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1
Zhao, Jian, Yan Wang, Wenjing Liu, Hongsheng Bi, Edward D. Cokelet, Calvin W. Mordy, Noah Lawrence-Slavas, and Christian Meinig. "Sea Surface Salinity Variability in the Bering Sea in 2015–2020." Remote Sensing 14, no. 3 (February 6, 2022): 758. http://dx.doi.org/10.3390/rs14030758.
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Salinity in the Bering Sea is vital for the physical environment that is tied to the productive ecosystem and the properties of Pacific waters transported to the Arctic Ocean. Its salinity variability reflects many fundamental processes, including sea ice formation/melting and river runoff, but its spatial and temporal characteristics require better documentation. This study utilizes remote sensing products and in situ observations collected by saildrone missions to investigate Sea Surface Salinity (SSS) variability. All Satellite products resolve the large-scale pattern set up by the relatively salty deep basin and the fresh coastal region, but they can be inaccurate near the ice edge and near land. The SSS annual cycle exhibits seasonal maxima in winter to spring, and minima in summer to fall. The amplitude and timing of the seasonal cycle are variable, especially on the eastern Bering Sea shelf. SSS variability recorded by both saildrone, and satellite instruments provide unprecedented insights into short-term oceanic processes including sea ice melting, wind-driven currents during weather events, and river plumes etc. In particular, the Soil Moisture Active Passive (SMAP) satellite demonstrates encouraging skills in capturing the freshening signals induced by spring sea ice melting. The Yukon River plume is another source of intense SSS variability. Surface wind forcing plays an essential role in controlling the horizontal movement of plume water and thereby shaping the SSS seasonal cycle in local regions.
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Sharma, Rashmi, Neeraj Agarwal, Imran M. Momin, Sujit Basu, and Vijay K. Agarwal. "Simulated Sea Surface Salinity Variability in the Tropical Indian Ocean." Journal of Climate 23, no. 24 (December 15, 2010): 6542–54. http://dx.doi.org/10.1175/2010jcli3721.1.
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Abstract A long-period (15 yr) simulation of sea surface salinity (SSS) obtained from a hindcast run of an ocean general circulation model (OGCM) forced by the NCEP–NCAR daily reanalysis product is analyzed in the tropical Indian Ocean (TIO). The objective of the study is twofold: assess the capability of the model to provide realistic simulations of SSS and characterize the SSS variability in view of upcoming satellite salinity missions. Model fields are evaluated in terms of mean, standard deviation, and characteristic temporal scales of SSS variability. Results show that the standard deviations range from 0.2 to 1.5 psu, with larger values in regions with strong seasonal transitions of surface currents (south of India) and along the coast in the Bay of Bengal (strong Kelvin-wave-induced currents). Comparison of simulated SSS with collocated SSS measurements from the National Oceanographic Data Center and Argo floats resulted in a high correlation of 0.85 and a root-mean-square error (RMSE) of 0.4 psu. The correlations are quite high (>0.75) up to a depth of 300 m. Daily simulations of SSS compare well with a Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA) buoy in the eastern equatorial Indian Ocean (1.5°S, 90°E) with an RMSE of 0.3 psu and a correlation better than 0.6. Model SSS compares well with observations at all time scales (intraseasonal, seasonal, and interannual). The decorrelation scales computed from model and buoy SSS suggest that the proposed 10-day sampling of future salinity sensors would be able to resolve much of the salinity variability at time scales longer than intraseasonal. This inference is significant in view of satellite salinity sensors, such as Soil Moisture and Ocean Salinity (SMOS) and Aquarius.
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Michel, S., B. Chapron, J. Tournadre, and N. Reul. "Sea surface salinity variability from a simplified mixed layer model of the global ocean." Ocean Science Discussions 4, no. 1 (January 15, 2007): 41–106. http://dx.doi.org/10.5194/osd-4-41-2007.
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Abstract. A bi-dimensional mixed layer model (MLM) of the global ocean is used to investigate the sea surface salinity (SSS) balance and variability at daily to seasonal scales. Thus a simulation over an average year is performed with daily climatological forcing fields. The forcing dataset combines air-sea fluxes from a meteorological model, geostrophic currents from satellite altimeters and in situ data for river run-offs, deep temperature and salinity. The model is based on the "slab mixed layer" formulation, which allows many simplifications in the vertical mixing representation, but requires an accurate estimate for the Mixed Layer Depth. Therefore, the model MLD is obtained from an original inversion technique, by adjusting the simulated temperature to input sea surface temperature (SST) data. The geographical distribution and seasonal variability of this "effective" MLD is validated against an in situ thermocline depth. This comparison proves the model results are consistent with observations, except at high latitudes and in some parts of the equatorial band. The salinity balance can then be analysed in all the remaining areas. The annual tendency and amplitude of each of the six processes included in the model are described, whilst providing some physical explanations. A map of the dominant process shows that freshwater flux controls SSS in most tropical areas, Ekman transport in Trades regions, geostrophic advection in equatorial jets, western boundary currents and the major part of subtropical gyres, while diapycnal mixing leads over the remaining subtropical areas and at higher latitudes. At a global scale, SSS variations are primarily caused by horizontal advection (46%), then vertical entrainment (24%), freshwater flux (22%) and lateral diffusion (8%). Finally, the simulated SSS variability is compared to an in situ climatology, in terms of distribution and seasonal variability. The overall agreement is satisfying, which confirms that the salinity balance is reliable. The simulation exhibits stronger gradients and higher variability, due to its fine resolution and high frequency forcing. Moreover, the SSS variability at daily scale can be investigated from the model, revealing patterns considerably different from the seasonal cycle. Within the perspective of the future satellite missions dedicated to SSS retrieval (SMOS and Aquarius/SAC-D), the MLM could be useful for determining calibration areas, as well as providing a first-guess estimate to inversion algorithms.
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Bingham, Frederick M., Susannah Brodnitz, and Lisan Yu. "Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations." Remote Sensing 13, no. 1 (December 31, 2020): 110. http://dx.doi.org/10.3390/rs13010110.
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Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS is seasonal, it is important to know if satellites and gridded in situ products are observing the seasonal variability correctly. In this study we validate the seasonal SSS from satellite and gridded in situ products using observations from moorings in the global tropical moored buoy array. We utilize six different satellite products, and two different gridded in situ products. For each product we have computed seasonal harmonics, including amplitude, phase and fraction of variance (R2). These quantities are mapped for each product and for the moorings. We also do comparisons of amplitude, phase and R2 between moorings and all the satellite and gridded in situ products. Taking the mooring observations as ground truth, we find general good agreement between them and the satellite and gridded in situ products, with near zero bias in phase and amplitude and small root mean square differences. Tables are presented with these quantities for each product quantifying the degree of agreement.
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Castellanos, Paola, Estrella Olmedo, Josep Lluis Pelegrí, Antonio Turiel, and Edmo J. D. Campos. "Seasonal Variability of Retroflection Structures and Transports in the Atlantic Ocean as Inferred from Satellite-Derived Salinity Maps." Remote Sensing 11, no. 7 (April 3, 2019): 802. http://dx.doi.org/10.3390/rs11070802.
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Three of the world’s most energetic regions are in the tropical and South Atlantic: the North Brazil Current Retroflection, the Brazil-Malvinas Confluence, and the Agulhas Current Retroflection. All three regions display offshore diversions of major boundary currents, which define the intensity of the returning limb of the Atlantic meridional overturning circulation. In this work, we use a sea-surface salinity (SSS) satellite product, combined with a high-resolution numerical model and in situ measurements, in order to explore the seasonal variation of the surface currents and transports in these three regions. The analysis of the model output shows that the SSS patterns reflect the surface velocity structure, with the largest horizontal SSS gradients coinciding with those areas of highest velocity and the most predominant velocity vector being 90° anticlockwise (clockwise) from the horizontal SSS gradient in the northern (southern) hemisphere. This information is then applied to the SSS satellite product to obtain maps of water velocity and salt transports, leading to a quantitative tool to estimate both water and salt transports in key regions of the world ocean.
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Fournier, Severine, and Tong Lee. "Seasonal and Interannual Variability of Sea Surface Salinity Near Major River Mouths of the World Ocean Inferred from Gridded Satellite and In-Situ Salinity Products." Remote Sensing 13, no. 4 (February 17, 2021): 728. http://dx.doi.org/10.3390/rs13040728.
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Large rivers are key components of the land-ocean branch of the global water and biogeochemical cycles. River discharges can have important influences on physical, biological, optical, and chemical processes in coastal oceans. It is, therefore, of importance to routinely monitor the time-varying dispersal patterns of river plumes. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active Passive (SMAP) satellites provide Sea Surface Salinity (SSS) observations capable of characterizing the spatial and temporal variability of major river plumes. The main objective of this study is to examine the consistency of SSS products, from these two missions, and two in-situ gridded salinity products in depicting SSS variations on seasonal to interannual time scales within a few hundred kilometers of major river mouths. We show that SSS from SMOS and SMAP satellites have good consistency in depicting seasonal and interannual SSS variations near major river mouths. The two gridded in-situ products underestimate these variations substantially. This underestimation, most notably associated with the low SSS season following the high-discharge season, is attributable to the limited in-situ sampling of the river plumes when they are the most active. This work underscores the importance of using satellite SSS to study river plumes, as well as to evaluate and constrain models.
