Top PDF Observed anomalous atmospheric patterns in summers of unusual Arctic sea icemelt

Observed anomalous atmospheric patterns in summers of unusual Arctic sea icemelt

Observed anomalous atmospheric patterns in summers of unusual Arctic sea icemelt

With a more zonal jet stream, more cyclones track across the North Atlantic Ocean into northern Europe instead of the Arctic (Figure 6a). Screen et al. [2011] discovered that fewer cyclones tracking into the Arctic in MJJ favored lower September SIE. Using an empirical orthogonal function analysis, Dong et al. [2013] found a significant anticorrelation between cyclones entering the Arctic and those reaching northwestern Europe of −0 .61. They linked the two distinct North Atlantic paths to the summer NAO and blocking frequency over the United Kingdom and northwestern Europe. Here we find that only 5% of the anomalous sea ice melt underlying the cyclonic tendency can be explained by the NAO. In fact, not more than 10% of our sea ice melt index is due to any of the climate patterns analyzed (NAO, AO, and BSH). Our Figures 3a, 3b, 3d, and 10 bear some resemblance to the results dominated by the Arctic Dipole in Overland et al. [2012]. This is expected since half of their data (June 2007–2012) are included in our HMR months. However, for the rest of our time period (1979–2013), we find low coincidence between their Figure 2 and our LMR and HMR months in Figure 1. Finally, although there are some similarities to the features in Figures 3b, 3c, and 4 here, we do not find the negative summer Northern Annular Mode (NAM) in Tachibana et al. [2010] to demonstrate the observed atmospheric patterns in our study.
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Observed anomalous atmospheric patterns in summers of unusual Arctic sea ice melt

Observed anomalous atmospheric patterns in summers of unusual Arctic sea ice melt

Erlend M. Knudsen 1,2 , Yvan J. Orsolini 2,3 , Tore Furevik 1,2 , and Kevin I. Hodges 4 1 Geophysical Institute, University of Bergen, Bergen, Norway, 2 Bjerknes Centre for Climate Research, Bergen, Norway, 3 Norwegian Institute for Air Research, Kjeller, Norway, 4 Department of Meteorology, University of Reading, Reading, UK Abstract The Arctic sea ice retreat has accelerated over the last decade. The negative trend is largest in summer, but substantial interannual variability still remains. Here we explore observed atmospheric conditions and feedback mechanisms during summer months of anomalous sea ice melt in the Arctic. Compositing months of anomalous low and high sea ice melt over 1979–2013, we find distinct patterns in atmospheric circulation, precipitation, radiation, and temperature. Compared to summer months of anomalous low sea ice melt, high melt months are characterized by anomalous high sea level pressure in the Arctic (up to 7 hPa), with a corresponding tendency of storms to track on a more zonal path. As a result, the Arctic receives less precipitation overall and 39% less snowfall. This lowers the albedo of the region and reduces the negative feedback the snowfall provides for the sea ice. With an anticyclonic tendency, 12 W/m 2 more incoming shortwave radiation reaches the surface in the start of the season. The melting sea ice in turn promotes cloud development in the marginal ice zones and enhances downwelling longwave radiation at the surface toward the end of the season. A positive cloud feedback emerges. In midlatitudes, the more zonally tracking cyclones give stormier, cloudier, wetter, and cooler summers in most of northern Europe and around the Sea of Okhotsk. Farther south, the region from the Mediterranean Sea to East Asia experiences significant surface warming (up to 2 .4 ◦ C), possibly linked to changes in the jet stream.
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Recent summer Arctic atmospheric circulation anomalies in a historical perspective

Recent summer Arctic atmospheric circulation anomalies in a historical perspective

