Abstract

A linear Markov model has been developed to simulated and predict the short-term climate change in the Antarctic, with particular emphasis on sea ice variability. Seven atmospheric variables along with sea ice were chosen to define the state of the Antarctic climate, and the multivariate empirical orthogonal functions of these variables were used as the building blocks of the model. The predictive skill of the model was evaluated in a cross-validated fashion, and a series of sensitivity experiments was carried out. In both hindcast and forecast experiments, the model showed considerable skill in predicting the anomalous Antarctic sea ice concentration up to 1 yr in advance, especially in austral winter and in the Antarctic dipole regions. The success of the model is attributed to the domination of the Antarctic climate variability by a few distinctive modes in the coupled air–sea–ice system and to the model's ability to detect these modes. This model is presently being used for the experimental seasonal forecasting of Antarctic sea ice, and a current prediction example is presented.

Abstract

This study explores the impact of the Indian Ocean dipole (IOD) on the Southern Hemisphere sea ice variability. Singular value decomposition (SVD) of September–November sea ice concentration and sea surface temperature (SST) anomalies reveals patterns of El Niño–Southern Oscillation (ENSO) in the Pacific and the IOD in the equatorial Indian Ocean. The relative importance of the IOD’s impact on sea ice in the Pacific sector of Antarctica is difficult to assess for two reasons: 1) ENSO generates larger anomalies in the Pacific and Weddell Sea and 2) many of the positive (negative) IODs co-occur with El Niño (La Niña). West of the Ross Sea, sea ice growth can be attributed to the negative heat fluxes associated with cold meridional flow between high and low pressure cells generated by the effects of the IOD. However, the locations of these positive and negative pressure anomaly centers tend to appear north of the sea ice zone during combined ENSO–IOD events, reducing the influence of the IOD on sea ice. The IOD influence is at a maximum in the region west of the Ross Sea. When ENSO is removed, sea ice in the Indian Ocean (near 60°E) increases because of cold outflows west of low pressure centers while sea ice near 90°E decreases because of the warm advection west of a high pressure center located south of Australia.

Abstract

This study statistically evaluates the relationship between Antarctic sea ice extent and global climate variability. Temporal cross correlations between detrended Antarctic sea ice edge (SIE) anomaly and various climate indices are calculated. For the sea surface temperature (SST) in the eastern equatorial Pacific and tropical Indian Ocean, as well as the tropical Pacific precipitation, a coherent propagating pattern is clearly evident in all correlations with the spatially averaged (over 12° longitude) detrended SIE anomalies (〈SIE*〉). Correlations with ENSO indices imply that up to 34% of the variance in 〈SIE*〉 is linearly related to ENSO. The 〈SIE*〉 has even higher correlations with the tropical Pacific precipitation and SST in the tropical Indian Ocean. In addition, correlation of 〈SIE*〉 with global surface temperature produces four characteristic correlation patterns: 1) an ENSO-like pattern in the Tropics with strong correlations in the Indian Ocean and North America (r > 0.6); 2) a teleconnection pattern between the eastern Pacific region of the Antarctic and western–central tropical Pacific; 3) an Antarctic dipole across the Drake Passage; and 4) meridional banding structures in the central Pacific and Atlantic expending from polar regions to the Tropics, even to the Northern Hemisphere.

The SIE anomalies in the Amundsen Sea, Bellingshausen Sea, and Weddell Gyre of the Antarctic polar ocean sectors show the strongest polar links to extrapolar climate. Linear correlations between 〈SIE*〉 in those regions and global climate parameters pass a local significance test at the 95% confidence level. The field significance, designed to account for spatial coherence in the surface temperature, is evaluated using quasiperiodic colored noise that is more appropriate than white noise. The fraction of the globe displaying locally significant correlations (at the 95% confidence level) between 〈SIE*〉 and global temperature is significantly larger, at the 99.5% confidence level, than the fraction expected given quasiperiodic colored noise in place of the 〈SIE*〉. Based on EOF analysis and multiplicity theory, the four teleconnection patterns the authors found are the ones reflecting correlations most likely to be physically meaningful.

Abstract

This study reveals that sea ice in the Barents and Kara Seas plays a crucial role in establishing a new Arctic coupled climate system. The early winter sea ice before 1998 shows double dipole patterns over the Arctic peripheral seas. This pattern, referred to as the early winter quadrupole pattern, exhibits the anticlockwise sequential sea ice anomalies propagation from the Greenland Sea to the Barents–Kara Seas and to the Bering Sea from October to December. This early winter in-phase ice variability contrasts to the out-of-phase relationship in late winter. The mean temperature advection and stationary wave heat flux divergence associated with the atmospheric zonal wave-2 pattern are responsible for the early winter in-phase pattern.