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Yu, Lisan. "Variability and Uncertainty of Satellite Sea Surface Salinity in the Subpolar North Atlantic (2010–2019)." Remote Sensing 12, no. 13 (June 30, 2020): 2092. http://dx.doi.org/10.3390/rs12132092.
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Satellite remote sensing of sea surface salinity (SSS) in the recent decade (2010–2019) has proven the capability of L-band (1.4 GHz) measurements to resolve SSS spatiotemporal variability in the tropical and subtropical oceans. However, the fidelity of SSS retrievals in cold waters at mid-high latitudes has yet to be established. Here, four SSS products derived from two satellite missions were evaluated in the subpolar North Atlantic Ocean in reference to two in situ gridded products. Harmonic analysis of annual and semiannual cycles in in situ products revealed that seasonal variations of SSS are dominated by an annual cycle, with a maximum in March and a minimum in September. The annual amplitudes are larger (>0.3 practical salinity scale (pss)) in the western basin where surface waters are colder and fresher, and weaker (~0.06 pss) in the eastern basin where surface waters are warmer and saltier. Satellite SSS products have difficulty producing the right annual cycle, particularly in the Labrador/Irminger seas where the SSS seasonality is dictated by the influx of Arctic low-salinity waters along the boundary currents. The study also found that there are basin-scale, time-varying drifts in the decade-long SMOS data records, which need to be corrected before the datasets can be used for studying climate variability of SSS.
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Olmedo, Estrella, Carolina Gabarró, Verónica González-Gambau, Justino Martínez, Joaquim Ballabrera-Poy, Antonio Turiel, Marcos Portabella, Severine Fournier, and Tong Lee. "Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions." Remote Sensing 10, no. 11 (November 8, 2018): 1772. http://dx.doi.org/10.3390/rs10111772.
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This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.
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D’Addezio, Joseph M., Bulusu Subrahmanyam, Ebenezer S. Nyadjro, and V. S. N. Murty. "Seasonal Variability of Salinity and Salt Transport in the Northern Indian Ocean." Journal of Physical Oceanography 45, no. 7 (July 2015): 1947–66. http://dx.doi.org/10.1175/jpo-d-14-0210.1.
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AbstractAnalyses using a suite of observational datasets (Aquarius and Argo) and model simulations are carried out to examine the seasonal variability of salinity in the northern Indian Ocean (NIO). The model simulations include Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2), the European Centre for Medium-Range Weather Forecasts–Ocean Reanalysis System 4 (ECMWF–ORAS4), Simple Ocean Data Assimilation (SODA) reanalysis, and the Hybrid Coordinate Ocean Model (HYCOM). The analyses of salinity at the surface and at depths up to 200 m, surface salt transport in the top 5-m layer, and depth-integrated salt transports revealed different salinity processes in the NIO that are dominantly related to the semiannual monsoons. Aquarius proves a useful tool for observing this dynamic region and reveals some aspects of sea surface salinity (SSS) variability that Argo cannot resolve. The study revealed large disagreement between surface salt transports derived from observed- and analysis-derived salinity fields. Although differences in SSS between the observations and the model solutions are small, model simulations provide much greater spatial variability of surface salt transports due to finer detailed current structure. Meridional depth-integrated salt transports along 6°N revealed dominant advective processes from the surface toward near-bottom depths. In the Arabian Sea (Bay of Bengal), the net monthly mean maximum northward (southward) salt transport of ~50 × 106 kg s −1 occurs in July, and annual-mean salt transports across this section are about −2.5 × 106 kg s −1 (3 × 106 kg s −1).
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Rathore, Saurabh, Nathaniel L. Bindoff, Caroline C. Ummenhofer, Helen E. Phillips, and Ming Feng. "Near-Surface Salinity Reveals the Oceanic Sources of Moisture for Australian Precipitation through Atmospheric Moisture Transport." Journal of Climate 33, no. 15 (August 1, 2020): 6707–30. http://dx.doi.org/10.1175/jcli-d-19-0579.1.
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AbstractThe long-term trend of sea surface salinity (SSS) reveals an intensification of the global hydrological cycle due to human-induced climate change. This study demonstrates that SSS variability can also be used as a measure of terrestrial precipitation on interseasonal to interannual time scales, and to locate the source of moisture. Seasonal composites during El Niño–Southern Oscillation/Indian Ocean dipole (ENSO/IOD) events are used to understand the variations of moisture transport and precipitation over Australia, and their association with SSS variability. As ENSO/IOD events evolve, patterns of positive or negative SSS anomaly emerge in the Indo-Pacific warm pool region and are accompanied by atmospheric moisture transport anomalies toward Australia. During co-occurring La Niña and negative IOD events, salty anomalies around the Maritime Continent (north of Australia) indicate freshwater export and are associated with a significant moisture transport that converges over Australia to create anomalous wet conditions. In contrast, during co-occurring El Niño and positive IOD events, a moisture transport divergence anomaly over Australia results in anomalous dry conditions. The relationship between SSS and atmospheric moisture transport also holds for pure ENSO/IOD events but varies in magnitude and spatial pattern. The significant pattern correlation between the moisture flux divergence and SSS anomaly during the ENSO/IOD events highlights the associated ocean–atmosphere coupling. A case study of the extreme hydroclimatic events of Australia (e.g., the 2010/11 Brisbane flood) demonstrates that the changes in SSS occur before the peak of ENSO/IOD events. This raises the prospect that tracking of SSS variability could aid the prediction of Australian rainfall.
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Liu, Hao, Zexun Wei, and Xunwei Nie. "Assessing the Relationship between Freshwater Flux and Sea Surface Salinity." Remote Sensing 14, no. 9 (April 30, 2022): 2149. http://dx.doi.org/10.3390/rs14092149.
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Exploring the relationship between evaporation (E)-minus-precipitation (P) and sea surface salinity (SSS) is vital for understanding global hydrological cycle changes and investigating the salinity budget. This study quantifies the uncertainty in the relationship between E−P and SSS based on satellite data over the 50°S–50°N ocean from 2012 to 2017 in 140 sets of combinations of E, P and SSS. We find that the uncertainty (10%) in the variability of freshwater flux (FWF) over 2012–2017 is smaller than that in SSS (15%). The difference in the combination of sets of “E-P-SSS” products can lead to the 10% difference in RMSD and 25% difference in area-weighted mean correlation coefficients between SSS tendency and FWF. There is a 24.1~58% area over the global ocean with a significant (p value < 0.05) positive correlation between the FWF and SSS tendency derived from satellite products. The seasonal EMP and SSS tendencies show larger correlation coefficients and lower RMSDs over most sets compared with those on nonseasonal time scales. Large uncertainty in the FWF-SSS tendency relation associated with spread among products prevents the use of one combination of E, P and SSS from satellite-based products for salinity budget analysis.
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Naidu, P. D., N. Niitsuma, and S. Naik. "Oxygen isotopic analyses of individual planktic foraminifera species: implications for seasonality in the western Arabian Sea." Climate of the Past Discussions 10, no. 5 (September 1, 2014): 3661–88. http://dx.doi.org/10.5194/cpd-10-3661-2014.
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Abstract. The variation of stable isotopes between individual shells of planktic foraminifera of a given species and size may provide short-term seasonal insight on Paleoceanography. In this context, oxygen isotope analyses of individual Globigerinoides sacculifer and Neogloboquadrina dutertrei were carried out from the Ocean Drilling Program Site 723A in the western Arabian Sea to unravel the seasonal changes for the last 22 kyr. δ18O values of single shells of G. sacculifer range from of 0.54 to 2.09‰ at various depths in the core which cover a time span of the last 22 kyr. Maximum inter-shell δ18O variability and high standard deviation is noticed from 20 to 10 kyr, whereas from 10 kyr onwards the inter shell δ18O variability decreased. The individual contribution of sea surface temperature (SST) and sea surface salinity (SSS) on the inter shell δ18O values of G. sacculifer were quantified. Maximum seasonal SST between 20 and 14 ka was caused due to weak summer monsoon upwelling and strong cold winter arid continental winds. Maximum SSS differences between 18 and 10 ka is attributed to the increase of net evaporation minus precipitation due to the shift of ITCZ further south. Overall, winter dominated SST signal in Greenland would be responsible to make a teleconnection between Indian monsoon and Greenland temperature. Thus the present study has wider implications in understanding wether the forcing mechanisms of tropical monsoon climate lies in high latitudes or in the tropics.
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Fournier, Séverine, Tong Lee, Wenqing Tang, Michael Steele, and Estrella Olmedo. "Evaluation and Intercomparison of SMOS, Aquarius, and SMAP Sea Surface Salinity Products in the Arctic Ocean." Remote Sensing 11, no. 24 (December 17, 2019): 3043. http://dx.doi.org/10.3390/rs11243043.
