scholarly journals Impact of Initialization Methods on the Predictive skill in NorCPM - An Arctic-Atlantic Case Study

2021 ◽  
Author(s):  
Leilane Passos ◽  
Helene Langehaug ◽  
Marius Årthun ◽  
Tor Eldevik ◽  
Ingo Bethke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Complex Data ◽  
Surface Salinity ◽  
Norwegian Sea ◽  
Predictive Skill ◽  
The Impact

Abstract The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) – for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic-Atlantic region – focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8-10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

scholarly journals Impact of data assimilation of physical variables on the spring bloom from TOPAZ operational runs in the North Atlantic

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 635-647 ◽  
Author(s):  
A. Samuelsen ◽  
L. Bertino ◽  
C. Hansen
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Spring Bloom ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Sea Surface ◽  
The Arctic ◽  
The North ◽  
The North Atlantic ◽  
The Impact

Abstract. A reanalysis of the North Atlantic spring bloom in 2007 was produced using the real-time analysis from the TOPAZ North Atlantic and Arctic forecasting system. The TOPAZ system uses a hybrid coordinate general circulation ocean model and assimilates physical observations: sea surface anomalies, sea surface temperatures, and sea-ice concentrations using the Ensemble Kalman Filter. This ocean model was coupled to an ecosystem model, NORWECOM (Norwegian Ecological Model System), and the TOPAZ-NORWECOM coupled model was run throughout the spring and summer of 2007. The ecosystem model was run online, restarting from analyzed physical fields (result after data assimilation) every 7 days. Biological variables were not assimilated in the model. The main purpose of the study was to investigate the impact of physical data assimilation on the ecosystem model. This was determined by comparing the results to those from a model without assimilation of physical data. The regions of focus are the North Atlantic and the Arctic Ocean. Assimilation of physical variables does not affect the results from the ecosystem model significantly. The differences between the weekly mean values of chlorophyll are normally within 5–10% during the summer months, and the maximum difference of ~20% occurs in the Arctic, also during summer. Special attention was paid to the nutrient input from the North Atlantic to the Nordic Seas and the impact of ice-assimilation on the ecosystem. The ice-assimilation increased the phytoplankton concentration: because there was less ice in the assimilation run, this increased both the mixing of nutrients during winter and the area where production could occur during summer. The forecast was also compared to remotely sensed chlorophyll, climatological nutrients, and in-situ data. The results show that the model reproduces a realistic annual cycle, but the chlorophyll concentrations tend to be between 0.1 and 1.0 mg chla/m3 too low during winter and spring and 1–2 mg chla/m3 too high during summer. Surface nutrients on the other hand are generally lower than the climatology throughout the year.

scholarly journals The 2007 North Atlantic spring bloom in operational analysis from the TOPAZ system

2009 ◽  
Vol 6 (1) ◽  
pp. 343-369
Author(s):  
A. Samuelsen ◽  
L. Bertino ◽  
C. Hansen
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Spring Bloom ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Sea Surface ◽  
The Arctic ◽  
The North ◽  
The North Atlantic ◽  
The Impact

Abstract. A reanalysis of the North Atlantic spring bloom in 2007 was produced using the real-time analyses from the TOPAZ (Towards an Operational Prediction system for the North Atlantic European coastal Zones) North Atlantic and Arctic forecasting system. The TOPAZ system uses a hybrid coordinate general circulation ocean model and assimilates physical observations: sea surface anomalies, sea surface temperatures, and sea-ice concentrations using the Ensemble Kalman Filter. This ocean model was coupled to an ecosystem model, NORWECOM (Norwegian Ecological Model System), and the TOPAZ-NORWECOM coupled model was run throughout the spring and summer of 2007. The ecosystem model was run online, restarting from analyzed physical fields (result after data assimilation) every 7 days. Biological variables were not assimilated in the model. The forecast was compared to remotely sensed chlorophyll and in-situ data. The impact of physical data assimilation on the ecosystem model was determined by comparing the results to those from a model without assimilation of physical data. The regions of focus are the North Atlantic and the Arctic Ocean. The results show that the model reproduces a realistic annual cycle, but the chlorophyll concentrations tend to be too low during winter and spring and too high during summer. Surface nutrients on the other hand are generally too low throughout the year. Assimilation of physical variables does not affect the results from the ecosystem model significantly. The differences between the weekly mean values of chlorophyll are normally within 5–10% during the summer months, and the maximum difference of ~20% occurs in the Arctic, also during summer. Special attention was paid to the nutrient input from the North Atlantic to the Nordic Seas and the impact of ice-assimilation on the ecosystem. The ice-assimilation increased the phytoplankton concentration: because there was less ice in the assimilation run, this increased both the mixing of nutrients during winter and the area where production could occur during summer.

scholarly journals Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

2021 ◽  
Vol 13 (5) ◽  
pp. 831
Author(s):  
Jorge Vazquez-Cuervo ◽  
Chelle Gentemann ◽  
Wenqing Tang ◽  
Dustin Carroll ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Future Research ◽  
Surface Salinity ◽  
Cold Temperatures ◽  
Yukon River ◽  
Arctic Sea ◽  
The Arctic Ocean

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations.

scholarly journals Response of Freshwater Flux and Sea Surface Salinity to Variability of the Atlantic Warm Pool

2013 ◽  
Vol 26 (4) ◽  
pp. 1249-1267 ◽  
Author(s):  
Chunzai Wang ◽  
Liping Zhang ◽  
Sang-Ki Lee
Keyword(s):  
Central America ◽  
North Atlantic ◽  
Warm Pool ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Freshwater Flux ◽  
Surface Salinity ◽  
Atlantic Warm Pool ◽  
Mean Circulation ◽  
Circulation Dynamics

