Improved representation of river runoff in Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) simulations: implementation, evaluation, and impacts to coastal plume regions

Feng, Yang; Menemenlis, Dimitris; Xue, Huijie; Zhang, Hong; Carroll, Dustin; Du, Yan; Wu, Hui

doi:10.5194/gmd-14-1801-2021

Cited by 13 publications

(6 citation statements)

References 73 publications

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“…Notably, the row of locations along 8°N in the North Pacific does not exhibit the large variability seen in the mooring data (Figure 3a). The STV in the outflow of the Congo River near the coast of Africa is much smaller in the model than in the mooring data, possibly due to the model's use of climatological river outflow (Feng et al, 2021;Fekete et al, 2002). A similar set of maps for the RE was created for the model output, but not included here for brevity.…”

Section: Short-term Variabilitymentioning

confidence: 99%

Sea Surface Salinity Short Term Variability in the Tropics

Bingham¹,

Brodnitz²

2021

Preprint

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Abstract. Using data from the Global Tropical Moored Buoy Array we study the validation process for satellite measurement of sea surface salinity (SSS). We compute short-term variability (STV) of SSS, variability on time scales of 5–14 days. It is meant to be a proxy for subfootprint variability as seen by a satellite measuring SSS. We also compute representation error, which is meant to mimic the SSS satellite validation process where footprint averages are compared to pointwise in situ values. We present maps of these quantities over the tropical array. We also look at seasonality in the variability of SSS and find which months have maximum and minimum amounts. STV is driven at least partly by rainfall. Moorings exhibit larger STV during rainy periods than non-rainy ones. The same computations are also done using output from a high-resolution global ocean model to see how it might be used to study the validation process. The model gives good estimates of STV, in line with the moorings, though tending to have smaller values.

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Section: Short-term Variabilitymentioning

confidence: 99%

Sea Surface Salinity Short Term Variability in the Tropics

Bingham¹,

Brodnitz²

2021

Preprint

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“…Overall, the JRA55-do discharge data compare reasonably well with available river gauge data around the world ocean (Suzuki et al, 2018). We implement the JRA55-do river discharge by interpolating the 0.25°-resolution discharge estimates along the coastlines onto the coastlines of the LLC270 model grid without using a smoothing operator to spread the river discharge influence away from the coasts, similar to what was done in Dandapat et al (2020) and Feng et al (2021).…”

Section: Modelmentioning

confidence: 86%

“…While such resolutions are typical of global ocean data-assimilative and climate models, they are insufficient to simulate the smaller-scale salinity structure along the coasts associated with river plumes such as those discussed by Holt et al (2017). Feng et al (2021) has conducted a preliminary assessment of the impact of JRA55-do discharge in various ECCO solutions of different resolutions, including LLC270. The use of higher-resolution models would be of interest to further investigate the impacts of nonseasonal discharge on SSS and SSH near the river mouths.…”

Section: Notementioning

confidence: 99%

Influence of Nonseasonal River Discharge on Sea Surface Salinity and Height

Chandanpurkar

Lee

Wang

et al. 2022

J Adv Model Earth Syst

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“…Earth system models or climate models are important tools to represent and project the global water and biogeochemical cycles and climate change (e.g., Winkelbauer et al, 2022;Brady et al, 2019;Dufresne et al, 2013;Clark et al, 2015). An accurate simulation of the river water behavior over the shelf seas is one of key steps for accurate simulation in these models, but is usually hard to be achieved in earth system models or climate models (Feng et al, 2021). On one hand, climate models used relatively large grid cells which are too coarse for shelf seas with relatively small spatial scales (Graham et al, 2018;Feng et al, 2021;Holt et al, 2017).…”

mentioning

confidence: 99%

“…An accurate simulation of the river water behavior over the shelf seas is one of key steps for accurate simulation in these models, but is usually hard to be achieved in earth system models or climate models (Feng et al, 2021). On one hand, climate models used relatively large grid cells which are too coarse for shelf seas with relatively small spatial scales (Graham et al, 2018;Feng et al, 2021;Holt et al, 2017). This issue would be addressed in the future as the computation power increase.…”

mentioning

confidence: 99%

Effect of tides on river water behavior over the eastern shelf seas of China

Lin

Líu

et al. 2022

Preprint

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Abstract. Rivers carry large amounts of freshwater and terrestrial material into shelf seas, which is an important part of the global water and biogeochemical cycles. The earth system model or climate model is an important instrument for simulating and projecting the global water cycle and climate change, in which tides however are commonly removed. For a better understanding of the potential effect of the absence of tides in the simulation of the water cycle, this study compared the results of a regional model with and without considering tides, and evaluated the effect of tides on the behavior of three major rivers (i.e., the Yellow, Yalujiang, and Changjiang Rivers) water in the eastern shelf seas of China from the perspectives of transport pathways, timescales, and water concentration. The results showed that the tides induced more dispersed transport for the water of the Yellow and Yalujiang rivers, but more concentrated transport for the Changjiang River water. The effect of tides on the transit areas of the Yellow, Yalujiang, and Changjiang Rivers was 13, 40, and 21 %, respectively. The annual mean water age and transit time of the three rivers in the model with tides were several (~2–10) times higher than those in the no-tide model, suggesting that tides dramatically slow the river water transport and export rate over the shelf. By slowing the river water export, tides induced a three-fold increase in river water concentration and a decrease in shelf seawater salinity by >1. Moreover, the effect of tides on river behavior was stronger in relatively enclosed seas (i.e., the Bohai and Yellow Seas) than in relatively open seas (i.e., the East China Sea). The change in the shelf currents induced by tides is the main cause of the difference in the river water behavior between the two model runs. Tides can increase bottom stress and thus weaken shelf currents and decrease the water transport timescales. The improvement in tidal parameterization in the no-tide model in the simulation of river water behavior was very limited. Given the important role of river runoff on the global water cycle and the effect of changes in river water behavior on ocean carbon cycling, it is important to include the tidal effect in earth system models to improve their projection accuracy.

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Improved representation of river runoff in Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) simulations: implementation, evaluation, and impacts to coastal plume regions

Cited by 13 publications

References 73 publications

Sea Surface Salinity Short Term Variability in the Tropics

Sea Surface Salinity Short Term Variability in the Tropics

Influence of Nonseasonal River Discharge on Sea Surface Salinity and Height

Effect of tides on river water behavior over the eastern shelf seas of China

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