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Salinity is a critical parameter in the Arctic Ocean, having potential implications for climate and weather. This study presents the first systematic analysis of 6 commonly used sea surface salinity (SSS) products from the National Aeronautics and Space Administration (NASA) Aquarius and Soil Moisture Active Passive (SMAP) satellites and the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, in terms of their consistency among one another and with in-situ data. Overall, the satellite SSS products provide a similar characterization of the time mean SSS large-scale patterns and are relatively consistent in depicting the regions with strong SSS temporal variability. When averaged over the Arctic Ocean, the SSS show an excellent consistency in describing the seasonal and interannual variations. Comparison of satellite SSS with in-situ salinity measurements along ship transects suggest that satellite SSS captures salinity gradients away from regions with significant sea-ice concentration. The root-mean square differences (RMSD) of satellite SSS with respect to in-situ measurements improves with increasing temperature, reflecting the limitation of L-band radiometric sensitivity to SSS in cold water. However, the satellite SSS biases with respect to the in-situ measurements do not show a consistent dependence on temperature. The results have significant implications for the calibration and validation of satellite SSS as well as for the modeling community and the design of future satellite missions.
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Bingham, Frederick M., and Zhijin Li. "Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions." Remote Sensing 12, no. 23 (December 6, 2020): 3996. http://dx.doi.org/10.3390/rs12233996.
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Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall.
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Bingham, Frederick M. "Subfootprint Variability of Sea Surface Salinity Observed during the SPURS-1 and SPURS-2 Field Campaigns." Remote Sensing 11, no. 22 (November 18, 2019): 2689. http://dx.doi.org/10.3390/rs11222689.
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Subfootprint variability (SFV), variability within the footprint of a satellite measurement, is a source of error associated with the validation process, especially for a satellite measurement with a large footprint such as those measuring sea surface salinity (SSS). This type of error has not been adequately quantified in the past. In this study, I have examined SFV using in situ ocean data from the SPURS-1 (Salinity Processes in the Upper ocean Regional Studies-1) and SPURS-2 field campaigns in the subtropical North Atlantic and eastern tropical North Pacific respectively. I computed SFV from these data over two one-year periods of intense sampling. The results show that SFV is highly seasonal. I have computed SFV errors in several different forms, a median value of the weekly snapshot error, a total snapshot error, an absolute error of the Aquarius and SMAP (Soil Moisture Active Passive) measurement, a part of that error associated with SFV and a bias due to the skewness of the distribution of SSS. These results are characteristic only of the particular regions studied. However, comparison of the results with high resolution models, and in situ data from moorings gives the possibility of getting global estimates of SFV from these other more common sources of SSS data.
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Asami, Ryuji, Yasufumi Iryu, Kimio Hanawa, Takashi Miwa, Peter Holden, Ryuichi Shinjo, and Gustav Paulay. "MIS 7 interglacial sea-surface temperature and salinity reconstructions from a southwestern subtropical Pacific coral." Quaternary Research 80, no. 3 (November 2013): 575–85. http://dx.doi.org/10.1016/j.yqres.2013.09.002.
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We generated a 5.5-yr snapshot of biweekly-to-monthly resolved time series of carbon and oxygen isotope composition (δ13C and δ18O) and Sr/Ca and Mg/Ca from annually banded aragonite skeleton of a ~ 197 ka pristine Porites coral collected at Niue Island (19°00′S, 169°50′W) in the southwestern subtropical Pacific Ocean. This report is the first of a high-resolution coral-based paleoclimate archive during the Marine Isotope Stage (MIS) 7 interglacial. Statistical results suggest that annual averages of sea-surface temperature (SST) and salinity (SSS) at ~ 197 ka were not significantly different from and ~ 1.2 higher than at present, respectively. Monthly mean variations showed increased SSS at ~ 197 ka that was higher (1.4–1.9 relative to today) in the austral summer than in the austral winter. Monthly SST and SSS anomalies at ~ 197 ka indicated smaller amplitudes by ~ 0.3°C (11%) and ~ 0.3 (24%) relative to the present, possibly suggesting less influence of interannual climate variability around Niue. Our results, taken together with other climate proxy records, imply seasonal and interannual modulation of thermal and hydrological conditions, different from today, in the southwestern subtropical Pacific Ocean associated with the Western Pacific Warm Pool and the South Pacific Convergence Zone variability during the MIS 7 interglacial.
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Reverdin, Gilles, Hedinn Valdimarsson, Gael Alory, Denis Diverres, Francis Bringas, Gustavo Goni, Lars Heilmann, Leon Chafik, Tanguy Szekely, and Andrew R. Friedman. "North Atlantic subpolar gyre along predetermined ship tracks since 1993: a monthly data set of surface temperature, salinity, and density." Earth System Science Data 10, no. 3 (August 1, 2018): 1403–15. http://dx.doi.org/10.5194/essd-10-1403-2018.
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Abstract. We present a binned product of sea surface temperature, sea surface salinity, and sea surface density data in the North Atlantic subpolar gyre from 1993 to 2017 that resolves seasonal variability along specific ship routes (https://doi.org/10.6096/SSS-BIN-NASG). The characteristics of this product are described and validated through comparisons to other monthly products. Data presented in this work were collected in regions crossed by two predetermined ship transects, between Denmark and western Greenland (AX01) and between Iceland, Newfoundland, and the northeastern USA (AX02). The data were binned along a selected usable transect. The analysis and the strong correlation between successive seasons indicate that in large parts of the subpolar gyre, the binning approach is robust and resolves the seasonal timescales, in particular after 1997 and in regions away from the continental shelf. Prior to 2002, there was no winter sampling over the West Greenland Shelf. Variability in sea surface salinity increases towards Newfoundland south of 54∘ N, as well as in the western Iceland Basin along 59∘ N. Variability in sea surface temperature presents less spatial structure with an increase westward and towards Newfoundland. The contribution of temperature variability to density dominates in the eastern part of the gyre, whereas the contribution of salinity variability dominates in the southwestern part along AX02.
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Hu, Yue, Xiaoming Sun, Hai Cheng, and Hong Yan. "Evidence from giant-clam <i>δ</i><sup>18</sup>O of intense El Ninõ–Southern Oscillation-related variability but reduced frequency 3700 years ago." Climate of the Past 16, no. 2 (April 1, 2020): 597–610. http://dx.doi.org/10.5194/cp-16-597-2020.
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Abstract. Giant clams (Tridacna) are the largest marine bivalves, and their carbonate shells can be used for high-resolution paleoclimate reconstructions. In this contribution, δ18Oshell was used to estimate climatic variation in the Xisha Islands of the South China Sea. We first evaluate sea surface temperature (SST) and sea surface salinity (SSS) influence on the modern resampled monthly (r-monthly) resolution of Tridacna gigas δ18Oshell. The results obtained reveal that δ18Oshell seasonal variation is mainly controlled by SST and appears to be insensitive to local SSS change. Thus, the δ18O of Tridacna shells can be roughly used as a proxy of local SST: a 1 ‰ δ18Oshell change is roughly equal to 4.41 ∘C of SST. The r-monthly δ18O of a 40-year-old Tridacna squamosa (3673±28 BP) from the North Reef of the Xisha Islands was analyzed and compared with the modern specimen. The difference between the average δ18O of the fossil Tridacna shell (δ18O =-1.34 ‰) and the modern Tridacna specimen (δ18O =-1.15 ‰) probably implies a warm climate, roughly 0.84 ∘C, 3700 years ago. The seasonal variation 3700 years ago was slightly lower than that suggested by modern instrumental data, and the transition between warm and cold seasons was rapid. Higher amplitudes of reconstructed r-monthly and r-annual SST anomalies imply an enhanced climate variability during this warm period. Investigation of the El Ninõ–Southern Oscillation (ENSO) variation (based on the reconstructed SST series) indicates reduced ENSO frequency but increased ENSO-related variability and extreme El Ninõ winter events 3700 years ago.
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Ourbak, T., T. Corrège, B. Malaizé, F. Le Cornec, K. Charlier, and J. P. Peypouquet. "ENSO and interdecadal climate variability over the last century documented by geochemical records of two coral cores from the South West Pacific." Advances in Geosciences 6 (January 9, 2006): 23–27. http://dx.doi.org/10.5194/adgeo-6-23-2006.
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Abstract. The south west Pacific is affected by climatic phenomena such as ENSO (El Niño Southern Oscillation) or the PDO (Pacific Decadal Oscillation). Near-monthly resolution calibrations of Sr/Ca, U/Ca and δ18Oc were made on corals taken from New Caledonia and Wallis Island. These geochemical variations could be linked to SST (sea surface temperature) and SSS (sea surface salinity) variations over the last two decades, itselves dependent on ENSO occurrences. On the other hand, near-half-yearly resolution over the last century smoothes seasonal and interannual climate signals, but emphasizes low frequency climate variability.
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Reverdin, Gilles, Nicolas Metzl, Solveig Olafsdottir, Virginie Racapé, Taro Takahashi, Marion Benetti, Hedinn Valdimarsson, et al. "SURATLANT: a 1993–2017 surface sampling in the central part of the North Atlantic subpolar gyre." Earth System Science Data 10, no. 4 (October 18, 2018): 1901–24. http://dx.doi.org/10.5194/essd-10-1901-2018.
