scholarly journals Heterogeneous oceanic mass distribution in GRACE observations and its leakage effect

2020 ◽  
Vol 221 (1) ◽  
pp. 603-616
Author(s):  
Shuang Yi ◽  
Kosuke Heki
Keyword(s):  
Ocean Model ◽  
The Arctic ◽  
Global Ocean ◽  
The Sea Of Japan ◽  
Observation Data ◽  
Ocean Reanalysis ◽  
Ocean Mass ◽  
Grace Data ◽  
Leakage Effect ◽  
Gravity Recovery

SUMMARY Signal leakage between the land and ocean is a challenge in using Gravity Recovery and Climate Experiment (GRACE) observation data to study global mass redistributions. Although the leakage occurs in both directions, more attention has been paid to the land-to-ocean leakage and less to the ocean-to-land leakage. Here, we show that the ocean-to-land leakage is non-uniform and non-negligible and propose a new forward modelling method to fully consider bi-directional leakages with the help of the global Ocean ReAnalysis System ORAS5. This observation-driven model could significantly reduce the variations in ocean grids and thus decrease the ocean-to-land leakage. The results with different treatment of the ocean signal leakage are compared. We find that failing to consider the ocean-to-land leakage will cause an underestimation of ∼20 per cent in the seasonal variation and will introduce a bias of several giga-tons in the secular trend. Although the uniform and non-uniform model have similar results in the global average of seasonal mass variations, the non-uniform ocean model is necessary in most places, especially near the Arctic Ocean, the Sea of Japan and the Gulf of Carpentaria. Despite these achievements, we also point out that there is still much room for improvement in ocean mass models, particularly in long-term trends. Our results indicate the importance of the ocean-to-land leakage correction in the mass estimation in coastal land areas using the GRACE data.

scholarly journals Global ocean freshening, ocean mass increase and global mean sea level rise over 2005–2015

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
William Llovel ◽  
S. Purkey ◽  
B. Meyssignac ◽  
A. Blazquez ◽  
N. Kolodziejczyk ◽  
...  
Keyword(s):  
Sea Level ◽  
Global Ocean ◽  
Last Deglaciation ◽  
Mean Sea Level ◽  
Isostatic Adjustment ◽  
Ocean Mass ◽  
Grace Data ◽  
Global Mean Sea Level ◽  
Gravity Recovery ◽  
Global Mean

AbstractGlobal mean sea level has experienced an unabated rise over the 20th century. This observed rise is due to both ocean warming and increasing continental freshwater discharge. We estimate the net ocean mass contribution to sea level by assessing the global ocean salt budget based on the unprecedented amount of in situ data over 2005–2015. We obtain the ocean mass trends of 1.30 ± 1.13 mm · yr−1 (0–2000 m) and 1.55 ± 1.20 mm · yr−1 (full depth). These new ocean mass trends are smaller by 0.63–0.88 mm · yr−1 compared to the ocean mass trend estimated through the sea level budget approach. Our result provides an independent validation of Gravity Recovery And Climate Experiment (GRACE)-based ocean mass trend and, in addition, places an independent constraint on the combined Glacial Isostatic Adjustment – the Earth’s delayed viscoelastic response to the redistribution of mass that accompanied the last deglaciation- and geocenter variations needed to directly infer the ocean mass trend based on GRACE data.

scholarly journals Reduced misclosure of global sea-level budget with updated Tongji-Grace2018 solution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fengwei Wang ◽  
Yunzhong Shen ◽  
Qiujie Chen ◽  
Yu Sun
Keyword(s):  
Sea Level ◽  
Spherical Harmonic ◽  
Mass Change ◽  
Global Ocean ◽  
Argo Data ◽  
Ocean Mass ◽  
Grace Data ◽  
Budget Equation ◽  
Global Sea Level ◽  
Gravity Recovery

