Recent observations show dramatic changes of the Arctic atmosphere-ice-ocean system, including a rapid warming in the intermediate Atlantic water of the Arctic Ocean. Here it is demonstrated through the analysis of a vast collection of previously unsynthesized observational data, that over the twentieth century Atlantic water variability was dominated by low-frequency oscillations (LFO) on time scales of 50-80 yr. Associated with this variability, the Atlantic water temperature record shows two warm periods in the 1930s-40s and in recent decades and two cold periods earlier in the century and in the 1960s-70s. Over recent decades, the data show a warming and salinification of the Atlantic layer accompanied by its shoaling and, probably, thinning. The estimate of the Atlantic water temperature variability shows a general warming trend; however, over the 100-yr record there are periods (including the recent decades) with short-term trends strongly amplified by multidecadal variations. Observational data provide evidence that Atlantic water temperature, Arctic surface air temperature, and ice extent and fast ice thickness in the Siberian marginal seas display coherent LFO. The hydrographic data used support a negative feedback mechanism through which changes of density act to moderate the inflow of Atlantic water to the Arctic Ocean, consistent with the decrease of positive Atlantic water temperature anomalies in the late 1990s. The sustained Atlantic water temperature and salinity anomalies in the Arctic Ocean are associated with hydrographic anomalies of the same sign in the Greenland-Norwegian Seas and of the opposite sign in the Labrador Sea. Finally, it is found that the Arctic air-sea-ice system and the North Atlantic sea surface temperature display coherent low-frequency fluctuations. Elucidating the mechanisms behind this relationship will be critical to an understanding of the complex nature of low-frequency variability found in the Arctic and in lower-latitude regions.

Arctic atmospheric variability during the industrial era ( 1875 - 2000) is assessed using spatially averaged surface air temperature ( SAT) and sea level pressure (SLP) records. Air temperature and pressure display strong multidecadal variability on timescales of 50 - 80 yr [ termed low-frequency oscillation (LFO)]. Associated with this variability, the Arctic SAT record shows two maxima: in the 1930s - 40s and in recent decades, with two colder periods in between. In contrast to the global and hemispheric temperature, the maritime Arctic temperature was higher in the late 1930s through the early 1940s than in the 1990s. Incomplete sampling of large-amplitude multidecadal fluctuations results in oscillatory Arctic SAT trends. For example, the Arctic SAT trend since 1875 is 0.09 +/- 0.03degreesC decade(-1), with stronger spring- and wintertime warming; during the twentieth century ( when positive and negative phases of the LFO nearly offset each other) the Arctic temperature increase is 0.05 +/- 0.04degreesC decade(-1), similar to the Northern Hemispheric trend ( 0.06degreesC decade(-1)). Thus, the large-amplitude multidecadal climate variability impacting the maritime Arctic may confound the detection of the true underlying climate trend over the past century. LFO-modulated trends for short records are not indicative of the long-term behavior of the Arctic climate system. The accelerated warming and a shift of the atmospheric pressure pattern from anticyclonic to cyclonic in recent decades can be attributed to a positive LFO phase. It is speculated that this LFO-driven shift was crucial to the recent reduction in Arctic ice cover. Joint examination of air temperature and pressure records suggests that peaks in temperature associated with the LFO follow pressure minima after 5 - 15 yr. Elucidating the mechanisms behind this relationship will be critical to understanding the complex nature of low-frequency variability.

Three observational data sets are used to construct a continuous record (1850–2001) of April ice edge position in the Barents Sea: two sets of Norwegian ice charts (one from 1850 to 1949 and the other from 1966 to 2001) and Soviet aircraft reconnaissance ice extent charts from 1950 to 1966. The 152-year April ice extent series is subdivided into three sub-periods: 1850–1899, 1900–1949 and 1950–2001. For each of these study sub-periods, a mean April ice edge and a set of anomalies (differences in position between a given April and the mean April ice edge) are computed. The calculations show the mean ice edge position retreated north-eastward over the 152-year period, with the greater retreat seen in the changes from the 1850–1899 sub-period to the 1900–1949 sub-period. The distribution of the standard deviation of the ice edge anomaly over the linear distance along the mean ice edge shows no substantial difference between any of the three periods of the study. Within each study period, the maximum variation is observed in the sector bounded by the 25° E and 49° E meridians, which covers the main pathway of the warmer water flow from the Norwegian Sea.