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Abstract. This paper presents the SURATLANT data set (SURveillance ATLANTique). It consists of individual data of temperature, salinity, parameters of the carbonate system, nutrients, and water stable isotopes (δ18O and δD) collected mostly from ships of opportunity since 1993 along transects between Iceland and Newfoundland (https://doi.org/10.17882/54517). We discuss how the data are validated and qualified, their accuracy, and the overall characteristics of the data set. The data are used to reconstruct seasonal cycles and interannual anomalies, in particular of sea surface salinity (SSS); inorganic nutrients; dissolved inorganic carbon (DIC); and its isotopic composition δ13CDIC, total alkalinity (At), and water isotope concentrations. Derived parameters such as fCO2 and pH are also estimated. The relation between salinity and At is estimated from these data to investigate the possibility to replace missing At when estimating other parameters of the carbonate system. When examining the average seasonal cycle in the deep ocean, in both these data with other climatologies, we find a period of small seasonal change between January and late April. On the Newfoundland shelf and continental slope, changes related with spring stratification and blooms occur earlier. The data were collected in a period of multi-decennial variability associated with the Atlantic multi-decadal variability with warming between 1994 and 2004–2007, and with the recent cooling having peaked in 2014–2016. We also observe strong salinification in 2004–2009 and fresher waters in 1994–1995 as well as since 2010 south of 54° N and in 2016–2017 north of 54° N. Indication of multi-decadal variability is also suggested by other variables, such as phosphate or DIC, but cannot be well resolved seasonally with the discrete sampling and in the presence of interannual variability. As a whole, over the 24 years, the ocean fCO2 trend (+1.9 µatm yr−1) is close to the atmospheric trend and associated with an increase in DIC (+0.77 µmol kg−1 yr−1). The data also revealed a canonical pH decrease of −0.0021 yr−1. There is also a decrease in δ13CDIC between 2005 and 2017 (in winter, −0.014 ‰ yr−1, but larger in summer, −0.042 ‰ yr−1), suggesting a significant anthropogenic carbon signal at play together with other processes (mixing, biological activity).
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Wang, Chunzai, Liping Zhang, and Sang-Ki Lee. "Response of Freshwater Flux and Sea Surface Salinity to Variability of the Atlantic Warm Pool." Journal of Climate 26, no. 4 (February 15, 2013): 1249–67. http://dx.doi.org/10.1175/jcli-d-12-00284.1.
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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.
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Yuan, Xu, Xiaolong Yu, and Zhongbo Su. "Seasonal and interannual variabilities of the barrier layer thickness in the tropical Indian Ocean." Ocean Science 16, no. 5 (October 29, 2020): 1285–96. http://dx.doi.org/10.5194/os-16-1285-2020.
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Abstract. The seasonal and interannual variations of the barrier layer thickness (BLT) in the tropical Indian Ocean (TIO) is investigated in this study using the Simple Ocean Data Assimilation version 3 (SODA v3) ocean reanalysis dataset. Analysis of this study suggests energetic but divergent seasonal variabilities of BLT in the western TIO (5∘ N–12∘ S, 55–75∘ E) and the eastern TIO (5∘ N–12∘ S, 85–100∘ E). For instance, the thicker barrier layer (BL) is observed in the western TIO during boreal winter as a result of decreasing sea surface salinity (SSS) and deeper thermocline, which are associated with the intrusion of freshwater flux and the weakened upwelling, respectively. On the contrary, the variation of BLT in the eastern TIO mainly corresponds to the variation in thermocline depth in all seasons. The interannual variability of BLT with the Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO) is explored. During the mature phase of positive IOD events, a thinner BL in the eastern TIO is attributed to the shallower thermocline, while a thicker BL appears in the western TIO due to deeper thermocline and fresher surface water. During negative IOD events, the thicker BL only occurs in the eastern TIO, corresponding to the deeper thermocline. During ENSO events, prominent BLT patterns are observed in the western TIO corresponding to two different physical processes during the developing and decaying phase of El Niño events. During the developing phase of El Niño events, the thicker BL in the western TIO is associated with deepening thermocline induced by the westward Rossby wave. During the decaying phase of El Niño events, the thermocline is weakly deepening, while the BLT reaches its maxima induced by the decreasing SSS.
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Wang, Difeng, Qiyuan Cui, Fang Gong, Lifang Wang, Xianqiang He, and Yan Bai. "Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea." Remote Sensing 10, no. 12 (November 27, 2018): 1896. http://dx.doi.org/10.3390/rs10121896.
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Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (Rrs) were used as inputs in our model. Compared with models that only use Rrs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R2) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved Rrs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen.
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Amarasinghe, U. A., L. Mutuwatte, L. Surinaidu, S. Anand, and S. K. Jain. "Reviving the Ganges Water Machine: why?" Hydrology and Earth System Sciences Discussions 12, no. 9 (September 1, 2015): 8727–59. http://dx.doi.org/10.5194/hessd-12-8727-2015.
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Abstract. The Ganges River Basin may have a major pending water crisis. Although the basin has abundant surface water and groundwater resources, the seasonal monsoon causes a mismatch between supply and demand as well as flooding. Water availability and flood potential is high during the 3–4 months of the monsoon season. Yet, the highest demands occur during the 8–9 months of the non-monsoon period. Addressing this mismatch requires substantial additional storage for both flood reduction and improvements in water supply. Due to hydrogeological, environmental, and social constraints, expansion of surface storage in the Ganges River Basin is problematic. A range of interventions that focus more on the use of subsurface storage (SSS), and on the acceleration of surface–subsurface water exchange, have long been known as the "Ganges Water Machine". One approach for providing such SSS is through additional pumping prior to the onset of the monsoon season. An important necessary condition for creating such SSS is the degree of unmet water demand. This paper highlights that an unmet water demand ranging from 59 to 119 Bm3 exists under two different irrigation water use scenarios: (i) to increase Rabi and hot weather season irrigation to the entire irrigable area, and (ii) to provide Rabi and hot weather season irrigation to the entire cropped area. This paper shows that SSS can enhance water supply, and provide benefits for irrigation and other water use sectors. In addition, it can buffer the inherent variability in water supply and mitigate extreme flooding, especially in the downstream parts of the basin. It can also increase river flow during low-flow months via baseflow or enable the re-allocation of irrigation canal water. Importantly, SSS can mitigate the negative effects of both flooding and water scarcity in the same year, which often affects the most vulnerable segments of society – women and children, the poor and other disadvantaged social groups.
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Busecke, Julius, Ryan P. Abernathey, and Arnold L. Gordon. "Lateral Eddy Mixing in the Subtropical Salinity Maxima of the Global Ocean." Journal of Physical Oceanography 47, no. 4 (April 2017): 737–54. http://dx.doi.org/10.1175/jpo-d-16-0215.1.
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AbstractA suite of observationally driven model experiments is used to investigate the contribution of near-surface lateral eddy mixing to the subtropical surface salinity maxima in the global ocean. Surface fields of salinity are treated as a passive tracer and stirred by surface velocities derived from altimetry, leading to irreversible water-mass transformation. In the absence of surface forcing and vertical processes, the transformation rate can be directly related to the integrated diffusion across tracer contours, which is determined by the observed velocities. The destruction rates of the salinity maxima by lateral mixing can be compared to the production rates by surface forcing, which act to strengthen the maxima. The ratio of destruction by eddy mixing in the surface layer versus the surface forcing exhibits regional differences in the mean—from 10% in the South Pacific to up to 25% in the south Indian. Furthermore, the regional basins show seasonal and interannual variability in eddy mixing. The dominant mechanism for this temporal variability varies regionally. Most notably, the North Pacific shows a large sensitivity to the background salinity fields and a weak sensitivity to the velocity fields, while the North Atlantic exhibits the opposite behavior. The different mechanism for temporal variability could have impacts on the manifestation of a changing hydrological cycle in the sea surface salinity (SSS) field specifically in the North Pacific. The authors find evidence for large-scale interannual changes of eddy diffusivity and transformation rate in several ocean basins that could be related to large-scale climate forcing.
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Yan, Yu, Wei Gu, Andrea M. U. Gierisch, Yingjun Xu, and Petteri Uotila. "NEMO-Bohai 1.0: a high-resolution ocean and sea ice modelling system for the Bohai Sea, China." Geoscientific Model Development 15, no. 3 (February 14, 2022): 1269–88. http://dx.doi.org/10.5194/gmd-15-1269-2022.
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Abstract. Severe ice conditions in the Bohai Sea could cause serious harm to maritime traffic, offshore oil exploitation, aquaculture, and other economic activities in the surrounding regions. In addition to providing sea ice forecasts for disaster prevention and risk mitigation, sea ice numerical models could help explain the sea ice variability within the context of climate change in marine ecosystems, such as spotted seals, which are the only ice-dependent animal that breeds in Chinese waters. Here, we developed NEMO-Bohai, an ocean–ice coupled model based on the Nucleus for European Modelling of the Ocean (NEMO) model version 4.0 and Sea Ice Modelling Integrated Initiative (SI3) (NEMO4.0-SI3) for the Bohai Sea. This study will present the scientific design and technical choices of the parameterizations for the NEMO-Bohai model. The model was calibrated and evaluated with in situ and satellite observations of the ocean and sea ice. The model simulations agree with the observations with respect to sea surface height (SSH), temperature (SST), salinity (SSS), currents, and temperature and salinity stratification. The seasonal variation of the sea ice area is well simulated by the model compared to the satellite remote sensing data for the period of 1996–2017. Overall agreement is found for the occurrence dates of the annual maximum sea ice area. The simulated sea ice thickness and volume are in general agreement with the observations with slight overestimations. NEMO-Bohai can simulate seasonal sea ice evolution and long-term interannual variations. Hence, NEMO-Bohai is a valuable tool for long-term ocean and ice simulations and climate change studies.
