A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model

2015 ◽  
Vol 46 (3-4) ◽  
pp. 711-727 ◽  
Author(s):  
Xinrong Wu ◽  
Guijun Han ◽  
Shaoqing Zhang ◽  
Zhengyu Liu
Keyword(s):  
Parameter Optimization ◽  
Coupled Model ◽  
Enso Predictability ◽  
Intermediate Coupled Model ◽  
The Impact

A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

2011 ◽  
Vol 24 (23) ◽  
pp. 6210-6226 ◽  
Author(s):  
S. Zhang
Keyword(s):  
Time Scales ◽  
Parameter Optimization ◽  
Time Scale ◽  
Model Simulation ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Long Time ◽  
The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

scholarly journals Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model

2012 ◽  
Vol 140 (12) ◽  
pp. 3956-3971 ◽  
Author(s):  
Xinrong Wu ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Anthony Rosati ◽  
Thomas L. Delworth ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Surface Temperature ◽  
Parameter Optimization ◽  
General Circulation ◽  
Coupled Model ◽  
Model Parameters ◽  
Estimation And Prediction ◽  
Dependent Parameter ◽  
The Impact ◽  
Mean Square Errors

Abstract Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

scholarly journals Impact of Optimal Observational Time Window on Parameter Optimization and Climate Prediction: Simulation with a Simple Climate Model

2016 ◽  
Author(s):  
A. A. Yuxin Zhao ◽  
B. B. Xiong Deng ◽  
C. C. Shuo Yang
Keyword(s):  
Parameter Optimization ◽  
Climate Model ◽  
Time Window ◽  
Coupled Model ◽  
Climate Prediction ◽  
Optimal Time ◽  
The Impact ◽  
Ocean Observations ◽  
Assimilation Time

Abstract. Usually, an optimal time window (OTW) centred at the assimilation time to collect measured data for an assimilation cycle, can greatly improve the CDA analysis skill. Here, with a simple coupled model, we study the impact of optimal OTWs on the quality of parameter optimization and climate prediction. Results show that the optimal OTWs of valid atmosphere or ocean observations exist for the parameter being estimated and incorporating the parameter optimization will do some impact on the optimal OTWs for the state estimation. And using the optimal OTWs can enhance the predictability both of the atmosphere and ocean.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Impact of air–sea coupling on the climate change signal over the Iberian Peninsula

2021 ◽  
Author(s):  
Alba de la Vara ◽  
William Cabos ◽  
Dmitry V. Sein ◽  
Claas Teichmann ◽  
Daniela Jacob
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Mediterranean Sea ◽  
Large Scale ◽  
Regional Distribution ◽  
Coupled Model ◽  
Climate Change Signal ◽  
The North ◽  
The Mediterranean ◽  
The Impact

AbstractIn this work we use a regional atmosphere–ocean coupled model (RAOCM) and its stand-alone atmospheric component to gain insight into the impact of atmosphere–ocean coupling on the climate change signal over the Iberian Peninsula (IP). The IP climate is influenced by both the Atlantic Ocean and the Mediterranean sea. Complex interactions with the orography take place there and high-resolution models are required to realistically reproduce its current and future climate. We find that under the RCP8.5 scenario, the generalized 2-m air temperature (T2M) increase by the end of the twenty-first century (2070–2099) in the atmospheric-only simulation is tempered by the coupling. The impact of coupling is specially seen in summer, when the warming is stronger. Precipitation shows regionally-dependent changes in winter, whilst a drier climate is found in summer. The coupling generally reduces the magnitude of the changes. Differences in T2M and precipitation between the coupled and uncoupled simulations are caused by changes in the Atlantic large-scale circulation and in the Mediterranean Sea. Additionally, the differences in projected changes of T2M and precipitation with the RAOCM under the RCP8.5 and RCP4.5 scenarios are tackled. Results show that in winter and summer T2M increases less and precipitation changes are of a smaller magnitude with the RCP4.5. Whilst in summer changes present a similar regional distribution in both runs, in winter there are some differences in the NW of the IP due to differences in the North Atlantic circulation. The differences in the climate change signal from the RAOCM and the driving Global Coupled Model show that regionalization has an effect in terms of higher resolution over the land and ocean.

scholarly journals Evaluation of a Coupled Model to Predict the Impact of Adaptive Behaviour in the Thermal Sensation of Occupants of Naturally Ventilated Buildings in Warm-Humid Regions

2020 ◽  
Vol 13 (1) ◽  
pp. 255
Author(s):  
Luciano C. de Faria ◽  
Marcelo A. Romero ◽  
Lúcia F. S. Pirró
Keyword(s):  
Computational Models ◽  
Thermal Sensation ◽  
Coupled Model ◽  
Adaptive Behaviour ◽  
Humid Climate ◽  
Clothing Insulation ◽  
Tropical Regions ◽  
Naturally Ventilated Buildings ◽  
Adaptive Behaviours ◽  
The Impact

