Application of Coupled Bred Vectors to Seasonal-to-Interannual Forecasting and Ocean Data Assimilation

2009 ◽  
Vol 22 (11) ◽  
pp. 2850-2870 ◽  
Author(s):  
Shu-Chih Yang ◽  
Christian Keppenne ◽  
Michele Rienecker ◽  
Eugenia Kalnay
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
Model Errors ◽  
Background Error ◽  
Salinity Field ◽  
Ocean Data Assimilation ◽  
Bred Vectors ◽  
The Impact

Abstract Coupled bred vectors (BVs) generated from the NASA Global Modeling and Assimilation Office (GMAO) coupled general circulation model are designed to capture the uncertainties related to slowly varying coupled instabilities. Two applications of the BVs are investigated in this study. First, the coupled BVs are used as initial perturbations for ensemble-forecasting purposes. Results show that the seasonal-to-interannual variability forecast skill can be improved when the oceanic and atmospheric perturbations are initialized with coupled BVs. The impact is particularly significant when the forecasts are initialized from the cold phase of tropical Pacific SST (e.g., August and November), because at these times the early coupled model errors, not accounted for in the BVs, are small. Second, the structure of the BVs is applied to construct hybrid background error covariances carrying flow-dependent information for the ocean data assimilation. Results show that the accuracy of the ocean analyses is improved when Gaussian background covariances are supplemented with a term obtained from the BVs. The improvement is especially noticeable for the salinity field.

scholarly journals Using Temperature–Salinity Relations in a Global Ocean Implementation of a Multivariate Data Assimilation Scheme

2007 ◽  
Vol 135 (11) ◽  
pp. 3785-3807 ◽  
Author(s):  
A. Bellucci ◽  
S. Masina ◽  
P. DiPietro ◽  
A. Navarra
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Global Ocean ◽  
Special Focus ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
The Impact

Abstract In this paper results from the application of an ocean data assimilation (ODA) system, combining a multivariate reduced-order optimal interpolator (OI) scheme with a global ocean general circulation model (OGCM), are described. The present ODA system, designed to assimilate in situ temperature and salinity observations, has been used to produce ocean reanalyses for the 1962–2001 period. The impact of assimilating observed hydrographic data on the ocean mean state and temporal variability is evaluated. A special focus of this work is on the ODA system skill in reproducing a realistic ocean salinity state. Results from a hierarchy of different salinity reanalyses, using varying combinations of assimilated data and background error covariance structures, are described. The impact of the space and time resolution of the background error covariance parameterization on salinity is addressed.

Coupled Data Assimilation and Ensemble Initialization with Application to Multiyear ENSO Prediction

2019 ◽  
Vol 32 (4) ◽  
pp. 997-1024 ◽  
Author(s):  
Terence J. O’Kane ◽  
Paul A. Sandery ◽  
Didier P. Monselesan ◽  
Pavel Sakov ◽  
Matthew A. Chamberlain ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Circulation Model ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Tropical Ocean ◽  
Enso Prediction ◽  
Bred Vectors ◽  
Coupled Data Assimilation ◽  
The Impact

We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupled DA was employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting.

scholarly journals Strongly Coupled Data Assimilation Experiments with Linearized Ocean–Atmosphere Balance Relationships

2018 ◽  
Vol 146 (4) ◽  
pp. 1233-1257 ◽  
Author(s):  
Andrea Storto ◽  
Matthew J. Martin ◽  
Bruno Deremble ◽  
Simona Masina
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Circulation Model ◽  
Variational Data Assimilation ◽  
Global Ocean ◽  
Background Error ◽  
Near Surface ◽  
Strongly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact

