scholarly journals Next generation of Bluelink ocean reanalysis with multiscale data assimilation: BRAN2020

2021 ◽  
Vol 13 (12) ◽  
pp. 5663-5688
Author(s):  
Matthew A. Chamberlain ◽  
Peter R. Oke ◽  
Russell A. S. Fiedler ◽  
Helen M. Beggs ◽  
Gary B. Brassington ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
General Circulation ◽  
Mixed Layer Depth ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Multiscale Approach ◽  
Ocean Reanalysis

Abstract. BRAN2020 (2020 version of the Bluelink ReANalysis) is an ocean reanalysis that combines observations with an eddy-resolving, near-global ocean general circulation model to produce a four-dimensional estimate of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. There is a separation in the scales that are corrected in the two steps: the high-resolution step corrects the mesoscale dynamics in the same way as previous versions of BRAN, while the extra coarse step is effective at correcting biases that develop at large scales. The reanalysis currently spans January 1993 to December 2019 and assimilates observations of in situ temperature and salinity, as well as of satellite sea-level anomaly and sea surface temperature. BRAN2020 is planned to be updated to within months of real time after this initial release, until an updated version of BRAN is available. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with misfits between reanalysed and observed fields reduced by over 30 % for some variables, for subsurface temperature and salinity in particular. The BRAN2020 dataset is comprised of daily averaged fields of temperature, salinity, velocity, mixed-layer depth and sea level. Reanalysed fields realistically represent all of the major current systems within 75∘ S and 75∘ N, excluding processes relating to sea ice but including boundary currents, equatorial circulation, Southern Ocean variability and mesoscale eddies. BRAN2020 is publicly available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.

scholarly journals Next generation of Bluelink ocean reanalysis with multiscale data assimilation: BRAN2020

2021 ◽  
Author(s):  
Matthew A. Chamberlain ◽  
Peter R. Oke ◽  
Russell A. S. Fiedler ◽  
Helen M. Beggs ◽  
Gary B. Brassington ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
General Circulation ◽  
Mixed Layer Depth ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Multiscale Approach ◽  
Ocean Reanalysis

Abstract. BRAN2020 is an ocean reanalysis that combines ocean observations with an eddy-resolving, near-global ocean general circulation model, to produce four-dimensional estimates of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. The reanalysis spans January 1993 to December 2019, and assimilates observations of in situ temperature and salinity, as well as satellite sea-level anomaly and sea surface temperature. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with mis-fits between reanalysed and observed fields reduced by over 30 % for some variables. The BRAN2020 dataset is comprised of daily-averaged fields of temperature, salinity, velocity, mixed-layer depth, and sea-level. Reanalysed fields realistically represent all of the major current systems within 75° S and 75° N, excluding processes relating to sea ice, but including boundary currents, equatorial circulation, Southern Ocean variability, and mesoscale eddies. BRAN2020 is publicly-available at https://doi.org/10.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.

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.

Global Decadal Upper-Ocean Heat Content as Viewed in Nine Analyses

2008 ◽  
Vol 21 (22) ◽  
pp. 6015-6035 ◽  
Author(s):  
James A. Carton ◽  
Anthony Santorelli
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
General Circulation ◽  
Heat Content ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Global Ocean ◽  
Warm Bias ◽  
The North ◽  
Ocean General Circulation