Improving indoor environment quality and making urban centres in tropical regions more sustainable has become a challenge for which computational models for the prediction of thermal sensation for naturally ventilated buildings (NVBs) have major role to play. This work performed analysis on thermal sensation for non-residential NVBs located in Brazilian tropical warm-humid climate and tested the effectiveness of suggested adaptive behaviours to mitigate warm thermal sensation. The research method utilized transient computational fluid dynamics models coupled with a dynamic model for human thermophysiology to predict thermal sensation. The calculated results were validated with comparison with benchmark values from questionnaires and from field measurements. The calculated results for dynamic thermal sensation (DTS) seven-point scale showed higher agreement with the thermal sensation vote than with the predicted mean vote. The test for the suggested adaptive behaviours considered reducing clothing insulation values from 0.18 to 0.32 clo (reducing DTS from 0.1 to 0.9), increasing the air speed in 0.9 m/s (reducing DTS from 0.1 to 0.9), and applying both suggestions together (reducing DTS from 0.1 to 1.3) for five scenarios with operative temperatures spanning 34.5–24.0 °C. Results quantified the tested adaptive behaviours’ efficiency showing applicability to improve thermal sensation from slightly-warm to neutral.

scholarly journals Local Coupled Equatorial Variability versus Remote ENSO Forcing in an Intermediate Coupled Model of the Tropical Atlantic

2006 ◽  
Vol 19 (20) ◽  
pp. 5227-5252 ◽  
Author(s):  
Serena Illig ◽  
Boris Dewitte
Keyword(s):  
Mixed Layer ◽  
El Niño ◽  
Time Scale ◽  
El Nino ◽  
Coupled Model ◽  
Tropical Atlantic ◽  
Equatorial Atlantic ◽  
Layer Model ◽  
Intermediate Coupled Model ◽  
Mixed Layer Model

Abstract The relative roles played by the remote El Niño–Southern Oscillation (ENSO) forcing and the local air–sea interactions in the tropical Atlantic are investigated using an intermediate coupled model (ICM) of the tropical Atlantic. The oceanic component of the ICM consists of a six-baroclinic mode ocean model and a simple mixed layer model that has been validated from observations. The atmospheric component is a global atmospheric general circulation model developed at the University of California, Los Angeles (UCLA). In a forced context, the ICM realistically simulates both the sea surface temperature anomaly (SSTA) variability in the equatorial band, and the relaxation of the Atlantic northeast trade winds and the intensification of the equatorial westerlies in boreal spring that usually follows an El Niño event. The results of coupled experiments with or without Pacific ENSO forcing and with or without explicit air–sea interactions in the equatorial Atlantic indicate that the background energy in the equatorial Atlantic is provided by ENSO. However, the time scale of the variability and the magnitude of some peculiar events cannot be explained solely by ENSO remote forcing. It is demonstrated that the peak of SSTA variability in the 1–3-yr band as observed in the equatorial Atlantic is due to the local air–sea interactions and is not a linear response to ENSO. Seasonal phase locking in boreal summer is also the result of the local coupling. The analysis of the intrinsic sustainable modes indicates that the Atlantic El Niño is qualitatively a noise-driven stable system. Such a system can produce coherent interdecadal variability that is not forced by the Pacific or extraequatorial variability. It is shown that when a simple slab mixed layer model is embedded into the system to simulate the northern tropical Atlantic (NTA) SST variability, the warming over NTA following El Niño events have characteristics (location and peak phase) that depend on air–sea interaction in the equatorial Atlantic. In the model, the interaction between the equatorial mode and NTA can produce a dipolelike structure of the SSTA variability that evolves at a decadal time scale. The results herein illustrate the complexity of the tropical Atlantic ocean–atmosphere system, whose predictability jointly depends on ENSO and the connections between the Atlantic modes of variability.

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 Roles of initial ocean surface and subsurface states on successfully predicting 2006–2007 El Niño with an intermediate coupled model

Ocean Science ◽  
2015 ◽  
Vol 11 (1) ◽  
pp. 187-194 ◽  
Author(s):  
F. Zheng ◽  
J. Zhu
Keyword(s):  
El Niño ◽  
El Nino ◽  
Surface Condition ◽  
Coupled Model ◽  
Ocean Surface ◽  
Sea Surface ◽  
Initial Surface ◽  
El Niño Event ◽  
Successful Prediction ◽  
Intermediate Coupled Model

Abstract. The 2006–2007 El Niño event, an unusually weak event, was predicted by most models only after the warming in the eastern Pacific had commenced. In this study, on the basis of an El Niño prediction system, roles of the initial ocean surface and subsurface states on predicting the 2006–2007 El Niño event are investigated to determine conditions favorable for predicting El Niño growth and are isolated in three sets of hindcast experiments. The hindcast is initialized through assimilation of only the sea surface temperature (SST) observations to optimize the initial surface condition, only the sea level (SL) data to update the initial subsurface state, or both the SST and SL data. Results highlight that the hindcasts with three different initial states can all successfully predict the 2006–2007 El Niño event 1 year in advance and that the hindcast initialized by both the SST and SL data performs best. A comparison between the various sets of hindcast results further demonstrates that successful prediction is more significantly affected by the initial subsurface state than by the initial surface condition. The accurate initial surface state can trigger the easier prediction of the 2006–2007 El Niño, whereas a more reasonable initial subsurface state can contribute to improving the prediction in the growth of the warm event.

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