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Cao, Jian, Bin Wang, Young-Min Yang, Libin Ma, Juan Li, Bo Sun, Yan Bao, Jie He, Xiao Zhou, and Liguang Wu. "The NUIST Earth System Model (NESM) version 3: description and preliminary evaluation." Geoscientific Model Development 11, no. 7 (July 25, 2018): 2975–93. http://dx.doi.org/10.5194/gmd-11-2975-2018.
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Abstract. The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of high-latitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24 W m−2, yielding a climate sensitivity feedback parameter of −0.98 W m−2 K−1, and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1 % yr−1 CO2 (1pctCO2) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper.
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Walker Brown, C., J. Boutin, and L. Merlivat. "New insights into <i>f</i>CO<sub>2</sub> variability in the tropical eastern Pacific Ocean using SMOS SSS." Biogeosciences 12, no. 23 (December 14, 2015): 7315–29. http://dx.doi.org/10.5194/bg-12-7315-2015.
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Abstract. Complex oceanic circulation and air–sea interaction make the eastern tropical Pacific Ocean (ETPO) a highly variable source of CO2 to the atmosphere. Although the scientific community have amassed 70 000 surface fugacities of carbon dioxide (fCO2) data points within the ETPO region over the past 25 years, the spatial and temporal resolution of this data set is insufficient to fully quantify the seasonal to interannual variability of the region, a region where fCO2 has been observed to fluctuate by > 300 μatm. Upwelling and rainfall events dominate the surface physical and chemical characteristics of the ETPO, with both yielding unique signatures in sea surface temperature and salinity. Thus, we explore the potential of using a statistical description of fCO2 within sea-surface salinity–temperature space. These SSS/SST relationships are based on in situ surface ocean CO2 atlas (SOCAT) data collected within the ETPO. This statistical description is then applied to high-resolution (0.25°) Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) and Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) sea surface temperature (SST) in order to compute regional fCO2. As a result, we are able to resolve fCO2 at sufficiently high resolution to elucidate the influence that various physical processes have on the fCO2 of the surface ETPO. Normalised (to 2014) oceanic fCO2 between July 2010 and June 2014 within the entire ETPO was 39 (±10.7) μatm supersaturated with respect to 2014 atmospheric partial pressures, and featured a CO2 outgassing of 1.51 (±0.41) mmol m−2 d−1. Values of fCO2 within the ETPO were found to be broadly split between the Gulf of Panama region and the rest of the tropical eastern Pacific Ocean. The northwest, central and offshore regions were supersaturated, with wintertime wind-jet-driven upwelling found to constitute the first-order control on fCO2 values. This contrasts with the southeastern/Gulf of Panama region, where heavy rainfall combined with rapid stratification of the upper water column act to dilute dissolved inorganic carbon, and yield fCO2 values undersaturated with respect to atmospheric fugacities of CO2.
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Brown, C. W., J. Boutin, and L. Merlivat. "New insights of <i>p</i>CO<sub>2</sub> variability in the tropical eastern Pacific Ocean using SMOS SSS." Biogeosciences Discussions 12, no. 6 (March 20, 2015): 4595–625. http://dx.doi.org/10.5194/bgd-12-4595-2015.
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Abstract. Complex oceanic circulation and air–sea interaction make the eastern tropical Pacific Ocean (ETPO) a highly variable source of CO2 to the atmosphere. Although the scientific community have amassed 70 000 surface partial-pressure of carbon dioxide (pCO2) datapoints within the ETPO region over the past 25 years, the spatial and temporal resolution of this dataset is insufficient to fully quantify the seasonal to inter-annual variability of the region, a region where pCO2 has been observed to fluctuate by >300 μatm. Upwelling and rainfall events dominate the surface physical and chemical characteristics of the ETPO, with both yielding unique signatures in sea surface temperature and salinity. Thus, we explore the potential of using a statistical description of pCO2 within sea-surface salinity-temperature space. These SSS/SST relationships are based on in-situ SOCAT data collected within the ETPO. This statistical description is then applied to high resolution (0.25°) SMOS sea surface salinity and OSTIA sea surface temperature in order to compute regional pCO2. As a result, we are able to resolve pCO2 at sufficiently high resolution to elucidate the influence various physical processes have on the pCO2 of the surface ETPO. Normalised (to 2014) oceanic pCO2 between July 2010 and June 2014 within the entire ETPO was 41 μatm supersaturated with respect to 2014 atmospheric partial pressures. Values of pCO2 within the ETPO were found to be broadly split between southeast and a northwest regions. The north west, central and South Equatorial Current regions were supersaturated, with wintertime wind jet driven upwelling found to be the first order control on pCO2 values. This contrasts with the southeastern/Gulf of Panama region, where heavy rainfall combined with rapid stratification of the upper water-column act to dilute dissolved inorganic carbon, and yield pCO2 values undersaturated with respect to atmospheric partial pressures of CO2.
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Wrobel-Niedzwiecka, Iwona, Małgorzata Kitowska, Przemyslaw Makuch, and Piotr Markuszewski. "The Distribution of pCO2W and Air-Sea CO2 Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean." Remote Sensing 14, no. 2 (January 11, 2022): 312. http://dx.doi.org/10.3390/rs14020312.
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A feed-forward neural network (FFNN) was used to estimate the monthly climatology of partial pressure of CO2 (pCO2W) at a spatial resolution of 1° latitude by 1° longitude in the continental shelf of the European Arctic Sector (EAS) of the Arctic Ocean (the Greenland, Norwegian, and Barents seas). The predictors of the network were sea surface temperature (SST), sea surface salinity (SSS), the upper ocean mixed-layer depth (MLD), and chlorophyll-a concentration (Chl-a), and as a target, we used 2 853 pCO2W data points from the Surface Ocean CO2 Atlas. We built an FFNN based on three major datasets that differed in the Chl-a concentration data used to choose the best model to reproduce the spatial distribution and temporal variability of pCO2W. Using all physical–biological components improved estimates of the pCO2W and decreased the biases, even though Chl-a values in many grid cells were interpolated values. General features of pCO2W distribution were reproduced with very good accuracy, but the network underestimated pCO2W in the winter and overestimated pCO2W values in the summer. The results show that the model that contains interpolating Chl-a concentration, SST, SSS, and MLD as a target to predict the spatiotemporal distribution of pCO2W in the sea surface gives the best results and best-fitting network to the observational data. The calculation of monthly drivers of the estimated pCO2W change within continental shelf areas of the EAS confirms the major impact of not only the biological effects to the pCO2W distribution and Air-Sea CO2 flux in the EAS, but also the strong impact of the upper ocean mixing. A strong seasonal correlation between predictor and pCO2W seen earlier in the North Atlantic is clearly a yearly correlation in the EAS. The five-year monthly mean CO2 flux distribution shows that all continental shelf areas of the Arctic Ocean were net CO2 sinks. Strong monthly CO2 influx to the Arctic Ocean through the Greenland and Barents Seas (>12 gC m−2 day−1) occurred in the fall and winter, when the pCO2W level at the sea surface was high (>360 µatm) and the strongest wind speed (>12 ms−1) was present.
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Denvil-Sommer, Anna, Marion Gehlen, Mathieu Vrac, and Carlos Mejia. "LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean <i>p</i>CO<sub>2</sub> over the global ocean." Geoscientific Model Development 12, no. 5 (May 29, 2019): 2091–105. http://dx.doi.org/10.5194/gmd-12-2091-2019.
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Abstract. A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO2 climatology, and (2) the reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1∘×1∘ grid for the period from 2001 to 2016. Global ocean pCO2 was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea–air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.
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Sangelantoni, Lorenzo, Antonio Ricchi, Rossella Ferretti, and Gianluca Redaelli. "Dynamical Downscaling in Seasonal Climate Forecasts: Comparison between RegCM- and WRF-Based Approaches." Atmosphere 12, no. 6 (June 10, 2021): 757. http://dx.doi.org/10.3390/atmos12060757.
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The purpose of the present study is to assess the large-scale signal modulation produced by two dynamically downscaled Seasonal Forecasting Systems (SFSs) and investigate if additional predictive skill can be achieved, compared to the driving global-scale Climate Forecast System (CFS). The two downscaled SFSs are evaluated and compared in terms of physical values and anomaly interannual variability. Downscaled SFSs consist of two two-step dynamical downscaled ensembles of NCEP-CFSv2 re-forecasts. In the first step, the CFS field is downscaled from 100 km to 60 km over Southern Europe (D01). The second downscaling, driven by the corresponding D01, is performed at 12 km over Central Italy (D02). Downscaling is performed using two different Regional Climate Models (RCMs): RegCM v.4 and WRF 3.9.1.1. SFS skills are assessed over a period of 21 winter seasons (1982–2002), by means of deterministic and probabilistic approach and with a metric specifically designed to isolate downscaling signal over different percentiles of distribution. Considering the temperature fields and both deterministic and probabilistic metrics, regional-scale SFSs consistently improve the original CFS Seasonal Anomaly Signal (SAS). For the precipitation, the added value of downscaled SFSs is mainly limited to the topography driven refinement of precipitation field, whereas the SAS is mainly “inherited” by the driving CFS. The regional-scale SFSs do not seem to benefit from the second downscaling (D01 to D02) in terms of SAS improvement. Finally, WRF and RegCM show substantial differences in both SAS and climatologically averaged fields, highlighting a different impact of the common SST driving field.
