Using the Regional Ocean Modeling System (ROMS) to improve the ocean circulation from a GCM 20th century simulation

Melsom, Arne; Lien, Vidar S.; Budgell, W. Paul

doi:10.1007/s10236-009-0222-5

Cited by 38 publications

(19 citation statements)

References 44 publications

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“…2b,d), as also found in CMIP3 models in a previous study28. Under the same atmospheric forcing, a high-resolution regional ocean model simulates stronger mean state HT BSO than a low-resolution global model does29.…”

Section: Resultssupporting

confidence: 80%

On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline

Zhang

Knutson

2017

Nat Commun

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This study aims to understand the relative roles of external forcing versus internal climate variability in causing the observed Barents Sea winter sea ice extent (SIE) decline since 1979. We identify major discrepancies in the spatial patterns of winter Northern Hemisphere sea ice concentration trends over the satellite period between observations and CMIP5 multi-model mean externally forced response. The CMIP5 externally forced decline in Barents Sea winter SIE is much weaker than that observed. Across CMIP5 ensemble members, March Barents Sea SIE trends have little correlation with global mean surface air temperature trends, but are strongly anti-correlated with trends in Atlantic heat transport across the Barents Sea Opening (BSO). Further comparison with control simulations from coupled climate models suggests that enhanced Atlantic heat transport across the BSO associated with regional internal variability may have played a leading role in the observed decline in winter Barents Sea SIE since 1979.

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Section: Resultssupporting

confidence: 80%

On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline

Zhang

Knutson

2017

Nat Commun

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“…Melsom et al . [] showed that heat transports into the Barents Sea were improved by oceanic downscaling and increased resolution. Therefore, higher resolution may improve sea ice area in global climate models, but precautions should be taken with regard to parameterizations of processes that depend on model resolution.…”

Section: Discussionmentioning

confidence: 99%

Poleward ocean heat transports, sea ice processes, and Arctic sea ice variability in NorESM1-M simulations

Sandø

Gao

Langehaug

2014

J. Geophys. Res. Oceans

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Results from the NorESM1-M coupled climate model were used to examine relationships between Arctic sea ice area and ocean heat transports through the primary Arctic gateways. Comparisons were made with two other models (CNRM-CM5 and MRI-CGM3) that are part of the CMIP5 archive which have the required outputs for calculating ocean heat transports. Based on an evaluation, NorESM1-M was found to be best suited to study the effects of heat transports on sea ice area, and conclusions are based on results from this model. The Arctic Ocean was divided into two regions, the Barents Sea and the Central Arctic Ocean. The sea ice area variability was further analyzed in terms of frazil and congelation growth, top and bottom melting, and heat transports in the Barents Sea Opening (BSO) and the Fram Strait (FS). In the Barents Sea, increased heat transport in the BSO has a strong influence on sea ice area in terms of reduced congelation growth, while bottom melting is important for the variability in the Central Arctic Ocean. The negative trend in sea ice area is considerably greater in the Barents Sea than in the Central Arctic Ocean, despite the Central Arctic Ocean area being much larger, and reflects the major trend in the BSO heat transport. The model results in this study suggest that the ocean has stronger direct impact on changes in sea ice mass in terms of freezing and melting than the atmosphere, both in the mean and with respect to variability.

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“…Typically, they also lack important shelf sea physical processes like turbulent mixing, overflows, fronts and a realistic description of the bathymetry and the coastline. Hence, there is an increasing need to use regional/local ocean models to provide valuable, high-resolution information to governments, stakeholders and coastal engineers (Å dlandsvik and Bentsen 2007;Melsom et al 2009;Holt et al 2010;Brown et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Storm surges in the Western Baltic Sea: the present and a possible future

Gräwe

Burchard

2011

Clim Dyn

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Globally-coupled climate models are generally capable of reproducing the observed trends in the globally averaged atmospheric temperature or mean sea level. However, the global models do not perform as well on regional/local scales. Here, we present results from four 100-year ocean model experiments for the Western Baltic Sea. In order to simulate storm surges in this region, we have used the General Estuarine Transport Model (GETM) as a high-resolution local model (spatial resolution & 1 km), nested into a regional atmospheric and regional oceanic model in a fully baroclinic downscaling approach. The downscaling is based on the global model ECHAM5/ MPI-OM. The projections are imbedded into two greenhouse-gas emission scenarios, A1B and B1, for the period 2000-2100, each with two realisations. Two control runs from 1960 to 2000 are used for validation. We use this modelling system to statistically reproduce the present distribution of surge extremes. The usage of the high-resolution local model leads to an improvement in surge heights of at least 10% compared to the driving model. To quantify uncertainties associated with climate projections, we investigate the impact of enhanced wind velocities and changes in mean sea levels. The analysis revealed a linear dependence of surge height and mean sea level, although the slope parameter is spatially varying. Furthermore, the modelling system is used to project possible changes within the next century. The results show that the sea level rise has greater potential to increase surge levels than does increased wind speed. The simulations further indicate that the changes in storm surge height in the scenarios can be consistently explained by the increase in mean sea level and variation in wind speed.

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Using the Regional Ocean Modeling System (ROMS) to improve the ocean circulation from a GCM 20th century simulation

Cited by 38 publications

References 44 publications

On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline

On the discrepancy between observed and CMIP5 multi-model simulated Barents Sea winter sea ice decline

Poleward ocean heat transports, sea ice processes, and Arctic sea ice variability in NorESM1-M simulations

Storm surges in the Western Baltic Sea: the present and a possible future

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