jewicz and Marshall (2014), who state that the 2013 JJA mean Z500 over Greenland was significantly lower than the average over the last 7 decades, which contrasts with the strong positive anomaly of the preceding summers. The op- posite extreme anomalies between 2012 (positive anomaly) and 2013 (negative anomaly) have also been highlighted by Hanna et al. (2014b) on the basis of the Greenland Blocking Index. Secondly, as said above, similar circulation conditions were observed before 1880 and around 1891–1896, when the Arctic climate was likely to have been much colder than now. Furthermore, Ding et al. (2014) suggest that the geopo- tential height increase observed over north-eastern Canada and Greenland, as well as the negative NAO trend, could be due to SST changes in the tropical Pacific that induce changes in the Rossby wave train affecting the North Amer- ican region. Since the tropical SST changes are not repro- duced by general circulation models under current green- house gas concentrations, Ding et al. (2014) conclude that these changes are due to the natural variability of the cli- matic system. However, Wu et al. (2014) showed that the pro- gressive intensification of the Beaufort Sea High over 1979– 2005 can only be reproduced by climate models by including the observed greenhouse gas concentration increase. More- over, Screen et al. (2012) have shown that various forcings are needed to explain the observed Arctic warming: while Arctic sea ice and associated SST changes, as well as re- mote SST changes (corroborating the conclusions of Ding et al., 2014) are the main drivers of the winter warming, the summertime temperature increase could mainly be due to in- creased radiative forcing, suggesting a role of global warm- ing. Matsumura et al. (2014) have found a significant relation between the earlier spring snowmelt over the Eurasian con- tinent and the enhanced summertime Arctic anticyclonic cir- culation. The earlier snowmelt could induce a negative SLP anomaly over Eurasia, which is compensated by an SLP in- crease over the western part of the Arctic region. Finally, Bezeau et al. (2014) conclude that the anomalous anticy- clonic patterns over the Arctic over 2007–2012 are due to combined effects of sea ice loss, snow extent reduction, and enhanced meridional heat advection. Thus, while it is widely admitted that the Arctic region experiences a strong warm- ing since some years (Screen et al., 2012), the complexity of the climate of this region due to its multiple internal and external forcings and feedbacks does not allow us to solve the question of whether the 2007–2012 circulation anomaly is (mainly) due to global warming or to natural variability.
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The role of atmospheric circulation patterns in driving recent changes in indices of extreme seasonal precipitation across Arctic Fennoscandia

The role of atmospheric circulation patterns in driving recent changes in indices of extreme seasonal precipitation across Arctic Fennoscandia

The EAWR pattern generally had the most influence on AF EPE trends in both summer and autumn, but not so during spring and winter (Figs. 2 , 3, and 4 ). As significant correlations existed between EAWR and the EPEs across all seasons in AF, this seasonal distinction was primarily because of the significant (negligible) trends in the summer and autumn (spring and winter) EAWR indices (Table 3 ). For example, we note that Casanueva et al. ( 2014 ) found that correlations between EAWR and EPEs in AF were actually much stronger in winter than in autumn while Irannezhad et al. ( 2017 ) demonstrated that EAWR was the circulation pattern with the highest correlation to annual PRCPTOT in northern Finland. A positive EAWR has been linked to anomalous northerly flow over the Barents Sea with markedly increased precipitation over AF in winter (e.g. Ionita 2014 ; Lim 2015 ). However, we find that in summer, the relationship between EAWR and PRCPTOT was predominantly negative and hence the significant negative trend in EAWR contributed to the increasing PRCPTOT trends observed across AF in this season (Fig. 2b ). The EA actually had the largest impact on the summer CWD trend at the most stations and the marked seasonal increase in this index may have been responsible for driving the significant negative trends in parts of northern Norway (Figs. OR2.7b and 4b). In autumn, EAWR was the circulation pattern with the greatest influence on the most AF stations for all three EPEs studied. Nevertheless, both the EA and SCA patterns were also dominant at more than 25% of AF stations for one or more of the EPE trends in this season (Figs. 2c , 3, and 4c ).
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Atmospheric influences on the anomalous 2016 Antarctic sea ice decay

