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Mechanisms for and Predictability of a Drastic Reduction in the Sea Ice: APPOSITE Data with Model MIROC

J. ONO,H.TATEBE, AND Y. KOMURO Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan

(Manuscript received 2 April 2018, in final form 22 October 2018)

ABSTRACT

The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are in- vestigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

1. Introduction June, and the 2-m temperature in September have been identified as key contributors (Kauker et al. 2009). For The Arctic summer sea ice extent (SIE) has markedly the 2012 reduction, the recent thinning of sea ice decreased since satellite observations began in the late thickness (or decreasing of multiyear ice) due to the 1970s. For September Arctic sea ice, the 10 lowest mini- Arctic Ocean warming (Kwok and Rothrock 2009; mum extents have occurred since 2000 (NSIDC 2017). In Comiso 2012; Polyakov et al. 2012) and a great cyclone particular, in the summers of 2007 and 2012, extreme sea over the Arctic Ocean (Simmonds and Rudeva 2012; ice loss was observed. The September average SIE in Zhang et al. 2013) have been suggested as drivers. 2007 and 2012 was 4.27 and 3.57 million km2 (22.14 and Moreover, the sea ice memory and the positive feedback 22.84 million km2 anomaly from the 1981–2010 average), under warm atmospheric conditions contributed to the which have been ranked as the second lowest and lowest exceptional sea ice loss in 2012 (Guemas et al. 2013). recorded values, respectively (NSIDC 2018). These extreme sea ice losses have occurred under the Various physical mechanisms have been proposed for ongoing warming of the Arctic. Both natural and an- explaining such extreme sea ice reduction events. The thropogenic forcings have influenced the observed sea ice 2007 reduction was mainly triggered by a dipole anom- decline (Kay et al. 2011; Notz and Marotzke 2012; Day aly pattern in the sea level pressure (Wang et al. 2009; et al. 2012). However, their contributions are not well Zhang et al. 2008; Serreze and Stroeve 2015) and by the understood, partly because the sea ice records from sat- Pacific water inflow (Woodgate et al. 2010). In addition, ellites are short. The role of the internal variability in the ice thickness in March, the wind stresses in May to modulating the downward trend in the SIE has been suggested to be significant (Swart et al. 2015) and internal Denotes content that is immediately available upon publica- variability, which controls most of the Arctic atmospheric tion as open access. circulation changes, accounts for about 30%–50% of the decline in the September SIE (Ding et al. 2017). Al- Corresponding author: Jun Ono, [email protected] though the observed SIE minimum record in 2012 is