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Javari, Majid. "Spatial-temporal Variability of Seasonal Precipitation in Iran." Open Atmospheric Science Journal 10, no. 1 (December 26, 2016): 84–102. http://dx.doi.org/10.2174/1874282301610010084.
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Spatial-seasonal variability and temporal trends has essential importance to climatic prediction and analysis. The aim of this research is the seasonal variations and temporal trends in the Iran were predicted by using rainfall series. The exploratory-confirmatory method, and seasonal time series procedure (STSP), temporal trend (TT), seasonal least squares (SLS) and spatial (GIS) methods (STSP¬-SLS-GIS) were employed to bring to light rainfall spatial-seasonal variability and temporal trends (SSVTT). To explore the spatial-seasonal variability and temporal trends during the period over 1975 to 2014 at 140 stations. To investigate the spatial-seasonal variability and temporal trends amount of each series was studied using ArcGIS 10.3 on different time scale. New climatic findings for the region: the investigates and predictions revealed that: (a) range of monthly and seasonal changes of rainfall tends to be highest (increasing trend) during winter (Winter Seasonal Index or WUSI=137.83 mm); (b) lowest (decreasing trend) during summer (Summer Seasonal Index or SUSI=20.8l mm) and (c) the coefficient of rainfall seasonal pattern variations in winter to 5.94 mm, in spring to 11.13 mm, in summer to 4.44 mm and in autumn to 8.05 mm with seasonality being the most effective of all. Mean annual rainfall changed from 51.45 mm (at Bafgh) to 1834.9 mm (at Bandar Anzali). Maximum decrease in annual rainfall was obtained at Miandeh Jiroft (-143.83%) and minimum at Abali (-0.013%) station. The most apparent year of variation was 2007 in annual rainfall.
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Infante Ramírez, Karla Diana, and Ana Minerva Arce Ibarra. "Less Rain and More Heat”: Smallholders’ Perception and Climate Change Adaptation Strategies in Tropical Environments." Sociedad y Ambiente, no. 21 (November 1, 2019): 77–104. http://dx.doi.org/10.31840/sya.v0i21.2040.
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The main objective of this study was to analyze local perceptions of climate variability and the different adaptation strategies of four communities in the southern Yucatán Peninsula, using the Social-Ecological System (SES) approach. Four SESs were considered: two in the coastal zone and two in the tropical forest zone. Data were collected using different qualitative methodological tools (interviews, participant observation, and focal groups) and the information collected from each site was triangulated. In all four sites, changes in climate variability were perceived as “less rain and more heat”. In the tropical forest (or Maya) zone, an ancestral indigenous weather forecasting system, known as “Xook k’íin” (or “las cabañuelas”), was recorded and the main activity affected by climate variability was found to be slash-and burn farming or the milpa. In the coastal zone, the main activities affected are fishing and tourism. In all the cases analyzed, local climate change adaptation strategies include undertaking alternative work, and changing the calendar of daily, seasonal and annual labor and seasonal migration. The population of all four SESs displayed concern and uncertainty as regards dealing with these changes and possible changes in the future.
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Bingham, F. M., G. R. Foltz, and M. J. McPhaden. "Characteristics of the seasonal cycle of surface layer salinity in the global ocean." Ocean Science 8, no. 5 (October 30, 2012): 915–29. http://dx.doi.org/10.5194/os-8-915-2012.
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Abstract. The seasonal variability of surface layer salinity (SLS), evaporation (E), precipitation (P), E-P, advection and vertical entrainment over the global ocean is examined using in situ salinity data, the National Centers for Environmental Prediction's Climate System Forecast Reanalysis and a number of other ancillary data. Seasonal amplitudes and phases are calculated using harmonic analysis and presented in all areas of the open ocean between 60° S and 60° N. Areas with large amplitude SLS seasonal variations include: the intertropical convergence zone (ITCZ) in the Atlantic, Pacific and Indian Oceans; western marginal seas of the Pacific; and the Arabian Sea. The median amplitude in areas that have statistically significant seasonal cycles of SLS is 0.19. Between about 60° S and 60° N, 37% of the ocean surface has a statistically significant seasonal cycle of SLS and 75% has a seasonal cycle of E-P. Phases of SLS have a bimodal distribution, with most areas in the Northern Hemisphere peaking in SLS in March/April and in the Southern Hemisphere in September/October. The seasonal cycle is also estimated for surface freshwater forcing using a mixed-layer depth climatology. With the exception of areas near the western boundaries of the North Atlantic and North Pacific, seasonal variability is dominated by precipitation. Surface freshwater forcing also has a bimodal distribution, with peaks in January and July, 1–2 months before the peaks of SLS. Seasonal amplitudes and phases calculated for horizontal advection show it to be important in the tropical oceans. Vertical entrainment, estimated from mixed-layer heaving, is largest in mid and high latitudes, with a seasonal cycle that peaks in late winter. The amplitudes and phases of SLS and surface fluxes compare well in a qualitative sense, suggesting that much of the variability in SLS is due to E-P. However, the amplitudes of SLS are somewhat different than would be expected and the peak of SLS comes typically about one month earlier than expected. The differences of the amplitudes of the two quantities is largest in such areas as the Amazon River plume, the Arabian Sea, the ITCZ and the eastern equatorial Pacific and Atlantic.
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Bingham, F. M., G. R. Foltz, and M. J. McPhaden. "Characteristics of the seasonal cycle of surface layer salinity in the global ocean." Ocean Science Discussions 8, no. 6 (December 7, 2011): 2377–415. http://dx.doi.org/10.5194/osd-8-2377-2011.
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Abstract. The seasonal variability of surface layer salinity (SLS), evaporation (E), precipitation (P) and E-P over the global ocean is examined using in situ salinity data and the National Center for Environmental Prediction's Climate System Forecast Reanalysis. Seasonal amplitudes and phases are calculated using harmonic analysis and presented in all areas of the open ocean between 60° S and 60° N. Areas with large amplitude SLS seasonal variations include: the intertropical convergence zone in the Atlantic, Pacific and Indian Oceans; western marginal seas of the Pacific; and the Arabian Sea. The median value in areas that have statistically significant seasonal cycles of SLS is 0.19. Between about 60° S and 60° N, 37 % of the ocean surface has a significant seasonal cycle of SLS and 75 % a seasonal cycle of E-P. Phases of SLS have a bimodal distribution, with most areas of the ocean peaking in SLS in either March/April or September/October. The same calculation is done with surface freshwater flux using a mixed-layer depth climatology. With the exception of an area near the western boundaries of the North Atlantic and North Pacific, seasonal variability is dominated by precipitation. Surface freshwater fluxes also have a bimodal distribution, with peaks in January and July, 1–2 months before the peaks of SLS. The amplitudes and phases of SLS and surface fluxes compare well in a qualitative sense, suggesting that much of the variability in SLS is due to E-P forcing. However, the amplitudes of SLS are somewhat larger than would be expected and the peak of SLS comes typically about one month earlier than expected. The differences of the amplitudes of the two quantities is largest in such areas as the Amazon River plume, the Arabian Sea, the ITCZ and the eastern equatorial Pacific and Atlantic, indicating that other processes such as ocean mixing and lateral transport must be important, especially in the tropics.
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Xu, Wenlong, Guifen Wang, Long Jiang, Xuhua Cheng, Wen Zhou, and Wenxi Cao. "Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data." Remote Sensing 13, no. 1 (December 23, 2020): 30. http://dx.doi.org/10.3390/rs13010030.
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The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.
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Liu, Guimei, and Fei Chai. "Seasonal and interannual variability of primary and export production in the South China Sea: a three-dimensional physical–biogeochemical model study." ICES Journal of Marine Science 66, no. 2 (January 18, 2009): 420–31. http://dx.doi.org/10.1093/icesjms/fsn219.
