the end of the 21st century shows weak dependence on the SSP scenarios
An emergent constraint between AMOC strength and projected decline suggests possible AMOC decline between 34% and 45% by 2100
Orientation of eddy fluxes in geostrophic turbulence Nadiga, B.T
Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences,
07/2008, Volume:
366, Issue:
1875
Journal Article
Peer reviewed
Given its importance in parametrizing eddies, we consider the orientation of eddy flux of potential vorticity (PV) in geostrophic turbulence. We take two different points of view, a classical ...ensemble- or time-average point of view and a second scale decomposition point of view. A net alignment of the eddy flux of PV with the appropriate mean gradient or the large-scale gradient of PV is required. However, we find this alignment to be very weak. A key finding of our study is that in the scale decomposition approach, there is a strong correlation between the eddy flux and a nonlinear combination of resolved gradients. This strong correlation is absent in the classical decomposition. This finding points to a new model to parametrize the effects of eddies in global ocean circulation.
•The physical closures of two CFD codes are compared on a series of experiments.•Both sets of closures deliver acceptable predictions for the experimental database.•The differences in the closures ...are discussed.•A systematic study of the propagation of the uncertainties was also performed.•The void fraction evidences the highest sensitivity to the closure parameters.
The nuclear industry is interested in better understanding the behavior of turbulent boiling flows and in using modern computational tools for the design and analysis of advanced fuels and reactors and for simulation and study of mitigation strategies in accident scenarios. Such interests serve as drivers for the advancement of the 3-dimensional multiphase Computational Fluid Dynamics approach. A pair of parallel efforts have been underway in Europe and in the United States, the NEPTUNE and CASL programs respectively, that aim at delivering advanced simulation tools that will enable improved safety and economy of operations of the reactor fleet. Results from a collaboration between these two efforts, aimed at advancing the understanding of multiphase closures for pressurized water reactor (PWR) application, are presented. Particular attention is paid to assessment and analysis of the different physical models implemented in the CFD tools respectively used in the NEPTUNE and the CASL programs, for application to turbulent two-phase bubbly flows. The experiments conducted by Liu and Bankoff (Liu, 1989; Liu and Bankoff, 1993a,b) are selected for benchmarking, and predictions from NEPTUNE_CFD and STAR-CCM+ codes are presented for a broad range of flow conditions and with void fractions varying between 0 and 50%. Comparison of the CFD simulations and experimental measurements reveals that a similar level of accuracy is achieved in the two codes. The differences in both sets of closure models are analyzed, and their capability to capture the main features of the flow over a wide range of experimental conditions are discussed. Finally, a parametric sensitivity study for the set of closures used in STAR-CCM+ is included to serve as a preview of how uncertainty quantification methods can provide insights into interactions between closures of different phenomena. In conclusion, it is seen that, the multi-CFD-code approach and uncertainty analysis of a set of closures in a particular CFD code, are both of great value in assessing the limitations and the level of maturity of multiphase hydrodynamic closures, and can serve as aids in further improving them.
The barotropic
β-plane vorticity equation is considered under steady large scale (double-gyre) and small scale (stochastic) forcing. For both forcings, regimes are found in which alternating zonal ...jets appear. For steady large scale forcing, this regime is characterized by weak forcing and weak dissipation. Attention is focused on energy cascades due to the nonlinear and
β terms and the jets are found to be associated with to a near compensation in these cascades over a range of wavenumbers. Additionally, interaction between flow forced at large scale and flow forced at small scale is examined.
The dynamics of an idealized wind-driven double-gyre circulation in an ocean basin are studied from a dynamical systems point of view in an effort to better understand its variability. While previous ...analyses of this circulation have mostly dealt with local bifurcations of steady states and limit cycles, this study demonstrates the importance of considering global bifurcations as well.
This paper presents analytical and numerical results for a class of turbulence closure models called "alpha models," in which Lagrangian averaging and turbulence closure assumptions modify the ...Eulerian nonlinearity. The alpha models are investigated in the setting of the barotropic, double-gyre circulation in an ocean basin. Two variants of the alpha models for the barotropic vorticity (BV) equation are found to produce the correct four-gyre configuration for the mean barotropic circulation in numerical simulations performed at a resolution 4 times as coarse as that required in a resolved BV model. These are the BV-alpha model and the BV-Leray-alpha model. However, at a resolution 8 times as coarse, only the BV-alpha model produces the proper four-gyre configuration. Thus, the combination of modified nonlinearity and viscous dissipation (the viscosity is the same in all of the runs) in the BV-alpha model is found to provide a promising approach to modeling the mean effects of unresolved mesoscale (subgrid scale) activity in this problem. PUBLICATION ABSTRACT
We demonstrate the use of a probabilistic machine learning technique to develop stochastic parameterizations of atmospheric column-physics. After suitable preprocessing of NASA's Modern-Era ...Retrospective analysis for Research and Applications, version 2 (MERRA2) data to minimize the effects of high-frequency, high-wavenumber component of MERRA2 estimate of vertical velocity, we use generative adversarial networks to learn the probability distribution of vertical profiles of diabatic sources conditioned on vertical profiles of temperature and humidity. This may be viewed as an improvement over previous similar but deterministic approaches that seek to alleviate both, shortcomings of human-designed physics parameterizations, and the computational demand of the "physics" step in climate models.
Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate ...climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.
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