1. Introduction

During the International Polar Year (IPY) 2007–2008, the international scientific community completed an intensive physical survey of the Arctic Ocean (AO). Many countries and institutions contributed to this effort, which generated a significant number of in situ hydrographical observations including stationary fulldepth profiles of temperature/salinity (T/S) from conductivity-temperature-depth instruments (CTD) and partial-depth profiles of the upper 700 m along Lagrangian tracks followed by Ice-Tethered Profiler (ITP) affixed to sea ice, measurements of T/S along the tracks followed by submarine gliders near coastal areas, and a small number of profiles from less accurate expendable CTD and expendable bathythermograph (XBT) instruments.

Arctic T/S distribution is governed largely by water inflow and outflow through the major gateways, the properties of those waters, and regional circulation. AO sources include the warm saline waters advected with the Norwegian current from

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*Arctic Studies - A Proxy for Climate Change*

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North Atlantic [1], the fresh (relative to mean AO salinity) Pacific waters (PW) entering through Bering Strait [2], and the summertime fresh and warm river discharge from the Siberian and North American rivers [3, 4]. AO export occurs primarily through Fram Strait by way of the Transpolar and East Greenland currents and also through less-studied Canadian Archipelago transport [5]. Within the AO, shelf-basin exchanges are typically restricted to bathymetric features, such as Herald and Barrow Canyons in the Chukchi Sea [6] and St. Anna Trough north of Severnaya Zemlya in the Kara Sea [7]. Regional circulation is governed by topographically steered boundary currents along shelf breaks and other topographic features, restricted by density fronts between water masses of disparate origin, and subjected to external forcing including surface heating (cooling) in summer (winter) with significant effects from sea-ice melt/freeze and large-scale atmospheric pressure systems [1, 8–10].

The remote nature of the AO, together with practical difficulties in observation and navigation due to sea ice and sparse infrastructure, makes in situ sampling of the AO expensive and occasional. Satellite monitoring of the ocean surface is possible but inhibited by ice cover and clouds. Unfortunately, the accuracy of the satellite-surface observations and their processed (i.e., L2–L4) products is often far from optimal: they may contain large errors due to poor calibration, mask large portions of the AO for sea ice and thus lack of coverage over the central AO, and may contain anachronistic assumptions in their post-processing algorithms [26]. Modeling efforts and other interdisciplinary studies in need of static background ocean data may need to rely on gridded products that are biased toward older AO regimes or large amounts of surface observations from satellite. Further, climatological studies using older reference states for trend analysis may suffer from amplified trend errors. For example, the Arctic portion of the most recently available Polar Science Center Hydrographic Climatology (PHC 3.0, updated from [27])

Changes in Arctic Ocean Climate Evinced through Analysis of IPY 2007–2008 Oceanographic…

The concerns listed above motivate this work, which presents a 2007–2009 AO stationary analysis state inferred from algorithmic data conditioning of pan-Arctic hydrographical surveys and other at-depth observations to provide a snapshot of the non-coastal ocean state with an emphasis on the intermediate layers. The result is a dataset of gridded T/S available in NetCDF at http://bit.ly/2M6qsJ9, from which this chapter discusses mapped water masses and their differences relative to those mapped from 1950 to 1994 climatology. We also use 4DVar data assimilation to establish an analysis of major circulation changes during IPY relative to the climatological mean and discuss the evident anomalies of July–December 2008 [29]. The remainder of this chapter is organized as follows: Section 2 discusses the in situ data and the production algorithm for the gridded fields, Section 3 presents an atlas of water mass properties for the IPY and their differences from historical data fields, Section 4 discusses changes in the AO water mass distribution and thermal state evident from the use of IPY data and derived climatology, Section 5 presents analysis of circulation anomalies during the IPY, and Section 6 concludes the chapter.

As part of an IPY initiative, approximately 13,000 CTD/xCTD/XBT profiles along

The Data-Interpolating Variational Analysis tool (DIVA, [32]) is a robust finite element-based optimization tool for gridding large 2D, 3D, and 4D datasets and includes error estimates of the analysis. This freely available program, developed by the GeoHydrodynamics and Environment Research, was applied to the observational data described above to construct static full-depth fields on an equal-area polar-centered grid with 50 km resolution. Interpolation to 51 vertical levels occurs level-wise within DIVA, to which an internally applied stability algorithm ensures that analyses remain hydrodynamically stable with respect to density throughout

with ITP data were curated into a central database of AO T/S observations from contributors in Japan, Norway, Russia, Canada, the USA, Germany, Poland, Sweden, and China. Stroh et al. [[26], Figure 1] show the location of profiles over the AO, of which only the IPY CTD and ITP data during 2007–2008 are used here. CTD observations during the sea-ice minimum months of August–October account for approximately 40% of all ship-borne profiles, while wintertime November–March account for approximately 30%. ITP apparatuses provide a more temporally uniform stream of profile data for the uppermost 700 m throughout the year; ITP data were collected and made available by the Ice-Tethered Profiler Program [30, 31] based at the

Woods Hole Oceanographic Institution (http://www.whoi.edu/itp).

is based on historic observations through 1993 [28, dataset g01961].