Atmospheric influences on the anomalous 2016 Antarctic sea ice decay

Abstract. In contrast to the Arctic, where total sea ice extent (SIE) has been decreasing for the last three decades, Antarc- tic SIE has shown a small, but significant, increase during the same time period. However, in 2016, an unusually early onset of the melt season was observed; the maximum Antarc- tic SIE was already reached as early as August rather than the end of September, and was followed by a rapid decrease. The decay was particularly strong in November, when Antarctic SIE exhibited a negative anomaly (compared to the 1979– 2015 average) of approximately 2 million km 2 . ECMWF In- terim reanalysis data showed that the early onset of the melt and the rapid decrease in sea ice area (SIA) and SIE were associated with atmospheric flow patterns related to a posi- tive zonal wave number three (ZW3) index, i.e., synoptic sit- uations leading to strong meridional flow and anomalously strong southward heat advection in the regions of strongest sea ice decline. A persistently positive ZW3 index from May to August suggests that SIE decrease was preconditioned by SIA decrease. In particular, in the first third of Novem- ber northerly flow conditions in the Weddell Sea and the Western Pacific triggered accelerated sea ice decay, which was continued in the following weeks due to positive feed- back effects, leading to the unusually low November SIE. In 2016, the monthly mean Southern Annular Mode (SAM) index reached its second lowest November value since the beginning of the satellite observations. A better spatial and temporal coverage of reliable ice thickness data is needed to assess the change in ice mass rather than ice area.
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Unusual patterns of Monteggia fracture-dislocation

Unusual patterns of Monteggia fracture-dislocation

From all fourteen patients there were 7 Monteggia frac- ture-dislocations with additional (segmental) fracture of the ulnar diaphysis; 4 were type I, 2 were type II and one was type III according to Bado classification [8]. Three were complex patterns of Monteggia fracture-dislocations with additional comminuted fractures of the distal end of both forearm bones; one was type I and 2 were type III according to Bado classification. Two patients had Mon- teggia fracture-dislocation with additional fractures of the diaphysis of both forearm bones (both were Bado type I). Finally two patients had a Monteggia fracture-dislocation (one Bado type I and one Bado type II) and multiple other fractures of the upper arm.
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Exchange of sea ice between the Arctic Ocean and the Canadian Arctic Archipelago

Exchange of sea ice between the Arctic Ocean and the Canadian Arctic Archipelago

ment an unusual breakup of the old multiyear fast-ice plug in Sverdrup Channel during the summer of 1998. In particular, Jeffers et al. [2001] argue that the widespread loss of multiyear ice in 1998 can be attributed in part to air temperature that was 2.5C warmer than normal that sum- mer. Here, we examine the role of ice exchange between the Arctic Ocean and Sverdrup Basin during this time. The ice conditions at the end of September 1998 can be seen in the RADARSAT SAR image (Figure 1b): the Peary and Sverdrup Channels, north of Ellef Ringnes Island are largely ice free; the Arctic ice cover just west of these channels, however, remains fairly compact. In response to a Septem- ber SLP field with a high centered over Greenland, the direction of ice movement is toward the Arctic Ocean. In fact, the month of September saw the largest export of sea ice (10  10 3
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Perspectives on future sea ice and navigability in the Arctic

Perspectives on future sea ice and navigability in the Arctic

Linear trends of ice thickness and concentration under SSP5-8.5 in September. Changes in sea ice volume and age under SSP5-8.5[r]

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A Climatology of Atmospheric Patterns Associated

A Climatology of Atmospheric Patterns Associated

blizzard: Alberta Clippers, Arctic Fronts, Colorado Lows, and Hybrids.. Objective classification of 20.[r]

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The Arctic Front and its variability in the Norwegian Sea

The Arctic Front and its variability in the Norwegian Sea

a single section (shown in Fig. 4a) taken across the Mohn Ridge, where a strong frontal signature is found in the singu- larity exponents maps (Figs. 4–6). Analysing the variability of the AF over the Mohn Ridge is also important due to its proximity to Jan Mayen Island, which is well-known to be an important breeding region inhabited by large colonies of seabirds. Although higher up in the food chain, the variabil- ity of the AF may have an influence on the birds through the impact on biology and fisheries of the region. A similar sec- tion was used by Piechura and Walczowski (1995), to study the AF across the Mohn Ridge using CTD data during the time period 1987–1993. Figure 7a, b and c show respectively the mean, winter and summer climatology of potential tem- perature along the vertical cross-section at the Mohn Ridge (location shown in Fig. 4a). To the east (left side of the fig- ure), the figure shows the warm Atlantic Water residing in the Lofoten Basin, while the cold waters reside in the Greenland Sea (right side of the figure). Note that these results agree with those reported by Piechura and Walczowski (1995). The temperature gradient along the vertical section delimits the AF at the Mohn Ridge (Fig. 7d). The location of the core of the front along the section across the Mohn Ridge is marked as red in Fig. 4a. At the location of the AF, a temperature change of roughly 2 ◦ can be noted from Fig. 7a. The potential temperature gradient (0.04 ◦ C km −1 ) across the core of the AF is comparable with those reported at other high-latitude frontal regions (Lobb et al., 2003). The AF extends from the surface down to 600 m depth with the core stretching over depths from 50 to 400 m, as indicated by the strong potential temperature horizontal gradient areas (dark blue contours) in Fig. 7d–f. The frontal structure is well connected to the surface during winter (Fig. 7b and e), while in summer, the stratified layer of warm water at the surface (upper 25 m) dis- connects the AF deeper layers from the surface (Fig. 7c and f). The figures also show that there is no seasonal variability in the location of the core of the AF over the Mohn Ridge. 4.3 Impact of large-scale atmospheric forcing on the
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Arctic sea ice simulation in the PlioMIP ensemble

