scholarly journals ENSO in the Mid-Holocene according to CSM and HadCM3

2014 ◽  
Vol 27 (3) ◽  
pp. 1223-1242 ◽  
Author(s):  
William H. G. Roberts ◽  
David S. Battisti ◽  
Alexander W. Tudhope
Keyword(s):  
Wind Stress ◽  
Climate Models ◽  
Southern Oscillation ◽  
Spatiotemporal Variability ◽  
Bjerknes Feedback ◽  
Climate System Model ◽  
Ocean Atmosphere ◽  
The Mean ◽  
The Impact ◽  
Mean State

Abstract The offline linearized ocean–atmosphere model (LOAM), which was developed to quantify the impact of the climatological mean state on the variability of the El Niño–Southern Oscillation (ENSO), is used to illuminate why ENSO changed between the modern-day and early/mid-Holocene simulations in two climate modeling studies using the NCAR Climate System Model (CSM) and the Hadley Centre Coupled Model, version 3 (HadCM3). LOAM reproduces the spatiotemporal variability simulated by the climate models and shows both the reduction in the variance of ENSO and the changes in the spatial structure of the variance during the early/mid-Holocene. The mean state changes that are important in each model are different and, in both cases, are also different from those hypothesized to be important in the original papers describing these simulations. In the CSM simulations, the ENSO mode is stabilized by the mean cooling of the SST. This reduces atmospheric heating anomalies that in turn give smaller wind stress anomalies, thus weakening the Bjerknes feedback. Within the ocean, a change in the thermocline structure alters the spatial pattern of the variance, shifting the peak variance farther east, but does not reduce the overall amount of ENSO variance. In HadCM3, the ENSO mode is stabilized by a combination of a weaker thermocline and weakened horizontal surface currents. Both of these reduce the Bjerknes feedback by reducing the ocean’s SST response to wind stress forcing. This study demonstrates the importance of considering the combined effect of a mean state change on the coupled ocean–atmosphere system: conflicting and erroneous results are obtained for both models if only one model component is considered in isolation.

scholarly journals A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales

2021 ◽  
Vol 14 (1) ◽  
pp. 73-90
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Heat Fluxes ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experimental design for better evaluation of both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual timescales and facilitate model development. We used the Community Earth System Model version 1 as the base model and performed a suite of 3 d hindcasts initialized every day starting at 00:00 Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the central US, precipitation and diabatic heating associated with the Madden–Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño–Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment because these phenomena are either a result of interannual climate variability or only happen a few times in a given year (e.g., MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model's climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated with parameterized moist processes usually develop within a few days and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

scholarly journals A multi-year short-range hindcast experiment for evaluating climate model moist processes from diurnal to interannual time scales

2020 ◽  
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Time Scales ◽  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experiment procedure for better evaluating both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual time scales to facilitate model development. We use the Community Earth System Model version 1 as the based model and performed a suite of 3-day long hindcasts every day starting at 00Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the Central U.S., precipitation and diabatic heating associated with the Madden-Julian Oscillation propagation, and the response of moist processes to sea surface temperature anomalies associated with the El Niño-Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment design to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment. Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model’s climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated parameterized moist processes usually develop within a few days, and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

Successive Modulation of ENSO to the Future Greenhouse Warming

2008 ◽  
Vol 21 (1) ◽  
pp. 3-21 ◽  
Author(s):  
Soon-Il An ◽  
Jong-Seong Kug ◽  
Yoo-Geun Ham ◽  
In-Sik Kang
Keyword(s):  
General Circulation ◽  
Southern Oscillation ◽  
General Circulation Models ◽  
Tropical Pacific ◽  
Greenhouse Warming ◽  
Delayed Response ◽  
Subsurface Temperature ◽  
Climate System Model ◽  
The Mean ◽  
The Tropical Pacific

