Air temperature changes in the arctic from 1801 to 1920

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Rajmund Przybylak ◽  
Zsuzsanna Vízi ◽  
Przemysław Wyszyński
Keyword(s):  
Air Temperature ◽  
The Arctic ◽  
Temperature Changes

scholarly journals Recent air-temperature changes in the Arctic

2007 ◽  
Vol 46 ◽  
pp. 316-324 ◽  
Author(s):  
Rajmund Przybylak
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Marked Rise ◽  
Temperature Changes ◽  
The Pacific ◽  
Complete Series ◽  
Linear Effects ◽  
Autumn And Winter ◽  
Abrupt Rise

AbstractA detailed analysis of the spatial and temporal changes in mean seasonal and annual surface air temperature (SAT) in the Arctic is presented mainly for the period 1951–2005. Mean seasonal and annual homogenized and complete series of SAT from up to 35 Arctic stations were used in the analysis. The focus in this paper is on the 11 years 1995–2005, a period which saw dramatic warming in the Arctic (>1˚C for annual values in relation to the 1951–90 mean). An abrupt rise in SAT occurred in the mid-1990s and was most pronounced in autumn and winter (>2˚C). The greatest warming in the period 1995–2005 occurred in the Pacific and Canadian regions (>1˚C), while the lowest was in the Siberian region (0.82˚C). This period has been the warmest since at least the 17th century. In particular, 2005 was an exceptionally warm year (>2˚C in relation to the 1951–90 mean) and was warmer than 1938, the warmest year in the 20th century. The seasonal and annual trends of the areally averaged Arctic SAT for the periods 1936–2005, 1951–2005 and 1976–2005 are positive, with the exception of winter and autumn for the first period. The majority of trends calculated for the last two periods are statistically significant. While there are varying opinions about the forces driving the present warming, it seems likely that the marked rise in SAT in the mid-1990s (mainly from 1994 to 1995) was caused by (i) a set of natural factors, (ii) non-linear effects of greenhouse-gas loading, or (iii) the combined effect of these two groups of factors.

scholarly journals On the Differences in Precipitation Type Between the Arctic, Antarctica and Tibetan Plateau

2021 ◽  
Vol 9 ◽  
Author(s):  
Diyi Yang ◽  
Minghu Ding ◽  
Tingfeng Dou ◽  
Wei Han ◽  
Weigang Liu ◽  
...  
Keyword(s):  
Air Temperature ◽  
The Arctic ◽  
Temperature Threshold ◽  
Temperature Changes ◽  
Zhongshan Station ◽  
Comparison Results ◽  
Monthly Distribution ◽  
Precipitation Type ◽  
Parameterized Model ◽  
Increasing Trends

Under the effect of global warming, more precipitation will shift to rainfall in cryospheric regions. Considering the influence of the precipitation type on surface energy and mass cycles, it is important to determine the specific precipitation features and to classify the precipitation type in key areas correctly. We analyzed the monthly distribution, variations in each precipitation type’s annual days, and trends based on daily precipitation and air temperature observations from six tripolar stations. The results indicated that snow dominated the precipitation type at Zhongshan station (69.4°S, 76.4°E) throughout the year, while the Greatwall station (62.2°S, 59.0°W) exhibited a relatively diverse precipitation type distribution and significant seasonal cycles. Compared to the Greatwall station, every precipitation type was less frequently encountered at the Barrow (71.3°N, 156.8°W), Coral Harbour (64.2°N, 83.4°W), Linzhi (29.6°N, 94.5°E), and Maqu stations (34°N, 102.1°E), in which all the sites demonstrated classical reverse seasonal variation. A consistent trend across the years was found regarding the trends of the different precipitation types, except at the Greatwall and Coral Harbour stations. Due to snow/rain conditions partly converting into sleet conditions, which may be related to air temperature changes and synoptic atmospheric activities, inconsistent increasing trends of the sleet days were observed compared to the snow/rain days. Furthermore, a hyperbolic parameterized model was also fitted to determine the air temperature threshold of precipitation type transitions in this paper. According to the threshold comparison results, a warm bias in the temperature threshold was found at the warm stations. We also proposed that high relative humidity and low freezing levels were the likely reasons for the ERA5 reanalysis datasets. Finally, this paper’s fitted parameterized model was proven to perform better than the ERA5 reanalysis datasets through validation. This preliminary research provides observational evidence and possible interpretation of the mechanism of precipitation type changes in tripolar areas.