Since the end of the last century, the early winter quadrupole pattern has broken down because of the rapid decline of sea ice extent in the Barents–Kara Seas. This remarkable ice retreat modifies the local ocean–atmosphere heat exchange, forcing an anomalous low air pressure over the Barents–Kara Seas. The subsequent collapse of the atmospheric zonal wave-2 pattern is likely responsible for the breakdown of the early winter sea ice quadrupole pattern after 1998. Therefore, the sea ice anomalies in the Barents–Kara Seas play a key role in establishing new atmosphere–sea ice coupled relationships in the warming Arctic.

Abstract

CTD/STD data from 24 cruises in the North Pacific are studied for their vertical salinity structure and compared to bottle observations. A triple-salinity minimum is found in two separated regions in the eastern North Pacific. In the first region, bounded by the northern edge of the subarctic frontal zone and the 34°N front between 160° and 150°W, a middle salinity minimum is found below the permanent pycnocline in the density range of 26.0 and 26.5 σ_θ. This middle minimum underlies Reid's shallow salinity minimum and overlies the North Pacific Intermediate Water (NPIW). In the second region, southeast of the first, a seasonal salinity minimum appears above the shallow salinity minimum at densities lower than 25.1 σ_θ. The shallow salinity minimum and the NPIW can be found throughout year, while the seasonal minimum only appears in summer and fall.

The middle and shallow salinity minima, as well as the seasonal minimum, originate at the sea surface in the northeast Pacific. The properties at the minima depend on the surface conditions in their source areas. The source of the middle minimum is the winter surface water in a narrow band between the gyre boundary and the subarctic front west of 170°W. The shallow salinity minimum is generated in winter and is present throughout the year. The seasonal salinity minimum has the same source area as the shallow salinity minimum but is formed in summer and fall at lower density and is not present in winter.

A tropical shallow salinity minimum found south of 18°N does not appear to be connected with the shallow salinity minimum in the eastern North Pacific. South of 20°N, the shallow salinity minimum and the NPIW appear to merge into a thick, low salinity water mass. When an intrusion of high salinity water breaks through this low salinity water mass south of 18°N, this tropical salinity minimum appears at the same density as the shallow salinity minimum. Though the water mass of the tropical minimum is deprived from the water in the shallow salinity minimum, the formation of the vertical minimum is different.

Abstract

North Pacific monthly sea surface temperature (SST) anomalies are more persistent than a first-order Markev process, often lasting for more than 5 months. Sea surface temperature persistence undergoes an annual cycle that is attributable to the depth of the surface mixed layer and to the annual cycle of focing. For a given lag, the pattern correlation is minimum when it involves SST during the summer months and maximum when it involves SST during the winter months. Average winter SST anomalies that have exhibited greatest persistence during the last four decades have been negative in the central North Pacific and positive along the West Coast but antipersistent SST anomalies have not confermed to a repeated pattern. The atmospheric 700 mb height anomalies associated with high persistence SST cases indicate that strong SST persistence is associated with long-lasting atmospheric anomaly patterns. For highly persistent January SST anamalies, 700 mb anomalies often last from December through February. The high persistence 700 mb anomalies tend to be negative over the east-central North Pacific and positive over North America, with strong teleconnections. This pattern translates to strengthened westerlies over the subtropics and weakened westerlies in middle latitudes across the North Pacific—a zonal wind profile that is nearly opposite to that which appeared in low persistency SST cases.

Over the four decades since 1947, North Pacific SST persistence has undergone substantial multiyear variability, and has increased significantly since the beginning of this record. Related low-frequency fluctuations as well as linear trends, have occurred in the zonal mean subtropical westerlies across the North Pacific and in related large-scale atmospheric indices, the PNA pattern and the Southern Oscillation Index.

Abstract

The recent accelerated Arctic sea ice decline has been proposed as a possible forcing factor for midlatitude circulation changes, which can be projected onto the Arctic Oscillation (AO) and/or North Atlantic Oscillation (NAO) mode. However, the timing and physical mechanisms linking AO responses to the Arctic sea ice forcing are not entirely understood. In this study, the authors suggest a connection between November sea ice extent in the Barents and Kara Seas and the following winter’s atmospheric circulation in terms of the fast sea ice retreat and the subsequent modification of local air–sea heat fluxes. In particular, the dynamical processes that link November sea ice in the Barents and Kara Seas with the development of AO anomalies in February is explored. In response to the lower-tropospheric warming associated with the initial thermal effect of the sea ice loss, the large-scale atmospheric circulation goes through a series of dynamical adjustment processes: The decelerated zonal-mean zonal wind anomalies propagate gradually from the subarctic to midlatitudes in about one month. The equivalent barotropic AO dipole pattern develops in January because of wave–mean flow interaction and firmly establishes itself in February following the weakening and warming of the stratospheric polar vortex. This connection between sea ice loss and the AO mode is robust on time scales ranging from interannual to decadal. Therefore, the recent winter AO weakening and the corresponding midlatitude climate change may be partly associated with the early winter sea ice loss in the Barents and Kara Seas.