Arctic sea ice simulation in the PlioMIP ensemble

However, it appears that the performance of this albedo scheme is very sensitive to differences in other components of the climate models: NorESM-L (which shows a large con- tribution of clear-sky albedo) uses the same atmosphere com- ponent as CCSM4 (low contribution of clear-sky albedo), albeit at a lower resolution version in the PlioMIP experi- ment, but it employs a different ocean component that also has a lower resolution than the ocean component used in CCSM4. The contrast in the contribution of clear-sky albedo to high-latitude warming between NorESM-L and CCSM4 is reflected in the large difference in their simulations of sum- mer mid-Pliocene sea ice. One cause is certainly the nature of the sea ice–albedo feedback mechanism (Curry et al., 1995). Reduced albedo at high latitudes can be both a cause of and a result of a reduced sea ice extent. Models with parame- terisations with a lower sea ice albedo minimum therefore have a greater potential to amplify the warming that origi- nates from other sources in simulations of the mid-Pliocene, such as greenhouse gas emissivity. The low sea ice albedo assumed in NorESM-L is a likely explanation for the low sea ice extents it simulates (Figs. 2, 6), both in mid-Pliocene and pre-industrial simulations.
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On modelling the mass of Arctic sea ice

On modelling the mass of Arctic sea ice

The first recorded observations of ice motion was of ice in free drift. It was documented by Sverdrup [1928] and Nansen [1902] that ice is transported at 2% of wind speed and 15° to wind direction. Analysis of ice camp and buoy drifts during the 1960s gives a more detailed picture of Arctic sea ice motion. It was determined that 70% of the variance of sea ice motion can be attributed to the geostrophic wind [Thorndike & Colony, 1982]. In the same paper it was stated that on seasonal time scales the main forcing components are wind stress and ocean stress, both contributing equally to the ice motion. Field studies have provided more detailed analysis of the force balance. During the Arctic Ice Dynamics Joint Experiment (AIDJEX) it was found that long term mean ice motion, in the summer when ice concentration is low, results from the balance of four forces on the ice [Hunkins, 1975, McPhee, 1979]. In free drift the significant forces on the ice are wind stress, ocean stress, the Coriolis force and acceleration down the sea surface tilt. Inertial terms were found to be an order of magnitude smaller than these on daily or longer time scales [Thorndike, 1986]. The residual term in the measured force balance is found to be comparable to the Coriolis force, and an order of magnitude smaller than the wind stress [McPhee, 1979]. This residual is attributed to interactions between ice fioes. In areas of ice convergence, resistance to compression becomes important, and ice can no longer be considered to be in free drift. For climatological studies small time scale forcing may be ignored. Contributions to the force balance from tidal motion, pressure gradients and inertial terms are considered to be small when averaged over time scales greater than a day [McPhee, 1979].
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Predictability of the Arctic sea ice edge

Predictability of the Arctic sea ice edge

Abstract Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.
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Forever Arctic Sea Omega-3

Forever Arctic Sea Omega-3

Exclusive Nutrient Blend Vit♂lizeTM also includes a blend of nutrients specifically designed and balanced to support men’s health and promote healthy prostate function. Vitamins C, D, E,[r]

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Tropical Pacific spatial trend patterns in observed sea level: internal variability and/or anthropogenic signature?

Tropical Pacific spatial trend patterns in observed sea level: internal variability and/or anthropogenic signature?