DOI: 10.1175/JCLI-D-18-0195.1 Ó 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 10/05/21 08:20 PM UTC 1362 JOURNAL OF CLIMATE VOLUME 32 likely caused by a combination of anthropogenic and and minor-update version of MIROC5 (Watanabe et al. natural forcings (Zhang and Knutson 2013), further in- 2010) that contributed to phase 5 of the Coupled Model vestigations are needed to assess the role of internal Intercomparison Project and the Intergovernmental variability in drastic reduction events and to improve Panel on Fifth Assessment Report Arctic sea ice forecasting systems. (IPCC 2013). The horizontal resolution of the atmo- Regarding the predictability of the Arctic sea ice, many spheric component is a T42 spectral truncation (about studies have used climate models to consider forecasts 300 km), and there are 40 vertical levels up to 3 hPa. The on seasonal to interannual time scales (Blanchard- warped bipolar horizontal coordinate system of the Wrigglesworth et al. 2011; Chevallier et al. 2013; Wang MIROC5 oceanic component has been replaced by a et al. 2013; Sigmond et al. 2013; Day et al. 2014; Msadek tripolar coordinate system. The oceanic component had et al. 2014; Guemas et al. 2016a,b; Bushuk et al. 2017; 18 longitudinal grid spacing in the spherical coordinate Ono et al. 2018). The predictability of extreme sea ice portion south of 638N. The meridional grid spacing anomalies has also been investigated with perfect model varies from about 0.58 near the equator to 18 in the ensemble predictions. For example, Holland et al. (2011) midlatitudes. There are 63 vertical levels, the lowermost assessed the inherent predictability of Arctic sea ice fo- level of which is located at 6300-m depth. The sea cusing on the role of the preconditioning of ice and ice component implements one-layer thermodynamics natural variation in extreme SIE reduction and showed (Bitz and Lipscomb 1999), elastic–viscous–plastic rhe- that models can capture the occurrence of sea ice loss ology (Hunke and Dukowicz 1997), and a subgrid ice events but not their amplitude. In addition, Tietsche thickness distribution (Bitz et al. 2001) with five cate- et al. (2013) investigated the predictability of two ex- gories. Snow cover on sea ice in the model affects the treme anomalies for the present day in a historical run thermodynamic processes through changes in the surface and for the middle of the twenty-first century in the and vertical heat fluxes. The detailed framework RCP4.5 experiment and showed that although the onset and parameters have been described in Komuro et al. and amplitude of extreme anomalies are not predictable, (2012). In the land surface model, parameterizations of a the sea ice reduction after the onset can be predicted subgrid-scale snow cover distribution (Liston 2004; Nitta 1 year ahead. However, the previous studies include the et al. 2014) and a simple wetland scheme (Nitta et al. influence of the warming trend, and thus mechanisms (or 2017) have been newly implemented into MIROC5.2. sources) for the drastic sea ice reduction associated with An improved treatment of the turbulent kinetic energy the internal variability are not fully understood. input from the atmosphere (Komuro 2014) is also adopted Motivated by these previous studies, the present study in the Arctic Ocean sea ice area. examines the spatial pattern of extreme sea ice reduction b. Experimental designs under fixed at the present-day level (i.e., without the warming trend), based on the Arctic Predic- Using MIROC5.2, we performed a simulation with tion and Predictability on Seasonal-to-Interannual Time radiative forcing fixed at present-day levels (year 2000) Scales (APPOSITE) project, the main goal of which is to within the APPOSITE project (Day et al. 2016). After a quantify at what time scales the Arctic climate is pre- spinup of 1000 years, the model is run for a further 200 dictable (Day et al. 2016). Furthermore, by comparing years, which are arbitrarily labeled as 1 to 200. This ex- a control simulation with perfect model ensemble pre- periment will be referred to hereinafter as CTRL. For all dictions, the predictability of a drastic reduction in SIE variables, the monthly mean values during the period of and possible mechanisms for this reduction will be dis- 1 to 200 are used for the climatology and their anomalies cussed. In Day et al. (2016), a detailed description of the are defined as the deviation from the climatology. APPOSITE project is provided; however, the predict- To diagnose the predictability of a drastic reduction ability of extreme events is not investigated. Thus, this in the Arctic sea ice, we conducted a series of perfect study contributes to a better understanding of the pre- model ensemble prediction experiments (PRED). The dictability of drastic sea ice reduction events. target years are 51, 56, and 57 as discussed in section 3. The start dates are 1 April and 1 July in the same year. The PREDs with start dates of 1 April are referred to as 2. Methods PRED.APR, and those with 1 July as PRED.JUL. An ensemble of eight members is generated for each start a. date. The initial conditions are taken from the CTRL The climate model used in this study is the Model for and each member differs only by a perturbation of the Interdisciplinary Research on Climate (MIROC) ver- sea surface temperature, which is generated by spatially sion 5.2 (Tatebe et al. 2018), which is a low-resolution uncorrelated Gaussian noise with a standard deviation

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FIG. 1. Time series of the monthly mean (a) SIE and (b) SIV in a 200-yr CTRL experiment.

2 of 10 4 K under the APPOSITE protocol. Each en- and Kimoto 2009)]. Note that one of the reasons for semble is run for 3 years and 9 months from 1 April and differences between the model and observations could 3 years and 6 months from 1 July. be that the model was not run under the historical When conducting sea ice forecasts with coupled global forcing, and instead the forcing was fixed at year 2000 climate models, forecast errors arise from errors in the levels. The seasonal cycle of the SIE monthly climatol- initial conditions and model biases. In the perfect model ogy in MIROC5.2 is smaller than the observations experiments, however, the model can predict itself with (NSIDC and HadISST) by 1–3 million km2 except for perfect initial conditions and no biases (Collins 2002; Day August to November (Fig. 2a). This is partly because the et al. 2014; Tietsche et al. 2014; Blanchard-Wrigglesworth sea ice coverage is small in the Bering and Okhotsk Seas et al. 2015). Therefore, an upper bound of the predict- (not shown), and these regions have model biases in ability can be estimated with climate models. MIROC5.2 as documented by Komuro et al. (2012). The simulated SIE is smaller than the ProjD observations by 1–2 million km2 from January to April, and larger by ;1 3. Control experiment million km2 from July to October due to the differences in the Barents and Kara Seas. The standard deviations of a. Model performances the SIE in MIROC5.2 are larger in all months than those The features of the SIE and sea ice volume (SIV) from in the observations by 0.1–0.2 million km2 (Fig. 2b). This CTRL are examined (Fig. 1). In this study, the SIE and implies that there is more variability in the model than in SIV are defined as the cumulative area of grid cells with the observations. Following Day et al. (2014), the lagged at least 15% sea ice concentration (SIC) and the sum of correlation of the SIE in MIROC5.2 and three obser- grid cell volumes obtained by multiplying the ice thick- vational datasets for comparison is shown in Figs. 2c–f ness by the ice concentration, respectively. Interannual for each start month against different lead times. The variability in SIE is positively correlated with that in SIV linear trend was removed from the time series of SIE of for the 200 model years. The correlation coefficients are MIROC5.2 and the observations before the correlations higher (r 5 0.61–0.82) in June to October and lower (r 5 were calculated. The lagged correlations in MIROC5.2 0.47–0.51) in November to May. In addition, a period of are higher than those in the observations at all lead times drastic ice reduction can be seen around year 57. These except for 0 months. This could be due to sampling events are described in detail in section 3b. error, detrending, autocorrelation, or inadequate rep- Here, the basic performance of the model used in this resentation of processes in MIROC5.2, as suggested by study is examined based on the mean state, variability, Day et al. (2014). The reemergence in winter is stronger and diagnostic predictability (persistence) of sea ice. in MIROC5.2 than in the observations. In addition, the The model is also evaluated against observations pattern of the lagged correlation in MIROC5.2 is simi- [NSIDC, HadISST (Rayner et al. 2003), and ProjD (Ishii lar to that in the Hadley Centre Global Environment