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

Modeling air temperature changes in Northern Asia

2014 ◽  
Vol 122 ◽  
pp. 14-22 ◽  
Author(s):  
A. Onuchin ◽  
M. Korets ◽  
A. Shvidenko ◽  
T. Burenina ◽  
A. Musokhranova
Keyword(s):  
Air Temperature ◽  
Northern Asia ◽  
Temperature Changes

scholarly journals Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

2017 ◽  
Vol 8 (4) ◽  
pp. 963-976 ◽  
Author(s):  
Jaak Jaagus ◽  
Mait Sepp ◽  
Toomas Tamm ◽  
Arvo Järvet ◽  
Kiira Mõisja
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Large Scale ◽  
Regime Shifts ◽  
The Arctic ◽  
Dry Period ◽  
Snow Cover Duration ◽  
Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting of trends and regime shifts during 1951–2015. Trend analysis is realised using the Mann–Kendall test and regime shifts are detected with the Rodionov test (sequential t-test analysis of regime shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at all 12 stations by 0.3–0.4 K decade−1. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days decade−1. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March and also in June. A time series of specific runoff measured at 21 stations had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 L s−1 km−2 decade−1, while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May have notably decreased indicating the shift of the runoff maximum to the earlier time, i.e. from April to March. Air temperature, precipitation, snow cover duration and specific runoff of rivers are highly correlated in winter determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter of 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in annual specific runoff correspond to the alternation of wet and dry periods. A dry period started in 1964 or 1963, a wet period in 1978 and the next dry period at the beginning of the 21st century.

scholarly journals Extreme Warming in the Kara Sea and Barents Sea during the Winter Period 2000–16

2017 ◽  
Vol 30 (22) ◽  
pp. 8913-8927 ◽  
Author(s):  
Svenja H. E. Kohnemann ◽  
Günther Heinemann ◽  
David H. Bromwich ◽  
Oliver Gutjahr
Keyword(s):  
Sea Ice ◽  
Air Temperature ◽  
Barents Sea ◽  
Kara Sea ◽  
The Arctic ◽  
Winter Period ◽  
Limited Area ◽  
Spatial And Temporal Variability ◽  
Large Variability ◽  
Air Temperatures

The regional climate model COSMO in Climate Limited-Area Mode (COSMO-CLM or CCLM) is used with a high resolution of 15 km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 20°C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice. Also, the 30-km version of the Arctic System Reanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 1°C for the ocean and sea ice area. Thus, ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.5°C yr−1 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 70°N; for CCLM the warming amounts to an average of almost 5°C for 2002/03–2011/12.

scholarly journals Ranges of moisture-source temperature estimated from Antarctic ice cores stable isotope records over glacial–interglacial cycles

2012 ◽  
Vol 8 (3) ◽  
pp. 1109-1125 ◽  
Author(s):  
R. Uemura ◽  
V. Masson-Delmotte ◽  
J. Jouzel ◽  
A. Landais ◽  
H. Motoyama ◽  
...  
Keyword(s):  
Air Temperature ◽  
Ice Core ◽  
Ice Cores ◽  
Regression Equations ◽  
Temperature Changes ◽  
Source Temperature ◽  
Moisture Source ◽  
Isotopic Model ◽  
Temperature Proxy ◽  
Coefficient Of Regression

Abstract. A single isotope ratio (δD or δ18O) of water is widely used as an air-temperature proxy in Antarctic ice core records. These isotope ratios, however, do not solely depend on air-temperature but also on the extent of distillation of heavy isotopes out of atmospheric water vapor from an oceanic moisture source to a precipitation site. The temperature changes at the oceanic moisture source (Δ Tsource) and at the precipitation site (Δ Tsite) can be retrieved by using deuterium-excess (d) data. A new d record from Dome Fuji, Antarctica spanning the past 360 000 yr is presented and compared with records from Vostok and EPICA Dome C ice cores. In previous studies, to retrieve Δ Tsource and Δ Tsite information, different linear regression equations were proposed using theoretical isotope distillation models. A major source of uncertainty lies in the coefficient of regression, βsite which is related to the sensitivity of d to Δ Tsite. We show that different ranges of temperature and selections of isotopic model outputs may increase the value of βsite by more than a factor of two. To explore the impacts of this coefficient on reconstructed temperatures, we apply for the first time the exact same methodology to the isotope records from the three Antarctica ice cores. We show that uncertainties in the βsite coefficient strongly affect (i) the glacial–interglacial magnitude of Δ Tsource; (ii) the imprint of obliquity in Δ Tsource and in the site-source temperature gradient. By contrast, we highlight the robustness of Δ Tsite reconstruction using water isotopes records.

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

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