scholarly journals An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐ a based models

2015 ◽  
Vol 120 (9) ◽  
pp. 6508-6541 ◽  
Author(s):  
Younjoo J. Lee ◽  
Patricia A. Matrai ◽  
Marjorie A. M. Friedrichs ◽  
Vincent S. Saba ◽  
David Antoine ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Chlorophyll A ◽  
Primary Productivity ◽  
Ocean Color ◽  
The Arctic ◽  
The Arctic Ocean ◽  
Satellite Ocean Color

Assessment of Temperature and Specific Humidity Inversions and Their Relationships in Three Global Reanalysis Products over the Arctic Ocean

2021 ◽  
Vol 60 (4) ◽  
pp. 493-511
Author(s):  
Liang Chang ◽  
Shiqiang Wen ◽  
Guoping Gao ◽  
Zhen Han ◽  
Guiping Feng ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Specific Humidity ◽  
The Arctic ◽  
Weather Forecasts ◽  
The Arctic Ocean ◽  
Temperature Inversions ◽  
Global Reanalysis ◽  
Less Common Multiple ◽  
Better Than

AbstractCharacteristics of temperature inversions (TIs) and specific humidity inversions (SHIs) and their relationships in three of the latest global reanalyses—the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-I), the Japanese 55-year Reanalysis (JRA-55), and the ERA5—are assessed against in situ radiosonde (RS) measurements from two expeditions over the Arctic Ocean. All reanalyses tend to detect many fewer TI and SHI occurrences, together with much less common multiple TIs and SHIs per profile than are seen in the RS data in summer 2008, winter 2015, and spring 2015. The reanalyses generally depict well the relationships among TI characteristics seen in RS data, except for the TIs below 400 m in summer, as well as above 1000 m in summer and winter. The depth is simulated worst by the reanalyses among the SHI characteristics, which may result from its sensitivity to the uncertainties in specific humidity in the reanalyses. The strongest TI per profile in RS data exhibits more robust dependency on surface conditions than the strongest SHI per profile, and the former is better presented by the reanalyses than the latter. Furthermore, all reanalyses have difficulties simulating the relationships between TIs and SHIs, together with the correlations between the simultaneous inversions. The accuracy and vertical resolution in the reanalyses are both important to properly capture occurrence and characteristics of the Arctic inversions. In general, ERA5 performs better than ERA-I and JRA-55 in depicting the relationships among the TIs. However, the representation of SHIs is more challenging than TIs in all reanalyses over the Arctic Ocean.

scholarly journals Using ship-borne observations of methane isotopic ratio in the Arctic Ocean to understand methane sources in the Arctic

2019 ◽  
Author(s):  
Antoine Berchet ◽  
Isabelle Pison ◽  
Patrick M. Crill ◽  
Brett Thornton ◽  
Philippe Bousquet ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Large Scale ◽  
Transport Model ◽  
Isotopic Ratio ◽  
The Arctic ◽  
Isotopic Ratios ◽  
Air Masses ◽  
Remote Areas ◽  
The Arctic Ocean

Abstract. Due to the large variety and heterogeneity of sources in remote areas hard to document, the Arctic regional methane budget remain very uncertain. In situ campaigns provide valuable data sets to reduce these uncertainties. Here we analyse data from the SWERUS-C3 campaign, on-board the icebreaker Oden, that took place during summer 2014 in the Arctic Ocean along the Northern Siberian and Alaskan shores. Total concentrations of methane, as well as isotopic ratios were measured continuously during this campaign for 35 days in July and August 2014. Using a chemistry-transport model, we link observed concentrations and isotopic ratios to regional emissions and hemispheric transport structures. A simple inversion system helped constraining source signatures from wetlands in Siberia and Alaska and oceanic sources, as well as the isotopic composition of lower stratosphere air masses. The variation in the signature of low stratosphere air masses, due to strongly fractionating chemical reactions in the stratosphere, was suggested to explain a large share of the observed variability in isotopic ratios. These points at required efforts to better simulate large scale transport and chemistry patterns to use isotopic data in remote areas. It is found that constant and homogeneous source signatures for each type of emission in the region (mostly wetlands and oil and gas industry) is not compatible with the strong synoptic isotopic signal observed in the Arctic. A regional gradient in source signatures is highlighted between Siberian and Alaskan wetlands, the later ones having a lighter signatures than the first ones. Arctic continental shelf sources are suggested to be a mixture of methane from a dominant thermogenic origin and a secondary biogenic one, consistent with previous in-situ isotopic analysis of seepage along the Siberian shores.