Abstract This paper examines nine analyses of global ocean 0-/700-m temperature and heat content during the 43-yr period of warming, 1960–2002. Among the analyses are two that are independent of any numerical model, six that rely on sequential data assimilation, including an ocean general circulation model, and one that uses four-dimensional variational data assimilation (4DVAR), including an ocean general circulation model and its adjoint. Most analyses show gradual warming of the global ocean with an ensemble trend of 0.77 × 108 J m−2 (10 yr)−1 (=0.24 W m−2) as the result of rapid warming in the early 1970s and again beginning around 1990. One proposed explanation for these variations is the effect of volcanic eruptions in 1963 and 1982. Examination of this hypothesis suggests that while there is an oceanic signal, it is insufficient to explain the observed heat content variations. A second potential cause of decadal variations in global heat content is the uncorrelated contribution of heat content variations in individual ocean basins. The subtropical North Atlantic is warming at twice the global average, with accelerated warming in the 1960s and again beginning in the late 1980s and extending through the end of the record. The Barents Sea region of the Arctic Ocean and the Gulf of Mexico have also warmed, while the western subpolar North Atlantic has cooled. Heat content variability in the North Pacific differs significantly from the North Atlantic. There the spatial and temporal patterns are consistent with the decadal variability previously identified through observational and modeling studies examining SST and surface winds. In the Southern Hemisphere large heat content anomalies are evident, and while there is substantial disagreement among analyses on average the band of latitudes at 30°–60°S contribute significantly to the global warming trend. Thus, the uncorrelated contributions of heat content variations in the individual basins are a major contributor to global heat content variations. A third potential contributor to global heat content variations is the effect of time-dependent bias in the set of historical observations. This last possibility is examined by comparing the analyses to the unbiased salinity–temperature–depth dataset and finding a very substantial warm bias in all analyses in the 1970s relative to the latter decades. This warm bias may well explain the rapid increase in analysis heat content in the early 1970s, but not the more recent increase, which began in the early 1990s. Finally, this study provides information about the similarities and differences between analyses that are independent of a model and those that use sequential assimilation and 4DVAR. The comparisons provide considerable encouragement for the use of the sequential analyses for climate research despite the presence of erroneous variability (also present in the no-model analyses) resulting from instrument bias. The strengths and weaknesses of each analysis need to be considered for a given application.

scholarly journals FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model Intercomparison Project

2020 ◽  
Vol 37 (10) ◽  
pp. 1093-1101
Author(s):  
Yaqi Wang ◽  
Zipeng Yu ◽  
Pengfei Lin ◽  
Hailong Liu ◽  
Jiangbo Jin ◽  
...  
Keyword(s):  
Sea Level ◽  
Ocean Circulation ◽  
General Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Global Ocean ◽  
Model Intercomparison ◽  
Ocean Heat Uptake ◽  
Intercomparison Project ◽  
Global Mean

Abstract The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) is an endorsed Model Intercomparison Project in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The goal of FAFMIP is to investigate the spread in the atmosphere-ocean general circulation model projections of ocean climate change forced by increased CO2, including the uncertainties in the simulations of ocean heat uptake, global mean sea level rise due to ocean thermal expansion and dynamic sea level change due to ocean circulation and density changes. The FAFMIP experiments have already been conducted with the Flexible Global Ocean-Atmosphere-Land System Model, gridpoint version 3.0 (FGOALS-g3). The model datasets have been submitted to the Earth System Grid Federation (ESGF) node. Here, the details of the experiments, the output variables and some baseline results are presented. Compared with the preliminary results of other models, the evolutions of global mean variables can be reproduced well by FGOALS-g3. The simulations of spatial patterns are also consistent with those of other models in most regions except the North Atlantic and the Southern Ocean, indicating large uncertainties in the regional sea level projections of these two regions.

scholarly journals Parameter Estimation Using a Particle Method: Inferring Mixing Coefficients from Sea Level Observations

2007 ◽  
Vol 135 (3) ◽  
pp. 1006-1020 ◽  
Author(s):  
Femke C. Vossepoel ◽  
Peter Jan van Leeuwen
Keyword(s):  
Sea Level ◽  
General Circulation ◽  
Circulation Model ◽  
Particle Method ◽  
Ocean General Circulation Model ◽  
Global Ocean ◽  
High Complexity ◽  
Ensemble Size ◽  
Mixing Parameters ◽  
Mixing Coefficients