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Abstract Liu, G., and Chai, F. 2009. Seasonal and interannual variability of primary and export production in the South China Sea: a three-dimensional physical–biogeochemical model study. – ICES Journal of Marine Science, 66: 420–431. To investigate the seasonal and interannual variations in biological productivity in the South China Sea (SCS), a Pacific basin-wide physical–biogeochemical model has been developed and used to estimate the biological productivity and export flux in the SCS. The Pacific circulation model, based on the Regional Ocean Model Systems (ROMS), is forced with daily air–sea fluxes derived from the NCEP (National Centers for Environmental Prediction) reanalysis between 1990 and 2004. The biogeochemical processes are simulated with a carbon, Si(OH)4, and nitrogen ecosystem (CoSiNE) model consisting of silicate, nitrate, ammonium, two phytoplankton groups (small phytoplankton and large phytoplankton), two zooplankton grazers (small micrograzers and large mesozooplankton), and two detritus pools. The ROMS–CoSiNE model favourably reproduces many of the observed features, such as Chl a, nutrients, and primary production (PP) in the SCS. The modelled depth-integrated PP over the euphotic zone (0–125 m) varies seasonally, with the highest value of 386 mg C m−2 d−1 during winter and the lowest value of 156 mg C m−2 d−1 during early summer. The annual mean value is 196 mg C m−2 d−1. The model-integrated annual mean new production (uptake of nitrate), in carbon units, is 64.4 mg C m−2 d−1, which yields an f-ratio of 0.33 for the entire SCS. The modelled export ratio (e-ratio: the ratio of export to PP) is 0.24 for the basin-wide SCS. The year-to-year variation of biological productivity in the SCS is weaker than the seasonal variation. The large phytoplankton group tends to dominate over the smaller phytoplankton group, and likely plays an important role in determining the interannual variability of primary and new production.
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Wang, A., Y. Du, W. Zhuang, and Y. Qi. "Correlation between subsurface high-salinity water in the northern South China Sea and the North Equatorial Current–Kuroshio circulation system from HYCOM simulations." Ocean Science 11, no. 2 (April 1, 2015): 305–12. http://dx.doi.org/10.5194/os-11-305-2015.
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Abstract. The North Pacific Tropical Water (NPTW), characterized by subsurface high salinity, is observed in the South China Sea (SCS) and is often used as an indicator of the water intrusion from the northwestern Pacific into the SCS. Based on the assimilation product from a global high-resolution Hybrid Coordinate Ocean Model (HYCOM) from 2008 through 2013, this study investigates the seasonal variability of subsurface high-salinity water (SHSW) in the northern SCS and its relationship with the North Equatorial Current–Kuroshio circulation system. Results show that the obvious seasonal variability of the SHSW appears at about 100–200 m in depth. It extends as far west as southeast of Hainan, reaching its volume maximum (minimum) in January (May). The seasonal variance contribution (seasonal variance accounting for the entire variance) is 0.38 in the period we considered, albeit with significant annual variance in other years. Further analysis shows that the changes in high-salinity water volume are highly correlated with the shift in the North Equatorial Current bifurcation latitude (NECBL), which reaches its northernmost point in December and its southernmost point in May. Due to the large-scale wind changes in the Pacific, the Luzon Strait transport (LST) weakens (strengthens) when the NECBL shifts to the south (north) during summer (winter), which results in the reduced (enhanced) SHSW intrusion from the northwestern Pacific into the northern SCS. It is also found that, on a seasonal timescale, the Kuroshio transport (KT) does not vary in phase with NECBL, LST and SHSW, indicating that the KT changes are probably not the governing factor for the seasonal variability of SHSW in the northern SCS.
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Wang, A., Y. Du, W. Zhuang, and Y. Qi. "Seasonal variability of subsurface high salinity water in the northern South China Sea and its relationship with the northwestern Pacific currents." Ocean Science Discussions 11, no. 5 (October 28, 2014): 2423–46. http://dx.doi.org/10.5194/osd-11-2423-2014.
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Abstract. The North Pacific Tropical Water (NPTW), characterized by the subsurface high salinity (> 34.68 PSU), is observed in the South China Sea (SCS) and often used as an indicator of the water intrusion from the northwestern Pacific into the SCS. Based on the assimilation product from a global high-resolution Hybrid Coordinate Ocean Model (HYCOM), this study investigates the seasonal variability of subsurface high salinity water (SHSW) in the northern SCS and the influence from the northwestern Pacific. Results show that there exists obvious seasonal variability in the SHSW at about 100–200 m depth. It extends as far west as 111° E in the northern SCS, reaching its volume maximum (minimum) in January (May). Further analysis shows that the seasonal change of the high salinity water is strongly affected by the seasonal variability of large-scale circulations in the low-latitude northwestern Pacific. The changes of high salinity water volume are highly correlated with the shift of the North Equatorial Current (NEC) bifurcation latitude (NECBL), which reaches the northernmost in December and the southernmost in May. Due to the large-scale wind changes in the Pacific, the Luzon Strait transport weakens (strengthens) when the NECBL shifts to the south (north) during summer (winter), which results in the reduced (enhanced) SHSW intrusion from the northwestern Pacific into the northern SCS. The velocity and salinity distribution in the Luzon Strait show that the intrusion of the SHSW mainly occurs at around 20–21.3° N.
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Sangelantoni, Ferretti, and Redaelli. "Toward a Regional-Scale Seasonal Climate Prediction System over Central Italy based on Dynamical Downscaling." Climate 7, no. 10 (October 5, 2019): 120. http://dx.doi.org/10.3390/cli7100120.
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Anticipating seasonal climate anomalies is essential for defining short-term adaptation measures. To be actionable, many stakeholders require seasonal forecasts at the regional scale to be properly coupled to region-specific vulnerabilities. In this study, we present and preliminarily evaluate a regional-scale Seasonal Forecast System (SFS) over Central Italy. This system relies on a double dynamical downscaling performed through the Regional-scale Climate Model (RCM) RegCM4.1. A twelve-member ensemble of the NCEP-CFSv2 provides driving fields for the RegCM. In the first step, the RegCM dynamically downscales NCEP-CFSv2 predictions from a resolution of 100 to 60 km over Europe (RegCM-d1). This first downscaling drives a second downscaling over Central Italy at 12 km (RegCM-d2). To investigate the added value of the downscaled forecasts compared to the driving NCEP-CFSv2, we evaluate the driving CFS, and the two downscaled SFSs over the same (inner) domain. Evaluation involves winter temperatures and precipitations over a climatological period (1982–2003). Evaluation for mean bias, statistical distribution, inter-annual anomaly variability, and hit-rate of anomalous seasons are shown and discussed. Results highlight temperature physical values reproduction benefiting from the downscaling. Downscaled inter-annual variability and probabilistic metrics show improvement mainly at forecast lead-time 1. Downscaled precipitation shows an improved spatial distribution with an undegraded but not improved seasonal forecast quality.
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Bingham, F. M., G. R. Foltz, and M. J. McPhaden. "Seasonal cycles of surface layer salinity in the Pacific Ocean." Ocean Science 6, no. 3 (August 24, 2010): 775–87. http://dx.doi.org/10.5194/os-6-775-2010.
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Abstract. The seasonal variability of surface layer salinity (SLS) is examined in the Pacific Ocean between 40° S and 60° N using a variety of data sources. Significant seasonal cycles were found in 5 regions: 1) The western North Pacific, 2) The northeastern North Pacific and Alaska gyre, 3) the intertropical convergence zone (ITCZ), 4) an area of the central North Pacific north of the Hawaiian Islands, 5) the central South Pacific along 10–20° S. Amplitudes range from 0.1 to > 0.5. The largest amplitudes are in the tropical band and the western North Pacific. Maximum salinity is obtained in late (northern) winter in the western North Pacific, late winter and early spring in the northeastern North Pacific, early summer in the ITCZ area, late summer and early fall in the central North Pacific area and (austral) winter in the central South Pacific. Large areas of the Pacific have no significant seasonal variation in SLS. Seasonal variability of evaporation rate, precipitation rate and the difference between them (E-P) were calculated from the OAFlux and Global Precipitation Climatology Project datasets. Typical amplitudes of E-P are 0.1–1 × 10−4 kg m−2 s−1. The seasonal variability of E-P is largely dominated by variability in evaporation in the western North Pacific and precipitation elsewhere. The largest amplitudes are in areas along the edge of the western North Pacific and in the far eastern tropical Pacific around 10° N. Phases in these areas indicate maximum E-P in mid- to late winter in these areas of large amplitude. The closest correspondence between E-P and SLS is in the ITCZ. E-P was combined with seasonal variation of the mixed-layer depth to calculate the freshwater flux forcing term of the SLS balance equation. The term was found to be similar in magnitude and distribution to E-P. Some other terms of the SLS balance were calculated. Horizontal advection was found to have seasonal cycles in a region near the equator. Entrainment was found to be mostly not significant except for a small region along 2.5–7.5° N in the eastern Pacific. Averaged spatially over large areas in the western North Pacific, ITCZ, South Pacific and northern North Pacific, the seasonal cycle is mostly a balance between changes in SLS and E-P, with entrainment and advection playing relatively minor roles. This work highlights the potentially significant role of surface salinity in the hydrologic cycle and in subtropical mode water formation. It can also help to interpret measurements that will soon be available from the Aquarius and SMOS (Soil Moisture and Ocean Salinity) satellite missions.
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Zhou, Chun, Wei Zhao, Jiwei Tian, Qingxuan Yang, and Tangdong Qu. "Variability of the Deep-Water Overflow in the Luzon Strait*." Journal of Physical Oceanography 44, no. 11 (November 1, 2014): 2972–86. http://dx.doi.org/10.1175/jpo-d-14-0113.1.