Abstract

Recent Arctic sea ice changes have important societal and economic impacts and may lead to adverse effects on the Arctic ecosystem, weather, and climate. Understanding the predictability of Arctic sea ice melting is thus an important task. A vector autoregressive (VAR) model is evaluated for predicting the summertime (May–September) daily Arctic sea ice concentration on the intraseasonal time scale, using only the daily sea ice data and without direct information of the atmosphere and ocean. The intraseasonal forecast skill of Arctic sea ice is assessed using the 1979–2012 satellite data. The cross-validated forecast skill of the VAR model is found to be superior to both the anomaly persistence and damped anomaly persistence at lead times of ~20–60 days, especially over northern Eurasian marginal seas and the Beaufort Sea. The daily forecast of ice concentration also leads to predictions of ice-free dates and September mean sea ice extent. In addition to capturing the general seasonal melt of sea ice, the model is also able to capture the interannual variability of the melting, from partial melt of the marginal sea ice in the beginning of the period to almost a complete melt in the later years. While the detailed mechanism leading to the high predictability of intraseasonal sea ice concentration needs to be further examined, the study reveals for the first time that Arctic sea ice can be predicted statistically with reasonable skill at the intraseasonal time scales given the small signal-to-noise ratio of daily data.

Abstract

This paper summarizes advances in research on tropical–polar teleconnections, made roughly over the last decade. Elucidating El Niño–Southern Oscillation (ENSO) impacts on high latitudes has remained an important focus along different lines of inquiry. Tropical to polar connections have also been discovered at the intraseasonal time scale, associated with Madden–Julian oscillations (MJOs). On the time scale of decades, changes in MJO phases can result in temperature and sea ice changes in the polar regions of both hemispheres. Moreover, the long-term changes in SST of the western tropical Pacific, tropical Atlantic, and North Atlantic Ocean have been linked to the rapid winter warming around the Antarctic Peninsula, while SST changes in the central tropical Pacific have been linked to the warming in West Antarctica. Rossby wave trains emanating from the tropics remain the key mechanism for tropical and polar teleconnections from intraseasonal to decadal time scales. ENSO-related tropical SST anomalies affect higher-latitude annular modes by modulating mean zonal winds in both the subtropics and midlatitudes. Recent studies have also revealed the details of the interactions between the Rossby wave and atmospheric circulations in high latitudes. We also review some of the hypothesized connections between the tropics and poles in the past, including times when the climate was fundamentally different from present day especially given a larger-than-present-day global cryosphere. In addition to atmospheric Rossby waves forced from the tropics, large polar temperature changes and amplification, in part associated with variability in orbital configuration and solar irradiance, affected the low–high-latitude connections.

Abstract

A linear Markov model has been developed to predict sea ice concentration (SIC) in the pan-Arctic region at intraseasonal to seasonal time scales, which represents an original effort to use a reduced-dimension statistical model in forecasting Arctic sea ice year-round. The model was built to capture covariabilities in the atmosphere–ocean–sea ice system defined by SIC, sea surface temperature, and surface air temperature. Multivariate empirical orthogonal functions of these variables served as building blocks of the model. A series of model experiments were carried out to determine the model’s dimension. The predictive skill of the model was evaluated by anomaly correlation and root-mean-square errors in a cross-validated fashion . On average, the model is superior to the predictions by anomaly persistence, damped anomaly persistence, and climatology. The model shows good skill in predicting SIC anomalies within the Arctic basin during summer and fall. Long-term trends partially contribute to the model skill. However, the model still beats the anomaly persistence for all targeted seasons after linear trends are removed. In winter and spring, the predictability is found only in the seasonal ice zone. The model has higher anomaly correlation in the Atlantic sector than in the Pacific sector. The model predicts well the interannual variability of sea ice extent (SIE) but underestimates its accelerated long-term decline, resulting in a systematic model bias. This model bias can be reduced by the constant or linear regression bias corrections, leading to an improved correlation skill of 0.92 by the regression bias correction for the 2-month-lead September SIE prediction.