INTAROS synthesis of gap analysis of the existing Arctic observing systems

2020 ◽  
Author(s):  
Roberta Pirazzini ◽  
Michael Tjernström ◽  
Stein Sandven ◽  
Hanne Sagen ◽  
Torill Hamre ◽  
...  
Keyword(s):  
Arctic Ocean ◽  
Gap Analysis ◽  
Satellite Observations ◽  
The Arctic ◽  
Reference Level ◽  
Data Handling ◽  
Network Concept ◽  
The Arctic Ocean ◽  
Observing Systems

<p>A comprehensive assessment of a substantial subset of Arctic observing systems, data collections and satellite products across scientific disciplines was carried out in INTAROS, also including data repositories and a brief scientific gap analysis. The assessments cover a multitude of aspects such as sustainability, technical maturity and data handling for the entire chain from observation to users, including metadata procedures and availability to data. Community based environment monitoring programs were surveyed and assessed separately; they do not form part of the present assessment.</p><p>The assessed observing systems were first ranked according to general sustainability and other aspects, were analyzed subsequently. While the range of sustainability is large, it was found that high scores on all other aspects, such as for data handling and technical maturity, are more likely for systems with high sustainability. Moreover, many systems with high sustainability, as well as advanced systems for data handling and availability in place, resulted from national commitments to international monitoring or infrastructure programs, several of which are not necessarily particular to the Arctic.</p><p>Traditionally, terrestrial and atmospheric observation network assessments build on the network concept with a “comprehensive” level including all observations, a “baseline” level of an agreed subset of sustained observations, and a “reference” level, with observations adhering to specific calibrations and traceability criteria. Examples from atmospheric observations are the “comprehensive” global GCOS radiosounding network, the “baseline” GUAN (GCOS Upper Air Network) and “reference” GRUAN (GCOS Reference Upper Air Network) networks. With the lack of in-situ observations especially from the Arctic Ocean and the logistical difficulties to deploy new stations, it was concluded that this concept does not work well in the Arctic.</p><p>In summary, we recommend that:</p><ul><li>advancement in Arctic observing should be done in international global or regional programs with well-established routines and procedures, rather than to invest in new Arctic-specific programs</li> <li>investments in new instruments and techniques be done at already established sites, to benefit interdisciplinary studies and optimize infrastructure costs</li> <li>more observations be based on ships of opportunity and that a subset of ocean, sea-ice and atmosphere observations always be made on all research expeditions, regardless of their scientific aim</li> <li>the funding structures for science expeditions is reviewed to maintain, and preferably increase, the number of expeditions and to safeguard funding for appropriate data handling and storage</li> <li>observing-network concept for the atmosphere over the Arctic Ocean is revised, so that coupled reanalyses represent the “comprehensive level”, satellite observations complemented with available in-situ data is the “baseline level”, while scientific expeditions is the “reference level”. This requires substantial improvements in reanalysis, better numerical models and data assimilation, better satellite observations and improved data handling and accessibility for scientific expeditions.</li> </ul>

Gelatinous zooplankton of the Arctic Ocean: in situ observations under the ice

Polar Biology ◽  
2004 ◽  
Vol 28 (3) ◽  
pp. 207-217 ◽  
Author(s):  
K. A. Raskoff ◽  
J. E. Purcell ◽  
R. R. Hopcroft
Keyword(s):  
Arctic Ocean ◽  
Gelatinous Zooplankton ◽  
The Arctic ◽  
The Arctic Ocean ◽  
In Situ Observations

Synthesis of integrated primary production in the Arctic Ocean: II. In situ and remotely sensed estimates

2013 ◽  
Vol 110 ◽  
pp. 107-125 ◽  
Author(s):  
Victoria J. Hill ◽  
Patricia A. Matrai ◽  
Elise Olson ◽  
S. Suttles ◽  
Mike Steele ◽  
...  
Keyword(s):  
Primary Production ◽  
Arctic Ocean ◽  
Remotely Sensed ◽  
The Arctic ◽  
The Arctic Ocean

scholarly journals Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

2021 ◽  
Vol 14 (1) ◽  
pp. 71
Author(s):  
Sarah B. Hall ◽  
Bulusu Subrahmanyam ◽  
James H. Morison
Keyword(s):  
Soil Moisture ◽  
Arctic Ocean ◽  
Satellite Observations ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Beaufort Gyre ◽  
The Arctic Ocean

Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean.

scholarly journals Parameterization of vertical chlorophyll <i>a</i> in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal and annual primary production estimates

2013 ◽  
Vol 10 (1) ◽  
pp. 1345-1399 ◽  
Author(s):  
M. Ardyna ◽  
M. Babin ◽  
M. Gosselin ◽  
E. Devred ◽  
S. Bélanger ◽  
...  
Keyword(s):  
Primary Production ◽  
Arctic Ocean ◽  
Empirical Model ◽  
Ocean Color ◽  
The Arctic ◽  
Winter Period ◽  
Substantial Impact ◽  
Near Surface ◽  
Chl A ◽  
The Arctic Ocean

Abstract. Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC). In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs) that significantly contribute to primary production (PP) are often observed. These are neither detected by ocean color sensors nor accounted for the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i.e. 5206 stations) and develop an empirical model to estimate vertical chlorophyll a (chl a) according to: (1) the shelf-offshore gradient delimited by the 50 m isobath, (2) seasonal variability along pre-bloom, post-bloom and winter periods, and (3) regional differences across ten sub-Arctic and Arctic seas. Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface chl a concentration (chl asurf; 0.7–30 mg m−3) throughout the open water period, the chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when chl asurf is low (0–0.5 mg m−3). By applying our empirical model to annual chl asurf time series, instead of the conventional method assuming vertically homogenous chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i.e. pre-bloom, post-bloom > 0.05 mg m−3 and the winter period) somehow compensate for the underestimates found when SCMs are deep (i.e. post-bloom < 0.05 mg m−3). SCMs are, however, important seasonal features with a substantial impact on depth-integrated PP estimates, especially when surface nitrate is exhausted in the Arctic Ocean and where highly stratified and oligotrophic conditions prevail.

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