Coupled data assimilation is emerging as a target approach for Earth system prediction and reanalysis systems. Coupled data assimilation may be indeed able to minimize unbalanced air–sea initialization and maximize the intermedium propagation of observations. Here, we use a simplified framework where a global ocean general circulation model (NEMO) is coupled to an atmospheric boundary layer model [Cheap Atmospheric Mixed Layer (CheapAML)], which includes prognostic prediction of near-surface air temperature and moisture and allows for thermodynamic but not dynamic air–sea coupling. The control vector of an ocean variational data assimilation system is augmented to include 2-m atmospheric parameters. Cross-medium balances are formulated either through statistical cross covariances from monthly anomalies or through the application of linearized air–sea flux relationships derived from the tangent linear approximation of bulk formulas, which represents a novel solution to the coupled assimilation problem. As a proof of concept, the methodology is first applied to study the impact of in situ ocean observing networks on the near-surface atmospheric analyses and later to the complementary study of the impact of 2-m air observations on sea surface parameters, to assess benefits of strongly versus weakly coupled data assimilation. Several forecast experiments have been conducted for the period from June to December 2011. We find that especially after day 2 of the forecasts, strongly coupled data assimilation provides a beneficial impact, particularly in the tropical oceans. In most areas, the use of linearized air–sea balances outperforms the statistical relationships used, providing a motivation for implementing coupled tangent linear trajectories in four-dimensional variational data assimilation systems. Further impacts of strongly coupled data assimilation might be found by retuning the background error covariances.

scholarly journals Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

2017 ◽  
Vol 24 (4) ◽  
pp. 681-694 ◽  
Author(s):  
Yuxin Zhao ◽  
Xiong Deng ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Chang Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Climate Model ◽  
Time Window ◽  
Coupled Model ◽  
Circulation Model ◽  
Scale Interactions ◽  
Climate Signals ◽  
Coupled Data Assimilation ◽  
Climate Analysis

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

scholarly journals The Effect of a Well-Resolved Stratosphere on Surface Climate: Differences between CMIP5 Simulations with High and Low Top Versions of the Met Office Climate Model

2012 ◽  
Vol 25 (20) ◽  
pp. 7083-7099 ◽  
Author(s):  
S. C. Hardiman ◽  
N. Butchart ◽  
T. J. Hinton ◽  
S. M. Osprey ◽  
L. J. Gray
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Circulation Model ◽  
Climate Simulation ◽  
Reanalysis Dataset ◽  
Quasi Biennial Oscillation ◽  
Surface Climate ◽  
The Impact

Abstract The importance of using a general circulation model that includes a well-resolved stratosphere for climate simulations, and particularly the influence this has on surface climate, is investigated. High top model simulations are run with the Met Office Unified Model for the Coupled Model Intercomparison Project Phase 5 (CMIP5). These simulations are compared to equivalent simulations run using a low top model differing only in vertical extent and vertical resolution above 15 km. The period 1960–2002 is analyzed and compared to observations and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset. Long-term climatology, variability, and trends in surface temperature and sea ice, along with the variability of the annular mode index, are found to be insensitive to the addition of a well-resolved stratosphere. The inclusion of a well-resolved stratosphere, however, does improve the impact of atmospheric teleconnections on surface climate, in particular the response to El Niño–Southern Oscillation, the quasi-biennial oscillation, and midwinter stratospheric sudden warmings (i.e., zonal mean wind reversals in the middle stratosphere). Thus, including a well-represented stratosphere could improve climate simulation on intraseasonal to interannual time scales.

A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA)

2008 ◽  
Vol 136 (8) ◽  
pp. 2999-3017 ◽  
Author(s):  
James A. Carton ◽  
Benjamin S. Giese
Keyword(s):  
Data Assimilation ◽  
Interannual Variability ◽  
General Circulation ◽  
Heat Content ◽  
Circulation Model ◽  
Surface Boundary ◽  
Model Forecast ◽  
Simple Ocean Data Assimilation ◽  
Ocean Climate ◽  
Ocean Data Assimilation