Abstract. In this study we focus on the sea level trend pattern observed by satellite altimetry in the tropical Pacific over the 1993–2009 time span (i.e. 17 yr). Our objective is to inves- tigate whether this 17-yr-long trend pattern was different be- fore the altimetry era, what was its spatio-temporal variabil- ity and what have been its main drivers. We try to discrimi- nate the respective roles of the internal variability of the cli- mate system and of external forcing factors, in particular an- thropogenic emissions (greenhouse gases and aerosols). On the basis of a 2-D past sea level reconstruction over 1950– 2009 (based on a combination of observations and ocean modelling) and multi-century control runs (i.e. with constant, preindustrial external forcing) from eight coupled climate models, we have investigated how the observed 17-yr sea level trend pattern evolved during the last decades and cen- turies, and try to estimate the characteristic time scales of its variability. For that purpose, we have computed sea level trend patterns over successive 17-yr windows (i.e. the length of the altimetry record), both for the 60-yr long reconstructed sea level and the model runs. We find that the 2-D sea level reconstruction shows spatial trend patterns similar to the one observed during the altimetry era. The pattern appears to have fluctuated with time with a characteristic time scale of the order of 25–30 yr. The same behaviour is found in multi- centennial control runs of the coupled climate models. A similar analysis is performed with 20th century coupled cli- mate model runs with complete external forcing (i.e. solar plus volcanic variability and changes in anthropogenic forc-
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The color of melt ponds on Arctic sea ice

The color of melt ponds on Arctic sea ice

Finally, numerical simulations have been used to inves- tigate the physical processes of melt ponds from formation to summertime development and then to autumn refreezing (e.g., Tsamados et al., 2015). A three-dimensional model was used to simulate the evolution of melt ponds and found that the role of snow is important mainly at the onset of melting, whereas initial ice topography strongly controls pond size and fraction throughout the melt season (Scott and Feltham, 2010). The refreezing process of melt ponds was also mod- eled, and the results revealed that ice growth would be over- estimated by 26 % if the impact of refrozen ponds was ex- cluded (Flocco et al., 2015). New parameterizations for melt ponds have also been embedded into climate models to eval- uate the role of surface melting on the summer decay of Arc- tic sea ice (e.g., Holland et al., 2012). The improved models produced results that agreed more closely with observations than other models without or only implicitly including the effect of melt ponds (Flocco et al., 2012; Hunke et al., 2013). This study focuses on the color evolution of melt ponds on Arctic sea ice, a perspective on melt ponds that has seen few investigations so far (Perovich et al., 2002a; Light et al., 2015; Istomina et al., 2016). The photograph in Fig. 1 reveals the large variety in melt-pond appearances even on the same ice floe. The color of melt ponds varies from light bluish to dark, largely depending on the age of the pond and the prop- erties of the underlying ice, which can be easily examined during field investigations. First quantitative measurements of melt-pond color were performed in the central Arctic in 2012 (Istomina et al., 2016). Beyond spectral albedo of sea ice and melt ponds measured with the portable radiometer ASD FieldSpecPro 3 (Istomina et al., 2013, 2017), a photo- graph was taken at each albedo measurement site, together with ice thickness and water depth measured by means of drilling. These field data show a clear connection between the underlying ice thickness of the melt pond and its color and
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Constraining projections of summer Arctic sea ice

Constraining projections of summer Arctic sea ice

To help understand this issue, we show in Fig. 3 the run- ning trend in SSIE for all CMIP5 models for RCP8.5. As sug- gested in the figure, the trends, and thus the sea ice changes, become increasingly large sometime during the 21st century, and then go to zero. The timing of the most negative trend is marked with a vertical bar in the figure, and is clearly model-dependent. To gain further insight into this, we dis- play in Fig. 4 the evolution of SSIE trends as a function of the mean SSIE, in order to visualize the dynamics of the sys- tem. In these “phase-plane” plots (a variable versus its time derivative), clear similarities come to light. All models fol- low a similar trajectory: they start from the right, with rel- atively high mean SSIE at the beginning of the simulation. Then they move leftwards as the mean SSIE decreases and all experience a U-shaped trajectory as the mean SSIE de- creases further to ice-free conditions (the 2030–2061 posi- tion of each model is marked with a colored dot). In Fig. 4, the spread in the CMIP5 population is thus represented by the different 1979–2010 positions of the CMIP5 models on their trajectories (colored crosses): for example, BCC-CSM1.1, CanESM2 and GISS-E2-R are already near the minimum, while EC-EARTH and CCSM4 have not reached it yet. Un- der RCP4.5, similar trajectories exist (supplement figure) for the subset of models that reach ice-free conditions in September by ∼ 2060 – the approximate year at which the RCP4.5 forcing stabilizes – suggesting that, as long as the SSIE reaches (near) ice-free conditions under the effect of increased radiative forcing, the U-shaped trajectory occurs.
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Evaluation of sea-surface salinity observed by Aquarius