2. Observational data and gridding

DOI: http://dx.doi.org/10.5772/intechopen.80926

7

The IPY effort occurred at a significant time, coincidental with the largest recent positive Arctic Ocean Oscillation (AOO) index, a measure derived. From the central AO sea-surface height gradient over the central AO, which indicates strength of large-scale anticyclonic flow [11]. Prior to IPY, the AOO index had been in an overall positive regime for nearly two decades, while historical records suggest a sub-decadal frequency [9] (updated at www.whoi.edu/page/preview.do? pid=66578). Other modes of regional oscillation occur with timescales of 60– 80 years [12]. At the same time, in summer 2007, winds associated with the Beaufort High remained predominantly anticyclonic—a feature common to the Arctic winter but unusual for summer [13]—so Beaufort Gyre (BG) sea-level response to atmospheric forcing strengthened the AOO. Additionally, 2007 was a monumental year for river discharge; Eurasian river discharge surpassed the 2002 record by nearly 10% [14]. The effects of these drivers, whether purely anomalous or the result of long-term variability, relate to pronounced recent changes in the Arctic marine climate system and were witnessed by the IPY survey efforts.

Over a decade has passed since many of the observed strong and rapid warming trends were confirmed as both present and underway. Older climatologies may be inadequate for the study of more recent changes as they may depend much on pre-1996 data when the positive AOO regime was not such a strong and permanent feature of the region, thicker and more extensive sea ice regulated mechanical and thermodynamical fluxes with the atmosphere, continental riverine discharge was less, and the Lomonosov Ridge roughly defined a partition of the Arctic between Atlantic and Pacific upper-ocean layers. The thermal state of the AO over the past decade is above the long-term average, and this warming greatly affects both hydrographic and ice-related processes observed in the high latitudes; changes occurring under these new conditions are of particular interest [5, 13, 15–23].

Unlike other oceans, vertical stratification of AO water masses is governed more by salinity than temperature, and density gradients readily allow for decomposition of profiles into differentiated, typically noninteractive layers. Away from regions of significant freshwater influence, the vertical distribution of water masses throughout the AO generally comprises a well-mixed surface layer occupying the upper 50–100 m, underlain by a layer of intermediate water of Pacific origin (absent in the eastern, Atlantic domain of the Arctic), followed by a layer of warmer more saline water of Atlantic origins and an Arctic deepwater bottom layer. Importantly, the halocline of Pacific-originated waters overlies the warmer Atlantic water in the ocean in the Pacific sector [24], buffering sea ice from the warmer Atlantic waters below. The presence, thickness, and specific properties of each layer vary laterally throughout the Arctic [10, 25], and one may distinguish between layers of waters of Pacific and Atlantic origin on the depth of local haloclines and isotherms characteristic within each column.

### Changes in Arctic Ocean Climate Evinced through Analysis of IPY 2007–2008 Oceanographic… DOI: http://dx.doi.org/10.5772/intechopen.80926

The remote nature of the AO, together with practical difficulties in observation and navigation due to sea ice and sparse infrastructure, makes in situ sampling of the AO expensive and occasional. Satellite monitoring of the ocean surface is possible but inhibited by ice cover and clouds. Unfortunately, the accuracy of the satellite-surface observations and their processed (i.e., L2–L4) products is often far from optimal: they may contain large errors due to poor calibration, mask large portions of the AO for sea ice and thus lack of coverage over the central AO, and may contain anachronistic assumptions in their post-processing algorithms [26]. Modeling efforts and other interdisciplinary studies in need of static background ocean data may need to rely on gridded products that are biased toward older AO regimes or large amounts of surface observations from satellite. Further, climatological studies using older reference states for trend analysis may suffer from amplified trend errors. For example, the Arctic portion of the most recently available Polar Science Center Hydrographic Climatology (PHC 3.0, updated from [27]) is based on historic observations through 1993 [28, dataset g01961].

The concerns listed above motivate this work, which presents a 2007–2009 AO stationary analysis state inferred from algorithmic data conditioning of pan-Arctic hydrographical surveys and other at-depth observations to provide a snapshot of the non-coastal ocean state with an emphasis on the intermediate layers. The result is a dataset of gridded T/S available in NetCDF at http://bit.ly/2M6qsJ9, from which this chapter discusses mapped water masses and their differences relative to those mapped from 1950 to 1994 climatology. We also use 4DVar data assimilation to establish an analysis of major circulation changes during IPY relative to the climatological mean and discuss the evident anomalies of July–December 2008 [29]. The remainder of this chapter is organized as follows: Section 2 discusses the in situ data and the production algorithm for the gridded fields, Section 3 presents an atlas of water mass properties for the IPY and their differences from historical data fields, Section 4 discusses changes in the AO water mass distribution and thermal state evident from the use of IPY data and derived climatology, Section 5 presents analysis of circulation anomalies during the IPY, and Section 6 concludes the chapter.