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FIG. 2. Seasonal cycle of (a) the monthly mean SIE and (b) standard deviation of the SIE anomaly in a CTRL experiment. The NSIDC (red), HadISST (green; Rayner et al. 2003), and ProjD (blue; Ishii and Kimoto 2009) observations of the SIE from 1980 to 2009 are also included for comparison. Also shown are lagged correlations of the SIE anomaly from (c) a CTRL simulation, (d) NSIDC, (e) HadISST, and (f) ProjD observations, for each start month, against lead time as in Day et al. (2014). These data were linearly detrended before the calculations.

Model version 1.2 (HadGEM1.2; Shaffrey et al. 2009) Modeling and Assimilation System; Zhang and Rothrock [see Fig. 1 of Day et al. (2014)]. 2003; Schweiger et al. 2011) for all months (Fig. 3a). Similarly, the mean state and standard deviation for The standard deviation of the SIV is close to 1500 km3 the SIV are shown in Figs. 3a and 3b, and are almost the throughout the year and is larger than in the PIOMAS same as those indicated by magenta lines in Figs. 4b and data (Fig. 3b). The diagnostic predictability (persistence) 4d of Day et al. (2016) except for the length of the of the SIV (Fig. 3c)ishigherthanthatofSIE(Fig. 2c) simulation. The SIV in MIROC5.2 is larger by 3000– and the correlation pattern with lead times seems to 5000 km3 than in PIOMAS (Pan-Arctic Ice-Ocean be similar to that in HadGEM1.2 and the Max Planck

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FIG. 3. Seasonal cycle of (a) the monthly mean SIV and (b) standard deviation of the SIV anomaly in a CTRL experiment. The values for the PIOMAS data (magenta; Schweiger et al. 2011) from 1980 to 2009 are also included for comparison. (c) Lagged correlation of the SIV anomaly from a CTRL experiment, for each start month, against lead time as in Day et al. (2014). These data were linearly detrended before the calculations.

Institute Earth System Model (MPI-ESM; Jungclaus Overall, the Arctic sea ice in September has a high et al. 2013)[seeFig.5ofDay et al. (2014)]. concentration and thickness from the Chukchi and Beaufort Seas to the western central Arctic Ocean, and a b. Drastic ice reduction events low concentration and thickness from the To define an extreme sea ice loss event, years in which through the Kara and Laptev Seas to the East Siberian both the anomaly and year-to-year change in the Sep- Sea (Figs. 5a,c). Figures 5b and 5d show that in all years tember SIE were less than minus two standard de- the sea ice edge retreats poleward in the Laptev and viations were extracted from the time series of CTRL. East Siberian Seas, and thus the SIC is lower than the Figure 4 shows that three (model years 51, 56, and 57) climatology by 20%–60% along the ice edge and the drastic ice reductions occur even though the radiative corresponding sea ice thickness (SIT) is thinner by forcing is fixed at the present-day (year 2000) level. 0.5–1.0 m. Moreover, the SIC and SIT drastically de- Previously in a 600-yr control simulation of the Com- crease in the northern part of the Barents and Kara Seas munity Climate System Model version 3, Cullather and in year 51, in the Chukchi Sea in year 56, and in the Tremblay (2008) reported that three extraordinary Chukchi and Beaufort Seas in year 57. As a result, a minima occurred. In the 200-yr CTRL of this study, drastic sea ice reduction was identified in these three however, three events occur within just 7 years, and years. In particular, the year 57 SIC and SIT anomalies therefore they are likely to be related to each other. are the largest for all regions of the Arctic Ocean. These individual events and their composite are exam- The composite of the three events also shows that the ined below in more detail. Arctic sea ice retreats noticeably in the Pacific sector of

