73 research outputs found
Radiative effects of clouds and water vapor on an axisymmetric monsoon
Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement 794063 and the UK Natural Environment Research Council’s Grant NE/R000727/1.Monsoons are summertime circulations shaping climates and societies across the tropics and subtropics. Here the radiative effects controlling an axisymmetric monsoon and its response to climate change are investigated using aquaplanet simulations. The influences of clouds, water vapor, and CO2 on the axisymmetric monsoon are decomposed using the radiation-locking technique. Seasonal variations in clouds and water vapor strongly modulate the axisymmetric monsoon, reducing net precipitation by approximately half. Warming and moistening of the axisymmetric monsoon by seasonal longwave cloud and water vapor effects are counteracted by a strong shortwave cloud effect. The shortwave cloud effect also expedites onset of the axisymmetric monsoon by approximately two weeks, whereas longwave cloud and water vapor effects delay onset. A conceptual model relates the timing of monsoon onset to the efficiency of surface cooling. In climate change simulations CO2 forcing and the water vapor feedback have similar influences on the axisymmetric monsoon, warming the surface and moistening the region. In contrast, clouds have a negligible effect on surface temperature yet dominate the monsoon circulation response. A new perspective for understanding how cloud radiative effects shape the monsoon circulation response to climate change is introduced. The radiation-locking simulations and analyses advance understanding of how radiative processes influence an axisymmetric monsoon, and establish a framework for interpreting monsoon–radiation coupling in observations, in state-of-the-art models, and in different climate states.Publisher PDFPeer reviewe
A data-driven framework for dimensionality reduction and causal inference in climate fields
We propose a data-driven framework to simplify the description of
spatiotemporal climate variability into few entities and their causal linkages.
Given a high-dimensional climate field, the methodology first reduces its
dimensionality into a set of regionally constrained patterns. Time-dependent
causal links are then inferred in the interventional sense through the
fluctuation-response formalism, as shown in Baldovin et al. (2020). These two
steps allow to explore how regional climate variability can influence remote
locations. To distinguish between true and spurious responses, we propose a
novel analytical null model for the fluctuation-dissipation relation, therefore
allowing for uncertainty estimation at a given confidence level. Finally, we
select a set of metrics to summarize the results, offering a useful and
simplified approach to explore climate dynamics. We showcase the methodology on
the monthly sea surface temperature field at global scale. We demonstrate the
usefulness of the proposed framework by studying few individual links as well
as "link maps", visualizing the cumulative degree of causation between a given
region and the whole system. Finally, each pattern is ranked in terms of its
"causal strength", quantifying its relative ability to influence the system's
dynamics. We argue that the methodology allows to explore and characterize
causal relationships in high-dimensional spatiotemporal fields in a rigorous
and interpretable way
Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency
The Ocean Heat Uptake Efficiency (OHUE) quantifies the ocean's ability to
mitigate surface warming through deep heat sequestration. Despite its
importance, the main controls on OHUE, as well as its nearly two-fold spread
across contemporary climate models, remain unclear. We argue that OHUE is
primarily controlled by the strength of mid-latitude ventilation in the
background climate, itself related to subtropical pycnocline depth and ocean
stratification. This hypothesis is supported by a strong correlation between
OHUE and pycnocline depth in the CMIP5 and CMIP6 under RCP85/SSP585, as well as
in MITgcm. We explain these results through a regional OHUE decomposition,
showing that the mid-latitudes largely account for both: (1) global heat uptake
after increased radiative forcing and; (2) the correlation between pycnocline
depth and OHUE. Coupled with the nearly equivalent inter-model spreads in
OHUE/pycnocline depth, these results imply that mid-latitude ventilation also
dominates the ensemble spread in OHUE. Our results provide a pathway towards
observationally constraining OHUE, and thus future climate
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
Subgrid parameterizations of mesoscale eddies continue to be in demand for
climate simulations. These subgrid parameterizations can be powerfully designed
using physics and/or data-driven methods, with uncertainty quantification. For
example, Guillaumin and Zanna (2021) proposed a Machine Learning (ML) model
that predicts subgrid forcing and its local uncertainty. The major assumption
and potential drawback of this model is the statistical independence of
stochastic residuals between grid points. Here, we aim to improve the
simulation of stochastic forcing with generative models of ML, such as
Generative adversarial network (GAN) and Variational autoencoder (VAE).