Abstract The response of freshwater flux and sea surface salinity (SSS) to the Atlantic warm pool (AWP) variations from seasonal to multidecadal time scales is investigated by using various reanalysis products and observations. All of the datasets show a consistent response for all time scales: A large (small) AWP is associated with a local freshwater gain (loss) to the ocean, less (more) moisture transport across Central America, and a local low (high) SSS. The moisture budget analysis demonstrates that the freshwater change is dominated by the atmospheric mean circulation dynamics, while the effect of thermodynamics is of secondary importance. Further decomposition points out that the contribution of the mean circulation dynamics primarily arises from its divergent part, which mainly reflects the wind divergent change in the low level as a result of SST change. In association with a large (small) AWP, warmer (colder) than normal SST over the tropical North Atlantic can induce anomalous low-level convergence (divergence), which favors anomalous ascent (decent) and thus generates more (less) precipitation. On the other hand, a large (small) AWP weakens (strengthens) the trade wind and its associated westward moisture transport to the eastern North Pacific across Central America, which also favors more (less) moisture residing in the Atlantic and hence more (less) precipitation. The results imply that variability of freshwater flux and ocean salinity in the North Atlantic associated with the AWP may have the potential to affect the Atlantic meridional overturning circulation.

scholarly journals Data assimilation of SMOS observations into the Mercator Ocean operational system: focus on the Nino 2015 event

2018 ◽  
Author(s):  
Benoît Tranchant ◽  
Elisabeth Remy ◽  
Eric Greiner ◽  
Olivier Legalloudec
Keyword(s):  
Data Assimilation ◽  
Ocean Circulation ◽  
Positive Impact ◽  
Atmospheric Precipitation ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Data Sets ◽  
The Impact ◽  
Over The Top

Abstract. Monitoring Sea Surface Salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the acceleration of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, most often closer to 5 meters deep than the surface, were available to estimate the SSS. The recent satellite missions of ESA's SMOS, NASA's Aquarius, and now SMAP have made possible for the first time to measure SSS from space. The SSS drivers can be quite different than the temperature ones. The model SSS can suffer from significant errors coming not only from the ocean dynamical model but also the atmospheric precipitation and evaporation as well as ice melting and river runoff. Satellite SSS can bring a valuable additional constraint to control the model salinity. In the framework of the SMOS Nino 2015 ESA project (https://www.godae-oceanview.org/projects/smos-nino15/), the impact of satellite SSS data assimilation is assessed with the Met Office and Mercator Ocean global ocean analysis and forecasting systems with a focus on the Tropical Pacific region. This article presents the analysis of an Observing System Experiment (OSE) conducted with the 1/4° resolution Mercator Ocean analysis and forecasting system. SSS data assimilation constrains the model SSS to be closer to the observations in a coherent way with the other data sets already routinely assimilated in an operational context. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 meters. Comparisons to independent data sets show a small but positive impact. The sea surface height (SSH) has also been impacted by implying a reinforcement of TIWs during the El-Niño 2015/16 event. Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance.

scholarly journals Effects of Tropical Cyclones on Sea Surface Salinity in the Bay of Bengal Based on SMAP and Argo Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 2975
Author(s):  
Huabing Xu ◽  
Rongzhen Yu ◽  
Danling Tang ◽  
Yupeng Liu ◽  
Sufen Wang ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Bay Of Bengal ◽  
Vertical Mixing ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Ekman Pumping ◽  
Surface Salinity ◽  
Argo Data ◽  
Before And After ◽  
The Impact

This paper uses the Argo sea surface salinity (SSSArgo) before and after the passage of 25 tropical cyclones (TCs) in the Bay of Bengal from 2015 to 2019 to evaluate the sea surface salinity (SSS) of the Soil Moisture Active Passive (SMAP) remote sensing satellite (SSSSMAP). First, SSSArgo data were used to evaluate the accuracy of the 8-day SMAP SSS data, and the correlations and biases between SSSSMAP and SSSArgo were calculated. The results show good correlations between SSSSMAP and SSSArgo before and after TCs (before: SSSSMAP = 1.09SSSArgo−3.08 (R2 = 0.69); after: SSSSMAP = 1.11SSSArgo−3.61 (R2 = 0.65)). A stronger negative bias (−0.23) and larger root-mean-square error (RMSE, 0.95) between the SSSSMAP and SSSArgo were observed before the passage of 25 TCs, which were compared to the bias (−0.13) and RMSE (0.75) after the passage of 25 TCs. Then, two specific TCs were selected from 25 TCs to analyze the impact of TCs on the SSS. The results show the significant SSS increase up to the maximum 5.92 psu after TC Kyant (2016), which was mainly owing to vertical mixing and strong Ekman pumping caused by TC and high-salinity waters in the deep layer that were transported to the sea surface. The SSSSMAP agreed well with SSSArgo in both coastal and offshore waters before and after TC Roanu (2016) and TC Kyant (2016) in the Bay of Bengal.

Removing the impact of wind direction on remote sensing of sea surface salinity

2006 ◽  
Vol 51 (11) ◽  
pp. 1368-1373 ◽  
Author(s):  
Xiaobin Yin ◽  
Yuguang Liu ◽  
Hande Zhang
Keyword(s):  
Remote Sensing ◽  
Wind Direction ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
The Impact
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