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6 2 FIG. 4. Time series of the September (a) SIE anomaly (10 km ) relative to a 200-yr clima- tology and (b) year-to-year change (106 km2) in a 200-yr CTRL experiment. The three vertical lines in (a) and (b) denote years 51, 56, and 57. In (a) and (b), plus or minus two standard deviations are indicated by horizontal dashed lines. These data were linearly detrended before the calculations. the Arctic Ocean, while the large negative anomalies in increased sea ice melting. It is therefore suggested that the SIC and SIT are overall lessened due to the com- the sea ice retreat from July to August is triggered by pensation of the opposite sign anomaly. Compared with anomalous winds associated with the ADA-like pattern the observations (red contours in Fig. 5a), the ice edge in and then further ice melt occurs due to the increased heat the Pacific side of the Arctic Ocean is located farther input through the open water (namely, ice–albedo feed- south and sea ice remains in the Kara Sea partly because back), leading to the drastic ice reductions in September of the model topography, while the spatial distribution of years 56 and 57. In year 51, the driver for sea ice re- of sea ice is similar to that observed in September 2007. treat is different from that described above because the Previous studies have revealed various causes for the SLP anomaly does not show an ADA-like pattern. record minimum sea ice in September 2007. For exam- However,theseaiceinJulymovesawayfromtheEast ple, anomalous atmospheric circulations, such as the Siberian Sea and the to the north, and Arctic dipole anomaly (ADA) (Wang et al. 2009), the therefore the ice edge retreats poleward from July to thinning of ice cover in spring (Stroeve et al. 2008), and August. This is partly because of a small dipole anomaly the enhanced inflow of warm Pacific water (Woodgate that is characterized by a weak high pressure anomaly et al. 2006, 2010), are thought to be contributing factors. over the Canada Basin and a low pressure anomaly across Possible mechanisms for the extreme ice reduction in northern . CTRL are next investigated based on the results for As shown in Fig. 6, the ADA-like pattern is suggested years 51, 56, and 57 and their composite. to be a key driver of the extreme ice reduction, except in year 51. To investigate the relationship between the c. Possible mechanisms September SIE and the ADA, an ADA index is defined The sea level pressure (SLP) in the summer [June– using the SLP in CTRL (Fig. 7a). Following Wang et al. August (JJA)] for years 56 and 57 is characterized by a (2009), an empirical orthogonal function (EOF) analysis high pressure anomaly over and the Canada of the summer (JJA) SLP north of 708N was conducted Basin and a low pressure anomaly over the Kara and and the second mode, which accounts for 17.7% of the Laptev Seas (Fig. 6a). This ADA-like pattern causes variance, is shown in Fig. 7b. In this study, the ADA winds to blow from the Siberian and Alaskan coasts index is defined as the difference in the area-averaged and sea ice is moved offshore by anomalous winds (see SLP values over the center of action (Greenland and the vectors in Fig. 6b), and thereby the ice edge retreats western central Arctic, indicated by dots in Fig. 7b), greatly from July (blue) to August (red). In August, the instead of the EOF time series, and these values are heat flux from the atmosphere to the ocean has a large normalized by their standard deviation. A positive value positive anomaly in the increased open water areas of the ADA index means favorable conditions for sea (Fig. 6c). Correspondingly the sea surface salinity (SSS) ice retreat, especially, in the western part of the Arctic decreases by 0.5 to 2.0 psu near the sea ice edge between Ocean. There is no significant correlation between the July and August (Fig. 6d), indicating a freshening due to ADA index and the September SIE anomaly (Fig. 7a;

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FIG. 5. (a) SIC, (b) SIC anomaly, (c) SIT, and (d) SIT anomaly in September (top to bottom) for years 51, 56, and 57 and for the composite of the three years. The black lines in all panels indicate the 15% SIC contours from a CTRL experiment. The red line in the composite of the SIC indicates the 15% SIC contours in September 2007 from the ProjD observations. The SIC and SIT anomalies were relative to a 200-yr climatology and were linearly detrended before the calculations. r 5 0.03). In this study, statistical significance of a cor- western Kara Sea and the northern parts of the East relation coefficient at a 95% confidence level is based Siberian and Chukchi Seas and are positive in the east- on a two-sided Student’s t test. While the ADA index ern Kara Sea, the , and the has significant correlation with the ice volume export (Fig. 7c). These spatial patterns in the SIC are similar to from the Fram Strait (r 5 0.53), the correlation between those in the SIC for year 56 (see Fig. 5). This indicates the September SIE anomaly and the ice volume export that the SIC in the Pacific Sector of the Arctic Ocean from the Fram Strait is only 0.02. The regressed SIC decreases by 2%–8% per one standard deviation of the anomalies onto the ADA index are negative in the ADA index. For year 56, the SIC decreases by 5%–18%

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FIG. 6. (a) JJA SLP anomaly, (b) sea ice vectors in July, (c) heat flux anomaly in August, and (d) sea surface salinity changes from July to August (top to bottom) for years 51, 56, and 57 and for the composite of the three years. In (a) and (b), 15% SIC contours in July (blue) and August (red) are superimposed. Anomalies were relative to a 200-yr climatology and were linearly detrended before the calculations. in this region, which accounts for about half of the two cases (years 56 and 57) lead to extreme sea ice loss. negative SIC anomaly in year 56. To consider the reason why sea ice does not necessarily However, the September SIE is not always extreme decrease drastically when there is a high ADA index, negative in years with a high positive ADA index results for year 28, which had the highest ADA index (Table 1). If a threshold of 0.6 is used following Wang (2.50), are shown in Fig. 8. The ice edge retreats from et al. (2009), there are 37 cases that have an ADA index July to August in the Pacific sector (Fig. 8f). However, higher than 0.6 standard deviations. Among them, only the SIC and SIT have positive anomalies in the northern