This study has been motivated by reports of extraordinary change in the Arctic Ocean observed in recent decades. Most of these observations are based on synoptic measurements, while evaluation of anomalies requires an understanding of the underlying long‐term variability. Historical climatologies give reference means, and while these datasets are a reliable source of the mean Atlantic Layer temperature, they significantly underestimate variability. Using historical data, we calculated statistical parameters for selected Arctic Ocean regions. They demonstrate a high level of Atlantic Layer temperature variability in the Nansen Basin and sea‐surface salinity fluctuations on the Siberian shelf and the Amundsen Basin. These estimates suggest strong limitations on our ability to define amplitudes of anomalies by comparing recent synoptic measurements with climatologies, especially for regions characterized by strong variability.

Data collected by the International Arctic Buoy Programme from 1979 to 1998 are analyzed to obtain statistics of sea level pressure (SLP) and sea ice motion (SIM). The annual and seasonal mean fields agree with those obtained in previous studies of Arctic climatology. The data show a 3-hPa decrease in decadal mean SLP over the central Arctic Ocean between 1979-88 and 1989-98. This decrease in SLP drives a cyclonic trend in SIM, which resembles the structure of the Arctic Oscillation (AO).Regression maps of SIM during the wintertime (January-March) AO index show 1) an increase in ice advection away from the coast of the East Siberian and Laptev Seas, which should have the effect of producing more new thin ice in the coastal flaw leads; 2) a decrease in ice advection from the western Arctic into the eastern Arctic; and 3) a slight increase in ice advection out of the Arctic through Fram Strait. Taken together, these changes suggest that at least part of the thinning of sea ice recently observed over the Arctic Ocean can be attributed to the trend in the AO toward the high-index polarity.Rigor et al. showed that year-to-year variations in the wintertime AO imprint a distinctive signature on surface air temperature (SAT) anomalies over the Arctic, which is reflected in the spatial pattern of temperature change from the 1980s to the 1990s. Here it is shown that the memory of the wintertime AO persists through most of the subsequent year: spring and autumn SAT and summertime sea ice concentration are all strongly correlated with the AO index for the previous winter. It is hypothesized that these delayed responses reflect the dynamical influence of the AO on the thickness of the wintertime sea ice, whose persistent "footprint'' is reflected in the heat fluxes during the subsequent spring, in the extent of open water during the subsequent summer, and the heat liberated in the freezing of the open water during the subsequent autumn.

Arctic variability is dominated by multi‐decadal fluctuations. Incomplete sampling of these fluctuations results in highly variable arctic surface‐air temperature (SAT) trends. Modulated by multi‐decadal variability, SAT trends are often amplified relative to northern‐hemispheric trends, but over the 125‐year record we identify periods when arctic SAT trends were smaller or of opposite sign than northern‐hemispheric trends. Arctic and northern‐hemispheric air‐temperature trends during the 20th century (when multi‐decadal variablity had little net effect on computed trends) are similar, and do not support the predicted polar amplification of global warming. The possible moderating role of sea ice cannot be conclusively identified with existing data. If long‐term trends are accepted as a valid measure of climate change, then the SAT and ice data do not support the proposed polar amplification of global warming. Intrinsic arctic variability obscures long‐term changes, limiting our ability to identify complex feedbacks in the arctic climate system.