Abstract The multidecadal modulation of the El Niño–Southern Oscillation (ENSO) due to greenhouse warming has been analyzed herein by means of diagnostics of Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled general circulation models (CGCMs) and the eigenanalysis of a simplified version of an intermediate ENSO model. The response of the global-mean troposphere temperature to increasing greenhouse gases is more likely linear, while the amplitude and period of ENSO fluctuates in a multidecadal time scale. The climate system model outputs suggest that the multidecadal modulation of ENSO is related to the delayed response of the subsurface temperature in the tropical Pacific compared to the response time of the sea surface temperature (SST), which would lead a modulation of the vertical temperature gradient. Furthermore, an eigenanalysis considering only two parameters, the changes in the zonal contrast of the mean background SST and the changes in the vertical contrast between the mean surface and subsurface temperatures in the tropical Pacific, exhibits a good agreement with the CGCM outputs in terms of the multidecadal modulations of the ENSO amplitude and period. In particular, the change in the vertical contrast, that is, change in difference between the subsurface temperature and SST, turns out to be more influential on the ENSO modulation than changes in the mean SST itself.

scholarly journals Impact of Resolution on the Tropical Pacific Circulation in a Matrix of Coupled Models

2009 ◽  
Vol 22 (10) ◽  
pp. 2541-2556 ◽  
Author(s):  
Malcolm J. Roberts ◽  
A. Clayton ◽  
M.-E. Demory ◽  
J. Donners ◽  
P. L. Vidale ◽  
...  
Keyword(s):  
Coupled Model ◽  
Walker Circulation ◽  
Ocean Model ◽  
Tropical Pacific ◽  
Coupled Models ◽  
Model Stability ◽  
The Mean ◽  
The Impact ◽  
Mean State ◽  
The Tropical Pacific

Abstract Results are presented from a matrix of coupled model integrations, using atmosphere resolutions of 135 and 90 km, and ocean resolutions of 1° and 1/3°, to study the impact of resolution on simulated climate. The mean state of the tropical Pacific is found to be improved in the models with a higher ocean resolution. Such an improved mean state arises from the development of tropical instability waves, which are poorly resolved at low resolution; these waves reduce the equatorial cold tongue bias. The improved ocean state also allows for a better simulation of the atmospheric Walker circulation. Several sensitivity studies have been performed to further understand the processes involved in the different component models. Significantly decreasing the horizontal momentum dissipation in the coupled model with the lower-resolution ocean has benefits for the mean tropical Pacific climate, but decreases model stability. Increasing the momentum dissipation in the coupled model with the higher-resolution ocean degrades the simulation toward that of the lower-resolution ocean. These results suggest that enhanced ocean model resolution can have important benefits for the climatology of both the atmosphere and ocean components of the coupled model, and that some of these benefits may be achievable at lower ocean resolution, if the model formulation allows.

scholarly journals La Niña–like Mean-State Response to Global Warming and Potential Oceanic Roles

2017 ◽  
Vol 30 (11) ◽  
pp. 4207-4225 ◽  
Author(s):  
Tsubasa Kohyama ◽  
Dennis L. Hartmann ◽  
David S. Battisti
Keyword(s):  
Global Warming ◽  
Southern Oscillation ◽  
Forced Response ◽  
Equatorial Pacific ◽  
La Niña ◽  
The West ◽  
La Nina ◽  
The Mean ◽  
Mean State ◽  
Sst Gradient

Abstract The majority of the models that participated in phase 5 of the Coupled Model Intercomparison Project global warming experiments warm faster in the eastern equatorial Pacific Ocean than in the west. GFDL-ESM2M is an exception among the state-of-the-art global climate models in that the equatorial Pacific sea surface temperature (SST) in the west warms faster than in the east, and the Walker circulation strengthens in response to warming. This study shows that this “La Niña–like” trend simulated by GFDL-ESM2M could be a physically consistent response to warming, and that the forced response could have been detectable since the late twentieth century. Two additional models are examined: GFDL-ESM2G, which differs from GFDL-ESM2M only in the oceanic components, warms without a clear zonal SST gradient; and HadGEM2-CC exhibits a warming pattern that resembles the multimodel mean. A fundamental observed constraint between the amplitude of El Niño–Southern Oscillation (ENSO) and the mean-state zonal SST gradient is reproduced well by GFDL-ESM2M but not by the other two models, which display substantially weaker ENSO nonlinearity than is observed. Under this constraint, the weakening nonlinear ENSO amplitude in GFDL-ESM2M rectifies the mean state to be La Niña–like. GFDL-ESM2M exhibits more realistic equatorial thermal stratification than GFDL-ESM2G, which appears to be the most important difference for the ENSO nonlinearity. On longer time scales, the weaker polar amplification in GFDL-ESM2M may also explain the origin of the colder equatorial upwelling water, which could in turn weaken the ENSO amplitude.