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Abstract The Luzon Strait, with its deepest sills at the Bashi Channel and Luzon Trough, is the only deep connection between the Pacific Ocean and the South China Sea (SCS). To investigate the deep-water overflow through the Luzon Strait, 3.5 yr of continuous mooring observations have been conducted in the deep Bashi Channel and Luzon Trough. For the first time these observations enable us to assess the detailed variability of the deep-water overflow from the Pacific to the SCS. On average, the along-stream velocity of the overflow is at its maximum at about 120 m above the ocean bottom, reaching 19.9 ± 6.5 and 23.0 ± 11.8 cm s−1 at the central Bashi Channel and Luzon Trough, respectively. The velocity measurements can be translated to a mean volume transport for the deep-water overflow of 0.83 ± 0.46 Sverdrups (Sv; 1 Sv ≡ 106 m3 s−1) at the Bashi Channel and 0.88 ± 0.77 Sv at the Luzon Trough. Significant intraseasonal and seasonal variations are identified, with their dominant time scales ranging between 20 and 60 days and around 100 days. The intraseasonal variation is season dependent, with its maximum strength taking place in March–May. Deep-water eddies are believed to play a role in this intraseasonal variation. On the seasonal time scale, the deep-water overflow intensifies in late fall (October–December) and weakens in spring (March–May), corresponding well with the seasonal variation of the density difference between the Pacific and SCS, for which enhanced mixing in the deep SCS is possibly responsible.
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Li, Fangyuan, Jingyao Wang, Tingting Cao, and Chongwei Cui. "Spatial and seasonal variability of water quality in the Mopanshan Reservoir (Harbin, Northern China)." Water Supply 17, no. 2 (September 3, 2016): 389–98. http://dx.doi.org/10.2166/ws.2016.118.
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The purpose of this work is to evaluate the spatial and seasonal variability of water quality from the Mopanshan Reservoir, which is a typical reservoir in the northern cold regions of China. The results indicate that preventive or remediation actions are necessary to improve its water quality. Water samples were collected between 2012 and 2013 at five sampling stations at the Mopanshan Reservoir, and analyzed for CODMn and total nitrogen (TN). SPSS software was used to carry out analysis of variance and correlation analysis. Spatially, CODMn and TN exhibited a rather small distinction in the horizontal direction, but there was a significant difference regarding TN in the vertical orientation. The concentration of TN also increased with the increase in sampling depth. Seasonally, the concentration of CODMn and TN showed a pronounced seasonal pattern and was divided into four periods. CODMn reached a maximum in September and was at a minimum in June. And TN reached a maximum in June and was at a minimum in November or December. The use of correlation analysis shows that the regular variations of TN were primarily affected by temperature. The main form of nitrogen in the Mopanshan Reservoir is NO3-N, and the change of TN is consistent with that of NO3-N. By Pearson correlation coefficient, the seasonal variability of CODMn correlated to changes in the reservoir's water level. The results showed that the concentration of TN exceeded the guideline values in most months and CODMn was slightly over the standard limit. Thus, it is urgent that preventive actions and remediation processes are developed to improve the water quality in this reservoir.
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Hu, Wenting, and Renguang Wu. "Air–Sea Interaction in Association with Monthly Anomaly Departure over the Western North Pacific and Tropical Indian Ocean during the Spring-to-Summer Transition." Journal of Climate 29, no. 6 (March 7, 2016): 2095–108. http://dx.doi.org/10.1175/jcli-d-15-0461.1.
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Abstract The study analyzes precipitation variability and related air–sea interaction processes over the South China Sea (SCS) and tropical Indian Ocean (TIO) during the spring-to-summer transition season. It is found that physical processes are very different for the variations of seasonal mean and the monthly departures from the seasonal mean. Corresponding to the seasonal mean anomaly, remote forcing from the equatorial Pacific is a major factor for the precipitation variability with a prominent negative feedback of the atmosphere on the ocean. However, from the viewpoint of the monthly anomaly departure from the seasonal mean, a pronounced local coupled air–sea interaction is detected in both the SCS and TIO that features a sequential process of less rainfall, higher sea surface temperature (SST), more rainfall, and lower SST. The evolution of the SST tendency is well coordinated with that of net surface heat flux in the SCS and TIO. During the transition season, shortwave radiation is a dominant term for the SST change in the SCS, whereas both shortwave radiation and latent heat flux are responsible for the SST change in the TIO. The local air–sea relationship shows an obvious spatiotemporal variation during the transition season. Furthermore, the SST anomaly departure in the TIO (SCS) in April (May) could be considered as an indicator for local precipitation anomaly departure in May (June).
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Li, Qiang, Lei Zhou, and Lingling Xie. "Seasonal and Interannual Variability of EAPE in the South China Sea Derived from ECCO2 Data from 1997 to 2019." Water 13, no. 7 (March 28, 2021): 926. http://dx.doi.org/10.3390/w13070926.
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Using Estimating the Circulation and Climate of the Ocean (phase 2, ECCO2) reanalysis products from 1997 to 2019, this study analyzes the spatiotemporal features of the eddy available gravitational potential energy (EAPE) in the South China Sea (SCS). The results indicate that the EAPE accounts for 64% of the total APE in the SCS with the climatological mean. The 2D EAPE distribution images manifest show high-value regions which are generally consistent with the eddy distributions. One region is located around 21° N and west of the Luzon Strait, the second around 17° N and near Luzon Island, and the third off the Vietnam coast. In the region around 21° N and 17° N, both the seasonal variability and the interannual variability associated with the El Niño–Southern Oscillation (ENSO) are significant. Off the Vietnam coast, the EAPE is closely associated with coastal processes which heavily depend on the seasonal monsoon, the El Nino/La Nina events, and the Indian Ocean Dipole (IOD). The results provide new insights into SCS dynamics from the point of view of ocean energy sources.
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Peings, Y., Y. Lim, and G. Magnusdottir. "Potential Predictability of Southwest U.S. Rainfall: Role of Tropical and High-Latitude Variability." Journal of Climate 35, no. 6 (March 15, 2022): 1697–717. http://dx.doi.org/10.1175/jcli-d-21-0775.1.
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Abstract This study explores the potential predictability of Southwest U.S. (SWUS) precipitation for the November–March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden–Julian oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño–Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely. Significance Statement Subseasonal and seasonal predictability of precipitation over the Southwest United States (SWUS) during the wet season is challenging, and long-range forecasts from climate models still exhibit poor skill over this region. In this study we use numerical experiments with constrained tropical and/or Arctic atmospheric variability to explore how climate processes in these two regions impact the SWUS precipitation. Our results highlight how much forecast skill in SWUS precipitation may be gained from better predictions in tropical and high latitudes, from subseasonal to multiyear time scales.
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Kumar, Varun, Abhay Singh, Mrinmoy Adhikary, Shailaja Daral, Anita Khokhar, and Saudan Singh. "Seasonality of Tuberculosis in Delhi, India: A Time Series Analysis." Tuberculosis Research and Treatment 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/514093.
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Background. It is highly cost effective to detect a seasonal trend in tuberculosis in order to optimize disease control and intervention. Although seasonal variation of tuberculosis has been reported from different parts of the world, no definite and consistent pattern has been observed. Therefore, the study was designed to find the seasonal variation of tuberculosis in Delhi, India.Methods. Retrospective record based study was undertaken in a Directly Observed Treatment Short course (DOTS) centre located in the south district of Delhi. Six-year data from January 2007 to December 2012 was analyzed. Expert modeler of SPSS ver. 21 software was used to fit the best suitable model for the time series data.Results. Autocorrelation function (ACF) and partial autocorrelation function (PACF) at lag 12 show significant peak suggesting seasonal component of the TB series. Seasonal adjusted factor (SAF) showed peak seasonal variation from March to May. Univariate model by expert modeler in the SPSS showed that Winter’s multiplicative model could best predict the time series data with 69.8% variability. The forecast shows declining trend with seasonality.Conclusion. A seasonal pattern and declining trend with variable amplitudes of fluctuation were observed in the incidence of tuberculosis.
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Shao, Caixia, Weimin Zhang, Chunjian Sun, Xinmin Chai, and Zhimin Wang. "Statistical Prediction of the South China Sea Surface Height Anomaly." Advances in Meteorology 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/907313.
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Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007.
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Fu, Y., X. Zhou, D. Zhou, W. Sun, and C. Jiang. "SEA LEVEL TREND AND VARIABILITY IN THE SOUTH CHINA SEA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 589–93. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-589-2019.
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<p><strong>Abstract.</strong> Sea level rise due to climate change is nonuniform globally, necessitating regional estimates. Spatial-temporal behaviour of sea level anomalies (SLAs) in the South China Sea (SCS) was investigated over 24 years period from 1993 to 2016. Based on the spatial distribution of monthly and seasonal mean SLAs in the SCS, the strong regularity of SLAs performed maybe mainly predominantly driven by monsoonal wind. Variations of SLA were different in each month, which was the largest in July and December, and the smallest in April. Positive sea level linear trends were estimated in most cases. The averaged sea level trend in the SCS showed a rise of 4.42&thinsp;&plusmn;&thinsp;0.25&thinsp;mm/year from 1993 to 2016. Further investigations are expected from muliti-resources such as ENSO, wind stress, and vertical land movement data.</p>