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FIG. 7. (a) Time series of the normalized September SIE (black), Arctic dipole anomaly (ADA) index (red), and ice volume export from the Fram Strait (blue), obtained from a 200-yr CTRL experiment. Four vertical lines denote the model years 28, 51, 56, and 57. (b) Spatial pattern of the second EOF mode for the JJA SLP anomaly north of 708N. (c) Correlation (colors) and regression (contours; %) co- efficients between the SIC anomaly in September (%) and the normalized ADA index. Note that the sign of the ADA index is reversed in (a). White dots in (b) denote grid points used for calculating the ADA index. Stippling in (c) indicates areas where the correlation coefficient is not statistically significant at the 95% confidence level. These data were linearly detrended before the calculations.

Barents Sea and Laptev Sea (Figs. 8a–d), which is dif- drastically in year 28. This suggests that atmospheric ferent from years 51, 56, and 57. As shown in Fig. 8e, the patterns with wind directions that cause offshore ice ADA structure is clearly formed, but sea ice is advected motion are a key driver of extreme ice reduction, in from the Barents Sea through the Kara Sea to the Laptev addition to the ADA. Sea under the influence of another dipole anomaly d. Effects of preconditioning that consists of a negative anomaly over the western central Arctic and positive (but weak) anomaly over Other factors have been identified that contribute to the Siberian continent. Furthermore, the ice edge ex- drastic ice reduction. As suggested by Hutchings and tends toward the East Siberian Sea from August to Perovich (2015), thinner (thicker) sea ice and warmer September due to the ice advection (not shown). This is (colder) ocean temperatures are favorable conditions one of the reasons why the sea ice does not decrease for sea ice retreat (advance). In the real world, the

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TABLE 1. September SIE anomaly, normalized ADA index, Fram Strait ice volume export, spring SIV anomaly, Arctic Ocean state anomaly, and horizontal ocean heat transport anomaly from the Atlantic and Pacific Oceans, for three extreme years. The OHT anomaly from the Atlantic Ocean is shown for 4 years before (years 47, 52, and 53 for the 51, 56, and 57 ice reduction events).

September Fram Strait ice Spring SIV OHT anomaly OHT anomaly SIE anomaly Normalized volume export anomaly Arctic Ocean from the from the 2 Model year (106 km2) ADA index (103 km3 month 1) (103 km3) state anomaly (8C) Atlantic (PW) Pacific (PW) 51 21.10 21.18 20.03 1.11 0.03 20.45 0.03 56 20.89 2.25 20.13 0.37 0.07 0.78 20.00 57 21.98 1.07 20.08 22.11 0.07 1.03 0.03 summer upper ocean has warmed over the last decade potential temperature averaged over the region north of and enhanced the sea ice melt (Steele et al. 2008, 2010). 658N for the oceanic state. The spring SIV anomaly has In addition, the thinning of sea ice in spring and the heat significant correlation with the subsequent September flux from the Pacific water have been thought to be key SIE anomaly (Figs. 9a,b). The lagged correlation be- factors for the drastic ice reduction observed in 2007 and tween the spring SIV and the September SIE is greatest 2012 (Stroeve et al. 2008; Woodgate et al. 2010; Serreze when the September SIE leads by 1 year (black line in and Stroeve 2015). Hence, the effects of preconditioning Fig. 10). Focusing on the years of drastic ice reduction on extreme sea ice loss are examined here, based on the (51, 56, and 57), the spring SIV greatly decreases after oceanic and sea ice states (Fig. 9). In this study, the the drastic reduction in the September SIE. This means spring [March–May (MAM)] mean SIV anomaly is used that a delay in the fall to winter ice growth due to the as an indicator for the sea ice state and the summer increased open water areas causes the reduction of the

FIG. 8. (a) SIC, (b) SIC anomaly, (c) SIT, and (d) SIT anomaly in September, (e) JJA SLP anomaly, (f) sea ice vectors in July, (g) heat flux anomaly in August, and (h) sea surface salinity changes from July to August, for year 28. In (a)–(d), the 15% SIC contour in September is superimposed as a black line. In (f) and (h), the 15% SIC contours in July and August are superimposed as blue and red lines, respectively. Anomalies were relative to a 200-yr climatology and were linearly detrended before the calculations.

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FIG. 9. Time series of (a) the September SIE anomaly and (b) the spring (MAM) SIV anomaly. (c) Depth–time plots of the summer (JJA) potential temperature anomaly averaged over the region north of 658N. Time series of (d) depth-averaged JJA potential temperature anomaly [Arctic Ocean state (AOS)] and the annual integrated horizontal ocean heat transport (OHT) anomaly from (e) the Atlantic Ocean across the circle of 608N and (f) the Pacific Ocean across the . The positive (negative) value is the northward (southward) OHT. In (d)–(f), thick black lines denote the 11-yr running average. Black vertical lines indicate the model years 51, 56, and 57. Anomalies were relative to a 200-yr climatology and were linearly detrended before the calculations.