scholarly journals Northern winter stratospheric temperature and ozone responses to ENSO inferred from an ensemble of Chemistry Climate Models

2009 ◽  
Vol 9 (22) ◽  
pp. 8935-8948 ◽  
Author(s):  
C. Cagnazzo ◽  
E. Manzini ◽  
N. Calvo ◽  
A. Douglass ◽  
H. Akiyoshi ◽  
...  
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Polar Vortex ◽  
Atmospheric Modeling ◽  
Residual Circulation ◽  
Model Simulations ◽  
Enso Events ◽  
Stratospheric Temperature ◽  
The Mean ◽  
Column Ozone

Abstract. The connection between the El Niño Southern Oscillation (ENSO) and the Northern polar stratosphere has been established from observations and atmospheric modeling. Here a systematic inter-comparison of the sensitivity of the modeled stratosphere to ENSO in Chemistry Climate Models (CCMs) is reported. This work uses results from a number of the CCMs included in the 2006 ozone assessment. In the lower stratosphere, the mean of all model simulations reports a warming of the polar vortex during strong ENSO events in February–March, consistent with but smaller than the estimate from satellite observations and ERA40 reanalysis. The anomalous warming is associated with an anomalous dynamical increase of column ozone north of 70° N that is accompanied by coherent column ozone decrease in the Tropics, in agreement with that deduced from the NIWA column ozone database, implying an increased residual circulation in the mean of all model simulations during ENSO. The spread in the model responses is partly due to the large internal stratospheric variability and it is shown that it crucially depends on the representation of the tropospheric ENSO teleconnection in the models.

scholarly journals On the Dynamical Mechanisms Governing El Niño–Southern Oscillation Irregularity

2018 ◽  
Vol 31 (20) ◽  
pp. 8401-8419 ◽  
Author(s):  
Judith Berner ◽  
Prashant D. Sardeshmukh ◽  
Hannah M. Christensen
Keyword(s):  
El Niño ◽  
Time Scale ◽  
Climate Models ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Short Time Scale ◽  
Time Step ◽  
Climate System Model

This study investigates the mechanisms by which short time-scale perturbations to atmospheric processes can affect El Niño–Southern Oscillation (ENSO) in climate models. To this end a control simulation of NCAR’s Community Climate System Model is compared to a simulation in which the model’s atmospheric diabatic tendencies are perturbed every time step using a Stochastically Perturbed Parameterized Tendencies (SPPT) scheme. The SPPT simulation compares better with ECMWF’s twentieth-century reanalysis in having lower interannual sea surface temperature (SST) variability and more irregular transitions between El Niño and La Niña states, as expressed by a broader, less peaked spectrum. Reduced-order linear inverse models (LIMs) derived from the 1-month lag covariances of selected tropical variables yield good representations of tropical interannual variability in the two simulations. In particular, the basic features of ENSO are captured by the LIM’s least damped oscillatory eigenmode. SPPT reduces the damping time scale of this eigenmode from 17 to 11 months, which is in better agreement with the 8 months obtained from reanalyses. This noise-induced stabilization is consistent with perturbations to the frequency of the ENSO eigenmode and explains the broadening of the SST spectrum (i.e., the greater ENSO irregularity). Although the improvement in ENSO shown here was achieved through stochastic physics parameterizations, it is possible that similar improvements could be realized through changes in deterministic parameterizations or higher numerical resolution. It is suggested that LIMs could provide useful insight into model sensitivities, uncertainties, and biases also in those cases.

Regional oceanic and biogeochemical modeling strategy :forced or coupled model ?