The statistics of surface air temperature observations obtained from buoys, manned drifting stations, and meteorological land stations in the Arctic during 1979-97 are analyzed. Although the basic statistics agree with what has been published in various climatologies, the seasonal correlation length scales between the observations are shorter than the annual correlation length scales, especially during summer when the inhomogeneity between the ice-covered ocean and the land is most apparent. During autumn, winter, and spring, the monthly mean correlation length scales are approximately constant at about 1000 km; during summer, the length scales are much shorter, that is, as low as 300 km. These revised scales are particularly important in the optimal interpolation of data on surface air temperature (SAT) and are used in the analysis of an improved SAT dataset called International Arctic Buoy Programme/Polar Exchange at the Sea Surface (IABP/POLES). Compared to observations from land stations and the Russian North Pole drift stations, the IABP/POLES dataset has higher correlations and lower rms errors than previous SAT fields and provides better temperature estimates, especially during summer in the marginal ice zones. In addition, the revised correlation length scales allow data taken at interior land stations to be included in the optimal interpretation analysis without introducing land biases to grid points over the ocean. The new analysis provides 12-h fields of air temperatures on a 100-km rectangular grid for all land and ocean areas of the Arctic region for the years 1979-97.The IABP/POLES dataset is then used to study spatial and temporal variations in SAT. This dataset shows that on average melt begins in the marginal seas by the first week of June and advances rapidly over the Arctic Ocean, reaching the pole by 19 June, 2 weeks later Freeze begins at the pole on 16 August, and the Freeze isotherm advances more slowly than the melt isotherm. Freeze returns to the marginal seas a month later than at the pole, on 21 September. Near the North Pole, the melt season length is about 58 days, while near the margin, the melt season is about 100 days. A trend of +1 degrees C (decade)(-1) is found during winter in the eastern Arctic Ocean, but a trend of -1 degrees C (decade)(-1) is found in the western Arctic Ocean. During spring, almost the entire Arctic shows significant warming trends. In the eastern Arctic Ocean this warming is as much as 2 degrees C (decade)(-1). The spring warming is associated with a trend toward a lengthening of the melt season in the eastern Arctic. The western Arctic, however, shows a slight shortening of the melt season. These changes in surface air temperature over the Arctic Ocean are related to the Arctic Oscillation, which accounts for more than half of the surface air temperature trends over Alaska, Eurasia, and the eastern Arctic Ocean but less than half in the western Arctic Ocean.

AbstractThe Russian Arctic and Antarctic Research Institute (AARI) has conducted long-term meteorological studies over the Arctic basin and adjacent Siberian seas. Standard measurements of precipitation and snow geophysical properties were made, consistent with methods recommended by the World Meteorological Organization (WMO). An extensive set of snow and precipitation data has been collected during the last 40 years and has been assembled into a digital database. These data are now kept at the National Snow and Ice Data Center (NSIDC) and World Data Center A for Glaciology. The geophysical properties of snow and sea ice together affect the conductive, turbulent, and radiative energy exchanges between the ocean and atmosphere. The spatial and temporal variations in these exchanges have an impact on virtually all the physical processes operating across this interface. This paper describes some of the basic characteristics of these snow and precipitation data, including seasonal and interannual variability.

Based on 4 years of central arctic atmospheric sounding data and 10 years of surface wind data from Soviet drifting stations, combined with geostrophic winds from the arctic buoy program, the following relation between air-ice stress, tau, and the surface geostrophic wind speed, g, can be recommended tau = rhoC(g)2gamma2g2, rohC(g)2 = 0.85 x 10(-3) (nt m-2) (m2 s-2)-2, C(g) = 0.024, November-March, rhoC(g)2 = 1.10 x 10(-3) (nt m-2)(m2 s-2)-1, C(g) = 0.029, June-August. If the spacing between sea level pressure values from buoys is greater than 400 km, such as in the arctic buoy array, a speed enhancement factor of gamma = 1.3 should be applied to correct for insufficient sampling and smoothing in generating the geostrophic wind field. If the spacing is of order 100 km or less, then gamma = 1.0. An inflow angle alpha, the angle between the geostrophic wind and the surface wind, of 33-degrees- is recommended for winter and 23-degrees- for summer. Values for the transition months April, May, September and October can be interpolated between winter and summer values. The winter value of Cg is calculated 3 ways: from the Soviet station surface wind-geostrophic wind speed ratio using suitable 10 m drag coefficients, from regression equations based on surface-900 mb stability, and from the AIDJEX analyses. The summer values are based on the surface wind-geostrophic wind ratio, model, and AIDJEX derived values. There is considerable day-to-day variability in atmospheric stability and geostrophic coefficients, but no statistically significant variation in the within-season monthly mean and median values; month-to-month variability is within 5 % for C(g) and 5-degrees- for alpha for the winter and summer seasons. Neglect of stability variations for daily cases can contribute an error of +/- 40 % in the relation between surface stress and geostrophic wind speed squared compared with using constant winter values. Use of atmospheric temperature profiles from satellites may increase the accuracy of C(g) for daily cases by providing an estimate of lower-atmospheric inversion strength.