Evaluation of sea-surface salinity observed by Aquarius

The goal of the Aquarius mission is to globally measure monthly mean SSS with an accuracy of 0.2 psu over a spatial smoothing scale of 150 km. Because Aquarius does not measure SSS directly but measures the electromagnetic radiation signal emitted from the sea surface, validations of the retrieved SSS are necessary to evaluate the quality of the data and to assess the error structures. In the present study, Aquarius along-track SSS observations were compared with in situ salinity measurements from Argo floats and offshore moored buoys. In addition, the monthly averaged Aquarius SSS was compared with results from the ocean data optimal interpolation system operated by the Japan Agency for Marine- Earth Science and Technology (JAMSTEC) and the ocean data assimilation system used by the Meteoro- logical Research Institute, Japan Meteorological Agency (MRI/JMA). Residual analyses were conducted to clarify the error structures. Evaluation results obtained from the different Aquarius products are com- pared with each other and possible causes of errors are discussed in terms of the measurement princi- ples employed by the Aquarius mission and differences in the SSS retrieval algorithms. The paper is organized as follows: section 2 describes the data used, sections 3 and 4 describe the results obtained from analyses of along-track and monthly averaged SSS, respectively, and section 5 presents a brief summary.
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Sub arctic and Arctic sea surface temperature and its relation to ocean heat content

Sub arctic and Arctic sea surface temperature and its relation to ocean heat content

Over much of the 20 th century the subpolar gyre stands out from the rest of the Atlantic by exhibiting a slow -0.5 o C/100yr cooling trend (e.g., Deser et al., 2010). In contrast, our 29 year SST data set documents a reversal of this trend in the subpolar gyre and its replacement by rapid warming at a rate in excess of 0.6 o C/10yr (Fig. 5 left). This warming trend extends into the Nordic Seas where it still exceeds 0.3 o C/10yrs. Much of this warming of SST in the subpolar gyre occurred in the mid-1990s so that SST in the second half of the decade is more than 1 o C warmer than SST in the first half of the decade (Fig. 5 right). The warming in the 1990s is also evident in the subpolar North Pacific, and is evident in both seasonal and annual analyses (only the annual trend is shown). While this warming occurred coincident with a change in satellites (from NOAA11 to NOAA14); comparison to in situ SST observations (Fig. 3 lower panel) reassures us that the warming was not due to a change in satellite sensors. Indeed, it seems likely that an important aspect of the warming in the 1990s is the constructive interference of tripole like North Atlantic decadal (e.g. Wallace et al., 1990; Kushnir, 1994) and broad North Atlantic multi-decadal (Deser and Blackmon, 1993) patterns of surface climate in this basin, reviewed in the Introduction.
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Partitioning uncertainty in projections of Arctic sea ice

Partitioning uncertainty in projections of Arctic sea ice

a b c d e f Figure 5. Percent of occurrence of separate 5-year trends in September Arctic sea-ice extent (SIE) from 1950-2019 for the (a) CESM1-LE, (b) CanESM2-LE, (c) CSIRO- Mk3.6.0-LE, (d) GFDL-CM3-LE, (e) GFDL-ESM2M-LE, and (f) MPI-GE. A 4th order polynomial was removed from each member of each SMILE prior to trend calculations to estimate the forced response. The bars show the distribution of trends for all members. The grey bars show percent of occurrence of separate 5-year trends in September Arctic SIE from 1930-2019 as estimated from Walsh et al. (2017). A 4th order polynomial was removed from the dataset prior to trend calculations to estimate the forced response.
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