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4 years after. In addition, large negative anomalies in the SIE during years 56 to 70 are consistent with the warm ocean state (Figs. 9a,c), indicating that the internal low- frequency variability of the ocean state in the Arctic Ocean interior contributed to delay the recovery from the negative anomaly in the SIE. The interannual and longer variabilities in the AOS are likely caused by the horizontal ocean heat transport (OHT) from the North Atlantic across the circle of 608N and the North Pacific across the Bering Strait (Figs. 9d–f). Focusing on the interannual time scale, the September SIE anomaly is significantly correlated with the OHT from the Pacific Ocean at a lag of 0 years and from the Atlantic Ocean at a lag of 4 years (blue and red lines in Fig. 10). The variability in the OHT in the Pacific and Atlantic Oceans leads that in the September SIE by 0–4 years and played a role in preconditioning the sea ice loss through the ocean temperature anomalies in the Arctic Ocean. The September SIE is therefore influ- FIG. 10. Lagged correlation coefficients between the September enced by the ocean temperature anomalies in the Arctic SIE anomaly and the spring SIV anomaly (black line with closed Ocean with 0- to 4-yr time scales through the OHT circles), those between the September SIE anomaly and OHT variability. Compared to a previous study (Holland et al. anomaly from the Atlantic (red line with closed circles) and Pacific (blue line with closed circles), and those between the September 2006), the time lag for the OHT to the Arctic Ocean is SIE anomaly and the depth-averaged summer potential tempera- longer by 2–3 years. For years 56 and 57, the positive ture anomaly (green line with closed circles). Horizontal dashed OHT anomalies from the Atlantic Ocean contribute to lines denote statistical significance at the 95% confidence level with the warmer state in the Arctic Ocean (Table 1), which 66 degrees of freedom based on a two-sided Student’s t test. These is a favorable condition for sea ice loss. For year 51, data were linearly detrended before computing the correlations. although the Arctic Ocean state has a small positive anomaly, the spring SIV and ADA index are unfavorable thicker ice in spring, as inferred from a previous study for sea ice loss. Compared to years 56 and 57, the re- (Cullather and Tremblay 2008). Although the spring treat of the ice edge in the region north of Svalbard SIV anomalies for the years 51 and 56 are positive, 1110 partly contributes to the drastic ice reduction for year and 370 km3, respectively (Table 1), that for the year 57 51 (Figs. 5 and 6). has a large negative value of 22110 km3 due to the drastic ice reduction in year 56. This indicates that the large negative anomaly in the spring SIV played a role in the 4. Perfect model ensemble prediction experiments preconditioning for the drastic ice reduction in year 57. Here, whether and to what extent extreme sea ice loss The potential temperature in the Arctic Ocean in- events can be predicted with climate models is exam- terior is characterized by the strong warm (cold) ined, based on the results of PRED. Following previous anomaly from the surface to a 20-m depth, due to the studies (Collins 2002; Day et al. 2014; Tietsche et al. heat gain (loss) associated with the negative (positive) 2014), predictability in this study is assessed by two SIE anomaly, and the internal variability on longer time metrics: the root-mean-square error (RMSE) and the scales (Figs. 9a,c). For statistical analyses, the Arctic anomaly correlation coefficient (ACC). The ensemble Ocean state (AOS) (Fig. 9d) is defined as the vertically RMSE is defined as averaged potential temperature anomaly from 0 to vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 154 m based on Fig. 9c. The AOS has a warm anomaly in u u Ns Nm the three extreme reduction years (Table 1). On the 5 t1 å å å 2 2 RMSE(t) [xkj(t) xij(t)] , (1) interannual time scale, the September SIE anomaly is n j51 i51 k 6¼ i significantly correlated with the AOS at lags of 21to 4 years (green line in Fig. 10). This suggests that the where xij(t) is the monthly quantity of interest, for instance ocean state in the Arctic Ocean is influenced by the the sea ice extent, at lead time t for the ith member of the September SIE anomaly a year before, and then plays jth ensemble and n 5 NsNm(Nm 2 1) 2 1 5 167, in which a role in preconditioning the sea ice variability until Ns (5 3) is the number of start dates and Nm (5 8) is the