Abstract This paper describes the Simple Ocean Data Assimilation (SODA) reanalysis of ocean climate variability. In the assimilation, a model forecast produced by an ocean general circulation model with an average resolution of 0.25° × 0.4° × 40 levels is continuously corrected by contemporaneous observations with corrections estimated every 10 days. The basic reanalysis, SODA 1.4.2, spans the 44-yr period from 1958 to 2001, which complements the span of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA-40). The observation set for this experiment includes the historical archive of hydrographic profiles supplemented by ship intake measurements, moored hydrographic observations, and remotely sensed SST. A parallel run, SODA 1.4.0, is forced with identical surface boundary conditions, but without data assimilation. The new reanalysis represents a significant improvement over a previously published version of the SODA algorithm. In particular, eddy kinetic energy and sea level variability are much larger than in previous versions and are more similar to estimates from independent observations. One issue addressed in this paper is the relative importance of the model forecast versus the observations for the analysis. The results show that at near-annual frequencies the forecast model has a strong influence, whereas at decadal frequencies the observations become increasingly dominant in the analysis. As a consequence, interannual variability in SODA 1.4.2 closely resembles interannual variability in SODA 1.4.0. However, decadal anomalies of the 0–700-m heat content from SODA 1.4.2 more closely resemble heat content anomalies based on observations.

Impact of Accurate Geoid Fields on Estimates of the Ocean Circulation

2007 ◽  
Vol 24 (8) ◽  
pp. 1464-1478 ◽  
Author(s):  
Detlef Stammer ◽  
Armin Köhl ◽  
Carl Wunsch
Keyword(s):  
Ocean Circulation ◽  
General Circulation ◽  
Circulation Model ◽  
Formal Proof ◽  
Meridional Overturning Circulation ◽  
Dynamic Topography ◽  
Model Errors ◽  
Geoid Model ◽  
Meridional Heat Transport ◽  
The Impact

Abstract The impact of new geoid height models on estimates of the ocean circulation, now available from the Gravity Recovery and Climate Experiment (GRACE) spacecraft, is assessed, and the implications of far more accurate geoids, anticipated from the European Space Agency’s (ESA) Gravity and Ocean Circulation Explorer (GOCE) mission, are explored. The study is based on several circulation estimates obtained over the period 1992–2002 by combining most of the available ocean datasets with a global general circulation model on a 1° horizontal grid and by exchanging only the EGM96 geoid model with two different geoid models available from GRACE. As compared to the EGM96-based solution, the GRACE geoid leads to an estimate of the ocean circulation that is more consistent with the Levitus temperature and salinity climatology. While not a formal proof, this finding supports the inference of a substantially improved GRACE geoid skill. However, oceanographic implications of the GRACE model are only modest compared to what can be obtained from ocean observations alone. To understand the extent to which this is merely a consequence of a not-optimally converged solution or if a much more accurate geoid field could in principle play a profound role in the ocean estimation procedure, an additional experiment was performed in which the geoid error was artificially reduced relative to all other datasets. Adjustments occur then in all elements of the ocean circulation, including 10% changes in the meridional overturning circulation and the corresponding meridional heat transport in the Atlantic. For an optimal use of new geoid fields, improved error information is required. The error budget of existing time-mean dynamic topography estimates may now be dominated by residual errors in time-mean altimetric corrections. Both these and the model errors need to be better understood before improved geoid estimates can be fully exploited. As is commonly found, the Southern Ocean is of particular concern.

scholarly journals Assessing a New Coupled Data Assimilation System Based on the Met Office Coupled Atmosphere–Land–Ocean–Sea Ice Model

2015 ◽  
Vol 143 (11) ◽  
pp. 4678-4694 ◽  
Author(s):  
D. J. Lea ◽  
I. Mirouze ◽  
M. J. Martin ◽  
R. R. King ◽  
A. Hines ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Coupled Model ◽  
Background Information ◽  
Ocean Data Assimilation ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A new coupled data assimilation (DA) system developed with the aim of improving the initialization of coupled forecasts for various time ranges from short range out to seasonal is introduced. The implementation here is based on a “weakly” coupled data assimilation approach whereby the coupled model is used to provide background information for separate ocean–sea ice and atmosphere–land analyses. The increments generated from these separate analyses are then added back into the coupled model. This is different from the existing Met Office system for initializing coupled forecasts, which uses ocean and atmosphere analyses that have been generated independently using the FOAM ocean data assimilation system and NWP atmosphere assimilation systems, respectively. A set of trials has been run to investigate the impact of the weakly coupled data assimilation on the analysis, and on the coupled forecast skill out to 5–10 days. The analyses and forecasts have been assessed by comparing them to observations and by examining differences in the model fields. Encouragingly for this new system, both ocean and atmospheric assessments show the analyses and coupled forecasts produced using coupled DA to be very similar to those produced using separate ocean–atmosphere data assimilation. This work has the benefit of highlighting some aspects on which to focus to improve the coupled DA results. In particular, improving the modeling and data assimilation of the diurnal SST variation and the river runoff should be examined.