2021 ◽  
Author(s):  
Véra Oerder ◽  
Pierre-Amaël Auger ◽  
Joaquim Bento ◽  
Samuel Hormazabal
Keyword(s):  
High Resolution ◽  
Wind Stress ◽  
Coupled Model ◽  
Heat Fluxes ◽  
Atmospheric Conditions ◽  
Coupled Simulation ◽  
Biogeochemical Modeling ◽  
Ocean Atmosphere ◽  
The Impact ◽  
Oceanic Model

<p><span> Regional high resolution biogeochemical modeling studies generaly use an oceanic model forced by prescribed atmospheric conditions. The computational cost of such approach is far lower than using an high resolution ocean-atmosphere coupled model. However, forced oceanic models cannot represent adequately the atmospheric reponse to the oceanic mesoscale (~10-100km) structures and the impact on the oceanic dynamics.</span></p><p><span>To assess the bias introduce by the use of a forced model, we compare here a regional high resolution (1/12º) ocean-atmosphere coupled model with oceanic simulations forced by the outputs of the coupled simulation. Several classical forcing strategies are compared : bulk formulae, prescribed stress, prescribed heat fluxes with or without Sea Surface Temperature (SST) restoring term, .... We study the Chile Eastern Boundary Upwelling System, and the oceanic model includes a biogeochemical component,</span></p><p><span>The coupled model oceanic mesoscale impacts the atmosphere through surface current and SST anomalies. Surface currents mainly affect the wind stress while SST impacts both the wind stress and the heat fluxes. In the forced simulations, mesoscale structures generated by the model internal variability does not correspond to those of the coupled simulation. According to the forcing strategy, the atmospheric conditions are not modified by the forced model mesoscale, or the modifications are not realistic. The regional dynamics (coastal upwelling, mesoscale activity, …) is affected, with impact on the biogeochemical activity.</span></p><p> </p><p> </p><p><em>This work was supported by the FONDECYT project 3180472 (Chile), with computational support of the NLHPC from the Universidad de Chile, the HPC from the Pontificia Universidad Catolica de Valparaiso and the Irene HPC from the GENCI at the CEA (France).</em></p>

scholarly journals Influence of parallel computational uncertainty on simulations of the Coupled General Climate Model

2012 ◽  
Vol 5 (2) ◽  
pp. 313-319 ◽  
Author(s):  
Z. Song ◽  
F. Qiao ◽  
X. Lei ◽  
C. Wang
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Quality Standard ◽  
Ensemble Member ◽  
Climate System ◽  
Climate System Model ◽  
Climate Simulations ◽  
Computational Uncertainty ◽  
The Impact ◽  
General Climate

Abstract. This paper investigates the impact of the parallel computational uncertainty due to the round-off error on climate simulations using the Community Climate System Model Version 3 (CCSM3). A series of sensitivity experiments have been conducted and the analyses are focused on the Global and Nino3.4 average sea surface temperatures (SST). For the monthly time series, it is shown that the amplitude of the deviation induced by the parallel computational uncertainty is the same order as that of the climate system change. However, the ensemble mean method can reduce the influence and the ensemble member number of 15 is enough to ignore the uncertainty. For climatology, the influence can be ignored when the climatological mean is calculated by using more than 30-yr simulations. It is also found that the parallel computational uncertainty has no distinguishable effect on power spectrum analysis of climate variability such as ENSO. Finally, it is suggested that the influence of the parallel computational uncertainty on Coupled General Climate Models (CGCMs) can be a quality standard or a metric for developing CGCMs.

scholarly journals Progress towards a probabilistic Earth system model: examining the impact of stochasticity in the atmosphere and land component of EC-Earth v3.2

2019 ◽  
Vol 12 (7) ◽  
pp. 3099-3118 ◽  
Author(s):  
Kristian Strommen ◽  
Hannah M. Christensen ◽  
Dave MacLeod ◽  
Stephan Juricke ◽  
Tim N. Palmer
Keyword(s):  
Climate Model ◽  
Weather Forecasting ◽  
Southern Oscillation ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Beneficial Impact ◽  
The Impact ◽  
Mean State

Abstract. We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

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