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FIG. 11. ACC for (a) SIE and (b) SIV and normalized RMSE for (c) SIE and (d) SIV in PRED experiments started from 1 Apr (blue) and 1 Jul (red). Vertical lines denote September. Blue and red closed circles indicate where metrics have significant predictability at the 95% confidence level, based on an F test with 23 degrees of freedom. number of ensemble members. Here, the normalized year 51 and years 56 and 57. However, these three events pRMSEffiffiffi (NRMSE) is defined as the RMSE divided by are here grouped in the same category as extreme ice 2sctrl(t), where sctrl(t) is the standard deviation of reduction to investigate the predictability of drastic ice CTRL. There is significant predictability when the reduction events. NRMSE is significantly lower than 1 using an F test. As Figure 11 shows the ACC and NRMSE of the SIE and in Day et al. (2014), to compare the predictability of the SIV in PRED started from April (lead months 1 to 45) initialized perfect model predictions with the lagged and July (lead months 1 to 42). The ACC values are correlation of CTRL, the ACC is defined as statistically significant until October (lead months 7 and 4) in predictions started from April and July. The h 2 2 i [x (t) x(t)][x (t) x(t)] 6¼ ACC(t) 5 kj ij i,j,k i, (2) NRMSE values are significant during the first three lead h 2 2i [xij(t) x(t)] i,j months for both predictions. In PRED.APR, although the ACC values reemerge in August and September, the hi where i indicates the expected value, which is cal- NRMSE values are not significant. This indicates that culated by summing over the specified index, and x(t)is the predicted sea ice extents tend to decrease drastically the monthly mean climatology of x at lead time t.There but vary widely among the ensemble members. Focusing is significant predictability when the ACC is statisti- on the second prediction year, the ACC is significant in cally significant (0.413) at the 95% confidence level both PRED, while the NRMSE is large. From both the with 23 degrees of freedom based on a two-sided Stu- ACC and RMSE metrics, therefore, the drastic sea ice dent’s t test. The current study will judge whether reduction in September can be predicted from July but the drastic sea ice reduction is predictable based on not from April. In contrast, as suggested by previous the above two metrics. As mentioned in section 3c, studies (Day et al. 2014; 2016), the SIV is much more mechanisms for the drastic ice reduction differ between predictable than the SIE in predictions started from

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FIG. 12. (a) SIT anomaly in (left to right) April, July, August, and September for the composite of years 51, 56, and 57 in a CTRL experiment. Also shown are differences in SIT between a CTRL experiment and PRED experiments started from (b) 1 Apr and (c) 1 Jul for years 51, 56, and 57. Black and green lines indicate the 15% SIC contours in each month for a CTRL experiment and PRED ex- periments, respectively. both April and July (Figs. 11b,d) and is predictable up anomalies are consistent with those of CTRL. In PRED. to a lead time of 4 years. APR (Fig. 12b), the differences in the SIT between The regional predictability of sea ice is investigated by PRED and CTRL are small at lead months 1 and 4 comparing the ice edge and thickness for PRED with (April and July) but become large at lead months 5 and 6 that for CTRL (Fig. 12). In CTRL (Fig. 12a), negative (August and September). Particularly, the predicted SIT anomalies in the SIT are found in the Barents and parts increases by 0.5–1.0 m in the Pacific sector of the Arctic of the Kara, Laptev, and East Siberian Seas in April and Ocean, delaying the ice edge retreat. This may be be- July, and further extend to the Chukchi and Beaufort cause the model cannot predict the sea ice motion driven Seas in August in association with the retreat of the ice by the SLP dipole anomalies that developed from July to edge. In PRED.JUL (Fig. 12c), the sea ice edge location August in CTRL (Fig. 13a). In CTRL (Fig. 13a), the and the spatial patterns and magnitudes of the SIT composite of the three drastic reduction years shows the

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FIG. 13. As in Fig. 12, but for the SLP anomaly.

ADA-like SLP structures from July to August. Com- Using Eq. (1), the RMSE for the SIC and SIT is cal- paredtoPRED.APR(Fig. 13b), the differences in culated at a specific grid point in PRED started from the SLP between PRED and CTRL of PRED.JUL April and July. Figure 14a shows the SIC RMSE in July (Fig. 13c) are small in July to August, especially in the (lead months 4 and 1) and September (lead months 6 Canada Basin. These results suggest that the negative and 3) for PRED.APR and PRED.JUL. In PRED. SIE anomaly itself can be captured even in PRED. APR, the large SIC RMSE is found in the marginal ice APR, partly because of the initial ocean states, but zone of the Arctic Ocean and in the central Arctic. accurate prediction of the ADA-like SLP structure is In PRED.JUL, the SIC RMSE in July is consider- one of the key factors for a skillful forecast of drastic ably smaller over most of the Arctic Ocean and in ice reduction. September except for the central Arctic. The spatial As shown in Tietsche et al. (2014), spatial patterns of distribution of the SIT RMSE in PRED.APR shows potential forecast errors for the SIC and SIT are useful that the potential forecast errors are large in the ice information for operational forecasts of Arctic sea ice. edge, but small in the central Arctic basin. However, as

