scholarly journals Sea level rise and the warming of the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis

2005 ◽  
Vol 110 (C9) ◽  
Author(s):  
James A. Carton
Keyword(s):  
Data Assimilation ◽  
Sea Level Rise ◽  
Sea Level ◽  
Simple Ocean Data Assimilation ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation

On the Accuracy of the Simple Ocean Data Assimilation Analysis for Estimating Heat Budgets of the Near-Surface Arabian Sea and Bay of Bengal*

2005 ◽  
Vol 35 (3) ◽  
pp. 395-400 ◽  
Author(s):  
S S C. Shenoi ◽  
D. Shankar ◽  
S. R. Shetye
Keyword(s):  
Data Assimilation ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Heat Budget ◽  
Heat Content ◽  
Heat Fluxes ◽  
Simple Ocean Data Assimilation ◽  
Near Surface ◽  
Surface Heat Fluxes ◽  
Ocean Data Assimilation

Abstract The accuracy of data from the Simple Ocean Data Assimilation (SODA) model for estimating the heat budget of the upper ocean is tested in the Arabian Sea and the Bay of Bengal. SODA is able to reproduce the changes in heat content when they are forced more by the winds, as in wind-forced mixing, upwelling, and advection, but not when they are forced exclusively by surface heat fluxes, as in the warming before the summer monsoon.

Data assimilation of GRACE terrestrial water storage to improve sea level rise estimates and isolate anthropogenic influences

2021 ◽  
Author(s):  
Ann Scheliga ◽  
Manuela Girotto
Keyword(s):  
Data Assimilation ◽  
Sea Level Rise ◽  
Sea Level ◽  
Land Surface ◽  
Water Budget ◽  
Water Storage ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Land Hydrology

<p>Sea level rise (SLR) projections rely on the accurate and precise closure of Earth’s water budget. The Gravity Recovery and Climate Experiment (GRACE) mission has provided global-coverage observations of terrestrial water storage (TWS) anomalies that improve accounting of ice and land hydrology changes and how these changes contribute to sea level rise. The contribution of land hydrology TWS changes to sea level rise is much smaller and less certain than contributions from glacial melt and thermal expansion. Although land hydrology TWS plays a smaller role, it is still important to investigate to improve the precision of the overall global water budget. This study analyzes how data assimilation techniques improve estimates of the land hydrology contribution to sea level rise. To achieve this, three global TWS datasets were analyzed: (1) GRACE TWS observations alone, (2) TWS estimates from the model-only simulation using Catchment Land Surface Model, and (3) TWS estimates from a data assimilation product of (1) and (2). We compared the data assimilation product with the GRACE observations alone and the model-only simulation to isolate the contribution to sea level rise from anthropogenic activities. We assumed a balanced water budget between land hydrology and the ocean, thus changes in global TWS are considered equal and opposite to sea level rise contribution.  Over the period of 2003-2016, we found sea level rise contributions from each dataset of +0.35 mm SLR eq/yr for GRACE, -0.34 mm SLR eq/yr for model-only, and a +0.09 mm SLR eq/yr for DA (reported as the mean linear trend). Our results indicate that the model-only simulation is not capturing important hydrologic processes. These are likely anthropogenic driven, indicating direct anthropogenic and climate-driven TWS changes play a substantial role in TWS contribution to SLR.</p>

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.

Representation Error of Oceanic Observations for Data Assimilation

2008 ◽  
Vol 25 (6) ◽  
pp. 1004-1017 ◽  
Author(s):  
Peter R. Oke ◽  
Pavel Sakov
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Standard Technique ◽  
Original Data ◽  
Sea Level Anomalies ◽  
Ocean Data Assimilation ◽  
Representation Error ◽  
The Difference ◽  
Along Track ◽  
The Given

Abstract A simple approach to the estimation of representation error (RE) of sea level (η), temperature (T), and salinity (S) observations for ocean data assimilation is described. It is assumed that the main source of RE is due to unresolved processes and scales in the model. Therefore, RE is calculated as a function of model resolution. The methods described here exploit the availability of mapped sea level anomalies (mSLAs) and along-track sea level anomalies (atSLAs). The mSLA fields or atSLA observations are regarded as the true ocean state. Here, they are averaged according to the resolution of the model grid, and the averaged field is taken as a representation of the true state on the given grid. The difference between the original data and the averaged field is then regarded as the RE for η. Subsequently, the RE is projected for η over depth using a standard technique, giving an estimate of the RE for T and S. Examples of RE estimates for an intermediate- and high-resolution global grid are presented. It is found that there is significant spatial variability in the RE for η, T, and S, with values that are typically greater than or comparable to measurement error, particularly in regions of strong mesoscale variability.

scholarly journals Estimating the sources of global sea level rise with data assimilation techniques

2012 ◽  
Vol 110 (Supplement_1) ◽  
pp. 3692-3699 ◽  
Author(s):  
C. C. Hay ◽  
E. Morrow ◽  
R. E. Kopp ◽  
J. X. Mitrovica
Keyword(s):  
Data Assimilation ◽  
Sea Level Rise ◽  
Sea Level ◽  
Global Sea Level

scholarly journals Transports and budgets in a 1/4 ° global ocean reanalysis 1989–2010

Ocean Science ◽  
2012 ◽  
Vol 8 (3) ◽  
pp. 333-344 ◽  
Author(s):  
K. Haines ◽  
M. Valdivieso ◽  
H. Zuo ◽  
V. N. Stepanov
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Global Ocean ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation ◽  
Ocean Reanalyses ◽  
Atmospheric Observations ◽  
Atmospheric Heat ◽  
Regional Budgets

Abstract. Large-scale ocean transports of heat and freshwater have not been well monitored, and yet the regional budgets of these quantities are important to understanding the role of the oceans in climate and climate change. In contrast, atmospheric heat and freshwater transports are commonly assessed from atmospheric reanalysis products, despite the presence of non-conserving data assimilation based on the wealth of distributed atmospheric observations as constraints. The ability to carry out ocean reanalyses globally at eddy-permitting resolutions of 1/4 ° or better, along with new global ocean observation programs, now makes a similar approach viable for the ocean. In this paper we examine the budgets and transports within a global high resolution ocean model constrained by ocean data assimilation, and compare them with independent oceanic and atmospheric estimates.

scholarly journals Transports and budgets in a 1/4° global ocean reanalysis 1989–2010

2012 ◽  
Vol 9 (1) ◽  
pp. 261-290 ◽  
Author(s):  
K. Haines ◽  
M. Valdivieso ◽  
H. Zuo ◽  
V. N. Stepanov
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Ocean Model ◽  
Global Ocean ◽  
Ocean Reanalysis ◽  
Ocean Data Assimilation ◽  
Ocean Reanalyses ◽  
Atmospheric Observations ◽  
Atmospheric Heat ◽  
Regional Budgets

Abstract. Large scale ocean transports of heat and freshwater have not been well monitored, and yet the regional budgets of these quantities are vital to understanding the role of the oceans in climate and climate change. In contrast atmospheric heat and freshwater transports are commonly assessed from atmospheric reanalysis products, despite the presence of non-conserving data assimilation based on the wealth of distributed atmospheric observations as constraints. The ability to carry out ocean reanalyses globally at eddy permitting resolutions of 1/4° or better, along with new global ocean observation programs, now make a similar approach viable for the ocean. In this paper we examine the budgets and transports within a global high resolution ocean model constrained by ocean data assimilation, and compare them with independent ocean and atmospheric estimates.

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