Generative models learn the distribution of subgrid forcing conditioned on the
resolved flow directly from data and they can produce new samples from this
distribution. Generative models can potentially capture not only the spatial
correlation but any statistically significant property of subgrid forcing. We
test the proposed stochastic parameterizations offline and online in an
idealized ocean model. We show that generative models are able to predict
subgrid forcing and its uncertainty with spatially correlated stochastic
forcing. Online simulations for a range of resolutions demonstrated that
generative models are superior to the baseline ML model at the coarsest
resolution
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
Integration of machine learning (ML) models of unresolved dynamics into
numerical simulations of fluid dynamics has been demonstrated to improve the
accuracy of coarse resolution simulations. However, when trained in a purely
offline mode, integrating ML models into the numerical scheme can lead to
instabilities. In the context of a 2D, quasi-geostrophic turbulent system, we
demonstrate that including an additional network in the loss function, which
emulates the state of the system into the future, produces offline-trained ML
models that capture important subgrid processes, with improved stability
properties.Comment: Accepted after peer-review at the 1st workshop on Synergy of
Scientific and Machine Learning Modeling, SynS & ML ICML, Honolulu, Hawaii,
USA. July, 202
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Optimal Excitation of Interannual Atlantic Meridional Overturning Circulation Variability
The optimal excitation of Atlantic meridional overturning circulation (MOC) anomalies is investigated in an ocean general circulation model with an idealized configuration. The optimal three-dimensional spatial structure of temperature and salinity perturbations, defined as the leading singular vector and generating the maximum amplification of MOC anomalies, is evaluated by solving a generalized eigenvalue problem using tangent linear and adjoint models. Despite the stable linearized dynamics, a large amplification of MOC anomalies, mostly due to the interference of nonnormal modes, is initiated by the optimal perturbations. The largest amplification of MOC anomalies, found to be excited by high-latitude deep density perturbations in the northern part of the basin, is achieved after about 7.5 years. The anomalies grow as a result of a conversion of mean available potential energy into potential and kinetic energy of the perturbations, reminiscent of baroclinic instability. The time scale of growth of MOC anomalies can be understood by examining the time evolution of deep zonal density gradients, which are related to the MOC via the thermal wind relation. The velocity of propagation of the density anomalies, found to depend on the horizontal component of the mean flow velocity and the mean density gradient, determines the growth time scale of the MOC anomalies and therefore provides an upper bound on the MOC predictability time. The results suggest that the nonnormal linearized ocean dynamics can give rise to enhanced MOC variability if, for instance, overflows, eddies, and/or deep convection can excite high-latitude density anomalies in the ocean interior with a structure resembling that of the optimal perturbations found in this study. The findings also indicate that errors in ocean initial conditions or in model parameterizations or processes, particularly at depth, may significantly reduce the Atlantic MOC predictability time to less than a decade.Earth and Planetary Science
Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks
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. One such component is the eddy diffusivity model, where
vertical turbulent fluxes of a quantity are parameterized from a variable eddy
diffusion coefficient and the mean vertical gradient of the quantity. In this
work, we improve a parameterization of vertical mixing in the ocean surface
boundary layer by enhancing its eddy diffusivity model using data-driven
methods, specifically neural networks. The neural networks are designed to take
extrinsic and intrinsic forcing parameters as input to predict the eddy
diffusivity profile and are trained using output data from a second moment
closure turbulent mixing scheme. The modified vertical mixing scheme predicts
the eddy diffusivity profile through online inference of neural networks and
maintains the conservation principles of the standard ocean model equations,
which is particularly important for its targeted use in climate simulations. We
describe the development and stable implementation of neural networks in an
ocean general circulation model and demonstrate that the enhanced scheme
outperforms its predecessor by reducing biases in the mixed-layer depth and
upper ocean stratification. Our results demonstrate the potential for
data-driven physics-aware parameterizations to improve global climate models
Deep learning of systematic sea ice model errors from data assimilation increments
Data assimilation is often viewed as a framework for correcting short-term
error growth in dynamical climate model forecasts. When viewed on the time
scales of climate however, these short-term corrections, or analysis
increments, can closely mirror the systematic bias patterns of the dynamical
model. In this study, we use convolutional neural networks (CNNs) to learn a
mapping from model state variables to analysis increments, in order to showcase
the feasibility of a data-driven model parameterization which can predict
state-dependent model errors. We undertake this problem using an ice-ocean data
assimilation system within the Seamless system for Prediction and EArth system
Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory,
which assimilates satellite observations of sea ice concentration every 5 days
between 1982--2017. The CNN then takes inputs of data assimilation forecast
states and tendencies, and makes predictions of the corresponding sea ice
concentration increments. Specifically, the inputs are states and tendencies of
sea ice concentration, sea-surface temperature, ice velocities, ice thickness,
net shortwave radiation, ice-surface skin temperature, sea-surface salinity, as
well as a land-sea mask. We find the CNN is able to make skillful predictions
of the increments in both the Arctic and Antarctic and across all seasons, with
skill that consistently exceeds that of a climatological increment prediction.
This suggests that the CNN could be used to reduce sea ice biases in
free-running SPEAR simulations, either as a sea ice parameterization or an
online bias correction tool for numerical sea ice forecasts.Comment: 38 pages, 8 figures, 10 supplementary figure
Global reconstruction of historical ocean heat storage and transport
Most of the excess energy stored in the climate system due to anthropogenic
greenhouse gas emissions has been taken up by the oceans,
leading to thermal expansion and sea level rise. The oceans thus
have an important role in the Earth’s energy imbalance. Observational
constraints on future anthropogenic warming critically depend
on accurate estimates of past ocean heat content (OHC) change. We
present a novel reconstruction of OHC since 1871, with global coverage
of the full ocean depth. Our estimates combine timeseries of
observed sea surface temperatures, with much longer historical coverage
than those in the ocean interior, together with a representation
(a Green’s function) of time-independent ocean transport processes.
For 1955-2017, our estimates are comparable to direct estimates
made by infilling the available 3D time-dependent ocean temperature
observations. We find that the global ocean absorbed heat during
this period at a rate of 0.30 ± 0.06 W/m2
in the upper 2000 m and
0.028 ± 0.026 W/m2 below 2000 m, with large decadal fluctuations.
The total OHC change since 1871 is estimated at 436 ±91 × 1021 J,
with an increase during 1921-1946 (145 ± 62× 1021 J) that is as large
as during 1990-2015. By comparing with direct estimates, we also
infer that, during 1955-2017, up to half of the Atlantic Ocean warming
and thermosteric sea level rise at low-to-mid latitudes emerged due
to heat convergence from changes in ocean transport
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