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FIG. 14. (a) SIC RMSE and (b) SIT RMSE in lead months 4 (July) and 6 (September) in PRED started from 1 Apr and those in lead months 1 (July) and 3 (September) in PRED started from 1 Jul. Stippling denotes areas where the potential predictive skill (not shown) is below 10%, following Tietsche et al. (2014). in the SIC, the potential errors for the SIT in PRED. shown that 10–15 ensemble members are needed to JUL are fairly small in the Arctic Ocean, while re- capture well the internal variability. In addition, Hawkins gions of large errors are found in the Pacific sector in et al. (2016) have concluded that probabilistic measures September. of predictability were not reliable even with 16 members, Similarly, the spatial forecast errors for the SLP and the which is the maximum ensemble size in APPOSITE. atmospheric temperature at 2 m above the surface (T2) Therefore, care may be needed when interpreting the are shown in Fig. 15. The spatial distribution of the SLP results of this study. RMSE in July (lead months 4 and 1) clearly shows that the potential forecast errors in the Arctic Ocean are higher in 5. Concluding remarks PRED.APR than PRED.JUL, whereas the differences between them are quite small in September (lead months We have conducted a 200-yr control simulation with 6and3)(Fig. 15a). Some consistent features are found in radiative forcing fixed at the year 2000 level using the spatial distribution of the T2 RMSE (Fig. 15b), except MIROC5.2 as part of the APPOSITE project and that the errors are small compared to the SLP. perfect ensemble prediction simulations to evaluate In this study, only eight ensemble members were the predictability of a drastic reduction in the summer used for the perfect model experiments based on the sea ice and to clarify driving mechanisms. The key APPOSITE protocol. However, Jahn et al. (2016) have implications regarding the present study are as follows:

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FIG. 15. As in Fig. 14, but for (a) SLP and (b) atmospheric temperature at 2 m above the surface.

1) Extreme Arctic sea ice reductions comparable to the years from the year 51. However, only a single climate years 2007 and 2012 in the real world occur even for model, MIROC5.2, was used in this study. Therefore, radiative forcing fixed at present-day levels (year 2000). the robustness of the results should be assessed using 2) Wind directions favoring the offshore ice motion, different climate models under APPOSITE. including ADA-like patterns, are one of the key Here the implications for the future are discussed by drivers of drastic ice reduction events. comparing the present study with previous studies. 3) Warm ocean states associated with the OHT from Holland et al. (2006) showed that abrupt changes in the the Atlantic and Pacific Oceans and the SIV in spring summer Arctic sea ice could occur in the early twenty-first play a role in preconditioning for sea ice loss. century and suggested that reductions in future green- 4) An extreme reduction in the September SIE has house gas emissions reduce the likelihood of abrupt predictability from 1 July but not for April, because events. In this study, however, CTRL showed that the forecast errors for the July SLP and those for the drastic reductions in the September SIE occur under SIC and SIT along the ice edge are large in PRED radiative forcing fixed at year 2000 levels. The sea ice started from April. loss from a 200-year climatology (7.10 million km2)is 21.10, 20.89, and 21.98 million km2 for years 51, 56, In this study, three extreme events occurred within just and 57, respectively. These are comparable to the 42%– 7 years. This is because the atmospheric circulations 93% and 31%–70% observed ice loss in 2007 and 2012 were favorable wind directions for sea ice loss and the from the 1981–2010 average, while the comparison of a warm states in the Arctic Ocean continued for 20–30 CTRL experiment in MIROC5.2 with observations may

Unauthenticated | Downloaded 10/05/21 08:20 PM UTC 1378 JOURNAL OF CLIMATE VOLUME 32 not be appropriate. This indicates that under the warm J. Geophys. Res., 106, 2441–2463, https://doi.org/10.1029/ climate state an extreme ice loss event can occur even 1999JC000113. without a warming trend. Regarding the frequency of Blanchard-Wrigglesworth, E., K. C. Armour, C. M. Bitz, and E. DeWeaver, 2011: Persistence and inherent predict- extreme events, Cullather and Tremblay (2008), who con- ability of Arctic sea ice in a GCM ensemble and obser- ducted a control simulation under 1990 constant forcing, vations. J. Climate, 24, 231–250, https://doi.org/10.1175/ suggested that extreme ice loss will increase under climate 2010JCLI3775.1. states warmer than the 1990 level. In a 3600-yr-long control ——, R. I. Cullather, W. Wang, J. Zhang, and M. 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Climate, 25, 1176–1193, https://doi.org/10.1175/ indicated that Arctic warming and sea ice loss are JCLI-D-11-00113.1. strongly linked to remote influences from the tropical Cullather, R. G., and L.-B. Tremblay, 2008: Analysis of Arctic sea and extratropical regions (e.g., Ding et al. 2017; ice anomalies in a coupled model control simulation. Arctic Tokinaga et al. 2017). Their findings provide motivation Sea Ice Decline: Observations, Projections, Mechanisms, and Implications, Geophys. Monogr., Vol. 180, Amer. Geophys. to investigate these mechanisms more thoroughly. To Union, 187–211. improve the predictability of sea ice associated with Day, J. J., J. C. Hargreaves, J. D. Annan, and A. Abe-Ouchi, 2012: internal variability, further analyses and numerical ex- Sources of multi-decadal variability in Arctic sea ice extent. periments focusing on the relationship between sea ice Environ. Res. 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