AbstractThe global sea-level budget is studied using the Gravity Recovery and Climate Experiment (GRACE) solutions, Satellite Altimetry and Argo observations based on the updated budget equation. When the global ocean mass change is estimated with the updated Tongji-Grace2018 solution, the misclosure of the global sea-level budget can be reduced by 0.11–0.22 mm/year compared to four other recent solutions (i.e. CSR RL06, GFZ RL06, JPL RL06 and ITSG-Grace2018) over the period January 2005 to December 2016. When the same missing months as the GRACE solution are deleted from altimetry and Argo data, the misclosure will be reduced by 0.06 mm/year. Once retained the GRACE C20 term, the linear trends of Tongji-Grace2018 and ITSG-Grace2018 solutions are 2.60 ± 0.16 and 2.54 ± 0.16 mm/year, closer to 2.60 ± 0.14 mm/year from Altimetry–Argo than the three RL06 official solutions. Therefore, the Tongji-Grace2018 solution can reduce the misclosure between altimetry, Argo and GRACE data, regardless of whether the C20 term is replaced or not, since the low-degree spherical harmonic coefficients of the Tongji-Grace2018 solution can capture more ocean signals, which are confirmed by the statistical results of the time series of global mean ocean mass change derived from five GRACE solutions with the spherical harmonic coefficients truncated to different degrees and orders.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

Grid transformation for incorporating the Arctic in a global ocean model

1994 ◽  
Vol 10 (4-5) ◽  
pp. 241-247 ◽  
Author(s):  
Michael Eby ◽  
Greg Holloway
Keyword(s):  
Ocean Model ◽  
The Arctic ◽  
Global Ocean

scholarly journals Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

2021 ◽  
Vol 18 (2) ◽  
pp. 509-534
Author(s):  
David Ford
Keyword(s):  
Global Scale ◽  
Ocean Model ◽  
Point Of View ◽  
Global Ocean ◽  
Ocean Colour ◽  
Ocean Reanalysis ◽  
Argo Data ◽  
Argo Floats ◽  
Improved Model ◽  
The Impact

Abstract. A set of observing system simulation experiments was performed. This assessed the impact on global ocean biogeochemical reanalyses of assimilating chlorophyll from remotely sensed ocean colour and in situ observations of chlorophyll, nitrate, oxygen, and pH from a proposed array of Biogeochemical-Argo (BGC-Argo) floats. Two potential BGC-Argo array distributions were tested: one for which biogeochemical sensors are placed on all current Argo floats and one for which biogeochemical sensors are placed on a quarter of current Argo floats. Assimilating BGC-Argo data greatly improved model results throughout the water column. This included surface partial pressure of carbon dioxide (pCO2), which is an important output of reanalyses. In terms of surface chlorophyll, assimilating ocean colour effectively constrained the model, with BGC-Argo providing no added benefit at the global scale. The vertical distribution of chlorophyll was improved by assimilating BGC-Argo data. Both BGC-Argo array distributions gave benefits, with greater improvements seen with more observations. From the point of view of ocean reanalysis, it is recommended to proceed with development of BGC-Argo as a priority. The proposed array of 1000 floats will lead to clear improvements in reanalyses, with a larger array likely to bring further benefits. The ocean colour satellite observing system should also be maintained, as ocean colour and BGC-Argo will provide complementary benefits.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

2009 ◽  
Vol 6 (2) ◽  
pp. 1129-1171
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

scholarly journals Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design

2020 ◽  
Author(s):  
David Ford
Keyword(s):  
Ocean Model ◽  
Point Of View ◽  
Global Ocean ◽  
Ocean Colour ◽  
Ocean Reanalysis ◽  
Argo Data ◽  
Argo Floats ◽  
Variable Surface ◽  
Added Benefit ◽  
The Impact

Abstract. A set of observing system simulation experiments has been performed to explore the impact on global ocean biogeochemical reanalyses of assimilating chlorophyll from remotely sensed ocean colour, and assess the potential impact of assimilating in situ observations of chlorophyll, nitrate, oxygen, and pH from a proposed array of Biogeochemical-Argo (BGC-Argo) floats. Two different potential BGC-Argo array distributions were tested: one where biogeochemical sensors are placed on all current Argo floats, and one where biogeochemical sensors are placed on a quarter of current Argo floats. This latter approximately corresponds to the proposed BGC-Argo array of 1000 floats. Different strategies for updating model variables when assimilating ocean colour were assessed. All similarly improved the assimilated variable surface chlorophyll, but had a mixed impact on the wider ecosystem and carbon cycle, degrading some key variables of interest. Assimilating BGC-Argo data gave no added benefit over ocean colour in terms of simulating surface chlorophyll, but for most other variables, including sub-surface chlorophyll, adding BGC-Argo greatly improved results throughout the water column. This included surface partial pressure of carbon dioxide (pCO2), which was not assimilated but is an important output of reanalyses. Both BGC-Argo array distributions gave benefits, with greater improvements seen with more observations. From the point of view of ocean reanalysis, it is recommended to proceed with development of BGC-Argo as a priority. The proposed array of 1000 floats will lead to clear improvements in reanalyses, with a larger array likely to bring further benefits. The ocean colour satellite observing system should also be maintained, as ocean colour and BGC-Argo will provide complementary benefits. There is also much potential to improve the use of existing observations, particularly in terms of multivariate balancing, through improving assimilation methodologies.