Abstract This paper presents a first attempt to estimate mixing parameters from sea level observations using a particle method based on importance sampling. The method is applied to an ensemble of 128 members of model simulations with a global ocean general circulation model of high complexity. Idealized twin experiments demonstrate that the method is able to accurately reconstruct mixing parameters from an observed mean sea level field when mixing is assumed to be spatially homogeneous. An experiment with inhomogeneous eddy coefficients fails because of the limited ensemble size. This is overcome by the introduction of local weighting, which is able to capture spatial variations in mixing qualitatively. As the sensitivity of sea level for variations in mixing is higher for low values of mixing coefficients, the method works relatively well in regions of low eddy activity.

scholarly journals Formulation of a new explicit tidal scheme in ocean general circulation model

2022 ◽  
Author(s):  
Jiangbo Jin ◽  
Run Guo ◽  
Minghua Zhang ◽  
Guangqing Zhou ◽  
Qingcun Zeng
Keyword(s):  
Sea Level ◽  
General Circulation ◽  
Thermohaline Circulation ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Ocean Model ◽  
Tidal Mixing ◽  
Global Ocean ◽  
The North ◽  
Tidal Constituents

Abstract. Tides play an important role in ocean energy transfer and mixing, and provide major energy for maintaining thermohaline circulation. This study proposes a new explicit tidal scheme and assesses its performance in a global ocean model. Instead of using empirical specifications of tidal amplitudes and frequencies, the new scheme directly uses the positions of the Moon and Sun in a global ocean model to incorporate tides. Compared with the traditional method that has specified tidal constituents, the new scheme can better simulate the diurnal and spatial characteristics of the tidal potential of spring and neap tides as well as the spatial patterns and magnitudes of major tidal constituents (K1 and M2). It significantly reduces the total errors of eight tidal constituents (with the exception of N2 and Q1) in the traditional explicit tidal scheme. Relative to the control simulation without tides, both the new and traditional tidal schemes can lead to better dynamic sea level (DSL) simulation in the North Atlantic, reducing significant negative biases in this region. The new tidal scheme also shows smaller positive bias than the traditional scheme in the Southern Ocean. The new scheme is suited to calculate regional distributions of sea level height in addition to tidal mixing.

scholarly journals Evaluation of a near-global eddy-resolving ocean model

2013 ◽  
Vol 6 (3) ◽  
pp. 591-615 ◽  
Author(s):  
P. R. Oke ◽  
D. A. Griffin ◽  
A. Schiller ◽  
R. J. Matear ◽  
R. Fiedler ◽  
...  
Keyword(s):  
Sea Level ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Mixed Layer Depth ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Ocean Model ◽  
Ocean Dynamics ◽  
Future Version ◽  
Volume Transports

Abstract. Analysis of the variability of the last 18 yr (1993–2012) of a 32 yr run of a new near-global, eddy-resolving ocean general circulation model coupled with biogeochemistry is presented. Comparisons between modelled and observed mean sea level (MSL), mixed layer depth (MLD), sea level anomaly (SLA), sea surface temperature (SST), and {\\chla} indicate that the model variability is realistic. We find some systematic errors in the modelled MLD, with the model generally deeper than observations, which results in errors in the {\\chla}, owing to the strong biophysical coupling. We evaluate several other metrics in the model, including the zonally averaged seasonal cycle of SST, meridional overturning, volume transports through key straits and passages, zonally averaged temperature and salinity, and El Niño-related SST indices. We find that the modelled seasonal cycle in SST is 0.5–1.5 °C weaker than observed; volume transports of the Antarctic Circumpolar Current, the East Australian Current, and Indonesian Throughflow are in good agreement with observational estimates; and the correlation between the modelled and observed NINO SST indices exceeds 0.91. Most aspects of the model circulation are realistic. We conclude that the model output is suitable for broader analysis to better understand upper ocean dynamics and ocean variability at mid- and low latitudes. The new model is intended to underpin a future version of Australia's operational short-range ocean forecasting system.