scholarly journals A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

2021 ◽  
Author(s):  
Zhao Liu ◽  
Shaoqing Zhang ◽  
Yang Shen ◽  
Yuping Guan ◽  
Xiong Deng
Keyword(s):  
Parameter Estimation ◽  
General Circulation ◽  
Multiple Equilibria ◽  
Coupled Model ◽  
Circulation Model ◽  
Meridional Overturning Circulation ◽  
Model Parameters ◽  
Model Errors ◽  
Model Study ◽  
Regime Transitions

Abstract. The multiple equilibria are an outstanding characteristic of the Atlantic meridional overturning circulation (AMOC) that has important impacts on the Earth climate system appearing as regime transitions. The AMOC can be simulated in different models but the behavior deviates from the real world due to the existence of model errors. Here, we first combine a general AMOC model with an ensemble Kalman filter to form an ensemble coupled model data assimilation and parameter estimation (CDAPE) system, and derive the general methodology to capture the observed AMOC regime transitions through utilization of observational information. Then we apply this methodology designed within a twin experiment framework with a simple conceptual model that simulates the transition phenomenon of AMOC multiple equilibria, as well as a more physics-based MOC box model to reconstruct the observed AMOC multiple equilibria. The results show that the coupled model parameter estimation with observations can significantly mitigate the model deviations, thus capturing regime transitions of the AMOC. This simple model study serves as a guideline when a coupled general circulation model is used to incorporate observations to reconstruct the AMOC historical states and make multi-decadal climate predictions.

scholarly journals The Role of Air-Sea Coupling on November-April Intraseasonal Rainfall Variability Over the South Pacific

2021 ◽  
Author(s):  
Sunil Kumar Pariyar ◽  
Noel Keenlyside ◽  
Wan-Ling Tseng ◽  
Huang Hsiung Hsu ◽  
Ben-jei Tsuang
Keyword(s):  
General Circulation ◽  
Rainfall Variability ◽  
Phase Relationship ◽  
Coupled Model ◽  
Circulation Model ◽  
South Pacific ◽  
Ocean Model ◽  
The South ◽  
The Impact

Abstract We investigate the impact of resolving air-sea interaction on the simulation of the intraseasonal rainfall variability over the South Pacific using the ECHAM5 atmospheric general circulation model coupled with the Snow-Ice-Thermocline (SIT) ocean model. We compare the fully coupled simulation with two uncoupled ECHAM5 simulations, one forced with sea surface temperature (SST) climatology and one forced with daily SST from the coupled model. The intraseasonal rainfall variability over the South Pacific is reduced by 17% in the uncoupled model forced with SST climatology and increased by 8% in the uncoupled simulation forced with daily SST, suggesting the role of air-sea coupling and SST variability. The coupled model best simulates the key characteristics of two intraseasonal rainfall modes over the South Pacific with reasonable propagation and correct periodicity. The spatial structure of the two rainfall modes in all three simulations is very similar, suggesting these modes are primarily generated by the dynamics of the atmosphere. The southeastward propagation of rainfall anomalies associated with two leading rainfall modes in the South Pacific depends upon the eastward propagating MJO signals over the Indian Ocean and western Pacific. Air-sea interaction seems crucial for such propagation as both eastward and southeastward propagations are substantially reduced in the uncoupled model forced with SST climatology. The simulation of both eastward and southeastward propagations improved considerably in the uncoupled model forced with daily SST; however, the periodicity differs from the coupled model. Such discrepancy in the periodicity is attributed to the changes in the SST-rainfall relationship with weaker correlations and the nearly in-phase relationship.

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