scholarly journals The Global Ocean Mass Budget in 1993–2003 Estimated from Sea Level Change

2007 ◽  
Vol 37 (2) ◽  
pp. 203-213 ◽  
Author(s):  
Manfred Wenzel ◽  
Jens Schröter
Keyword(s):  
Atlantic Ocean ◽  
Sea Level ◽  
Ocean Circulation ◽  
General Circulation ◽  
Circulation Model ◽  
Ocean Model ◽  
Global Ocean ◽  
Mass Budget ◽  
Ocean Mass ◽  
The Pacific

Abstract The mass budget of the ocean in the period 1993–2003 is studied with a general circulation model. The model has a free surface and conserves mass rather than volume; that is, freshwater is exchanged with the atmosphere via precipitation and evaporation and inflow from land is taken into account. The mass is redistributed by the ocean circulation. Furthermore, the ocean’s volume changes by steric expansion with changing temperature and salinity. To estimate the mass changes, the ocean model is constrained by sea level measurements from the Ocean Topography Experiment (TOPEX)/Poseidon mission as well as by hydrographic data. The modeled ocean mass change within the years 2002–03 compares favorably to measurements from the Gravity Recovery and Climate Experiment (GRACE), and the evolution of the global mean sea level for the period 1993–2003 with annual and interannual variations can be reproduced to a 0.15-cm rms difference. Its trend has been measured as 3.37 mm yr−1 while the constrained model gives 3.34 mm yr−1 considering only the area covered by measurements (3.25 mm yr−1 for the total ocean). A steric rise of 2.50 mm yr−1 is estimated in this period, as is a gain in the ocean mass that is equivalent to an eustatic rise of 0.74 mm yr−1. The amplitude and phase (day of maximum value since 1 January) of the superimposed eustatic annual cycle are also estimated to be 4.6 mm and 278°, respectively. The corresponding values for the semiannual cycle are 0.42 mm and 120°. The trends in the eustatic sea level are not equally distributed. In the Atlantic Ocean (80°S–67°N) the eustatic sea level rises by 1.8 mm yr−1 and in the Indian Ocean (80°S–30°N) it rises by 1.4 mm yr−1, but it falls by −0.20 mm yr−1 in the Pacific Ocean (80°S–67°N). The latter is mainly caused by a loss of mass through transport divergence in the Pacific sector of the Antarctic Circumpolar Current (−0.42 Sv; Sv ≡ 109 kg s−1) that is not balanced by the net surface water supply. The consequence of this uneven eustatic rise is a shift of the oceanic center of mass toward the Atlantic Ocean and to the north.

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 On the assimilation of ice velocity and concentration data into large-scale sea ice models

2007 ◽  
Vol 4 (2) ◽  
pp. 265-301 ◽  
Author(s):  
V. Dulière ◽  
T. Fichefet
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Observation Data ◽  
Concentration Data ◽  
Ice Concentration ◽  
Ice Velocity

Abstract. Data assimilation into sea ice models designed for climate studies has started about 15 years ago. In most of the studies conducted so far, it is assumed that the improvement brought by the assimilation is straightforward. However, some studies suggest this might not be true. In order to elucidate this question and to find an appropriate way to further assimilate sea ice concentration and velocity observations into a global sea ice-ocean model, we analyze here results from a number of twin experiments (i.e. experiments in which the assimilated data are model outputs) carried out with a simplified model of the Arctic sea ice pack. Our objective is to determine to what degree the assimilation of ice velocity and/or concentration data improves the global performance of the model and, more specifically, reduces the error in the computed ice thickness. A simple optimal interpolation scheme is used, and outputs from a control run and from perturbed experiments without and with data assimilation are thoroughly compared. Our results indicate that, under certain conditions depending on the assimilation weights and the type of model error, the assimilation of ice velocity data enhances the model performance. The assimilation of ice concentration data can also help in improving the model behavior, but it has to be handled with care because of the strong connection between ice concentration and ice thickness. This study is preliminary study towards real observation data assimilation into NEMOLIM, a global sea ice-ocean model.

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