The Data Assimilation Research Testbed: Nonlinear Algorithms and Novel Applications for Community Ensemble Data Assimilation

2020 ◽  
Author(s):  
Jeffrey Anderson ◽  
Nancy Collins ◽  
Moha El Gharamti ◽  
Timothy Hoar ◽  
Kevin Raeder ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Prediction Models ◽  
Circulation Model ◽  
Global Ocean ◽  
Optimal Interpolation ◽  
Correlated Noise ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Novel Applications

<p>The Data Assimilation Research Testbed (DART) is a community facility for ensemble data assimilation developed and maintained by the National Center for Atmospheric Research (NCAR). DART provides ensemble data assimilation capabilities for NCAR community earth system models and many other prediction models. It is straightforward to add interfaces for new models and new observations to DART.</p><p>DART provides traditional ensemble data assimilation algorithms that implicitly assume Gaussianity and linearity. Traditional algorithms can still work when these assumptions are violated. However, it is possible to greatly improve results by extending ensemble algorithms to explicitly account for aspects of nonlinearity and non-Gaussianity. Two new algorithms have been added to DART. 1). Anamorphosis transforms variables to make the assimilation problem more linear and Gaussian before transforming posterior estimates back to the original model variables; 2). The marginal correction rank histogram filter (MCRHF) directly represents arbitrary non-Gaussian distributions. These methods are particularly valuable for data assimilation for bounded quantities like tracers or streamflow.</p><p>DART is being applied to a number of novel applications. Examples in the poster include 1). An eddy-resolving global ocean ensemble reanalysis with the POP ocean model and an ensemble optimal interpolation; 2). The WRF-Hydro/DART system now includes a multi-parametric ensemble, anamorphosis, and spatially-correlated noise for the forcing fields. 3). Results from the Carbon Monitoring System over Mountains using CLM5 to assimilate remotely-sensed observations (LAI, biomass, and SIF) for a field site in Colorado; 4). Assimilation of MODIS snow cover fraction and daily GRACE total water storage data and its impact on soil moisture using the DART/NOAH-MP system. 5). An ensemble atmospheric reanalysis using the CAM general circulation model.</p>

scholarly journals Evaluation of a near-global eddy-resolving ocean model

2012 ◽  
Vol 5 (4) ◽  
pp. 4305-4354 ◽  
Author(s):  
P. R. Oke ◽  
D. A. Griffin ◽  
A. Schiller ◽  
R. J. Matear ◽  
R. Fiedler ◽  
...  
Keyword(s):  
Sea Level ◽  
Chlorophyll A ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Mixed Layer Depth ◽  
Circulation Model ◽  
Ocean General Circulation Model ◽  
Ocean Model ◽  
Ocean Dynamics ◽  
Volume Transports

Abstract. Analysis of the variability in an 18-yr run of a near-global, eddy-resolving ocean general circulation model coupled with biogeochemistry is presented. Comparisons between modelled and observed mean sea level (MSL), mixed-layer depth (MLD), sea-level anomaly (SLA), sea-surface temperature (SST), and Chlorophyll a indicate that the model variability is realistic. We find some systematic errors in the modelled MLD, with the model generally deeper than observations, that results in errors in the Chlorophyll a, owing to the strong biophysical coupling. We evaluate several other metrics in the model, including the zonally-averaged seasonal cycle of SST, meridional overturning, volume transports through key Straits and passages, zonal averaged temperature and salinity, and El Nino-related SST indices. We find that the modelled seasonal cycle in SST is 0.5–1.5 °C weaker than observed; volume transports of the Antarctic Circumpolar Current, the East Australian Current, and Indonesian Throughflow are in good agreement with observational estimates; and the correlation between the modelled and observed NINO SST indices exceed 0.91. Most aspects of the model circulation are realistic. We conclude that the model output is suitable for broader analysis to better understand ocean dynamics and ocean variability.

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