The impact of Arctic warming on the timing of Indian monsoon and ice season in the Sea of Okhotsk

2021 ◽  
Author(s):  
Elena Surovyatkina
Keyword(s):  
Pacific Ocean ◽  
Sea Ice ◽  
Early Onset ◽  
Sea Of Okhotsk ◽  
Indian Monsoon ◽  
The Arctic ◽  
Arctic Warming ◽  
Arctic Circle ◽  
The Sea Of Okhotsk ◽  
The Impact

<p>In 2020, the Arctic Circle warming in Siberia was extraordinary. Strong anticyclones have been dominant over a large area in Northern Siberia through spring. It resulted in an all-time high-temperature record in the Arctic Circle - more than 6°C above the average (1981–2010). Thus, it accelerated the melting of snow, ice, permafrost and has gotten the wildfire in Siberia off to an unusually early and severe start. The Arctic warming has repercussions not only for Siberia but for the entire Eurasia and the Northern Hemisphere. Specifically, the Arctic conditions affect atmospheric circulation in the Pacific Ocean and the strength and direction of trade winds in the tropical zone.</p><p>Here, I show that Arctic Circle warming has impacted the timing of monsoon and sea ice seasons. First, I found the observational evidence of Arctic warming causing colder than average temperatures over the east of Eurasia, Central Europe, and Central Asia. Notably, North Pakistan and Northern India saw temperatures distinctly below the long-term average (1981–2010): 4°C below from March to December. Second, I took this evidence into account while developing a new method for forecasting the sea-ice timing and the recent long-range forecasting method of monsoon season [1]. Third, based on the forecast results for 2020, I found that utilizing only recent trends is an inadequate strategy for predictions. However, considering the current Arctic warming outcomes in specific regions overcomes this problem and results in successful forecasts for both sea-ice and monsoon seasons.</p><p>The results imply that when North Pakistan's temperature is cooler than usual: (i) it slows down an advance of monsoon, (ii) it accelerates the cooling of the entire Indian subcontinent during withdrawal from northern Pakistan to the east coast of central India. Hence, North Pakistan's cooling in 2020 caused a protracted offensive and early end of the Indian summer monsoon, thus, shortening its duration. As a result, it led to the early onset of the seasonal wind reversal in the eastern Pacific Ocean in the middle of October and, therefore, to the surprisingly early onset of the winter monsoon in South Asia and India [2]. The consequences of this change in monsoon timing strongly affected 70% of the Indian population directly related to farming.</p><p>In the Sea of Okhotsk in 2020, the sea ice retreated early due to heatwaves in Siberia. In December, the onset date of ice season was around average, but ice grew faster than average, creating a hazard to navigation safety.</p><p>Hence, the proposed forecasting methodology applied to India and the Sea of Okhotsk opens new possibilities to forecasting monsoon and sea ice seasons around the globe.</p><p>The author acknowledges financial support from RFBR, project number 20-07-01071 .</p><p> </p><p>[1] Stolbova, V., E. Surovyatkina, B. Bookhagen, and J. Kurths (2016): Tipping elements of the Indian monsoon: Prediction of onset and withdrawal. GRL 43, 1–9 [doi:10.1002/2016GL068392]</p><p>[2] https://www.pik-potsdam.de/en/output/infodesk/forecasting-indian-monsoon</p>

Importance of Human-Induced Nitrogen Flux Increases for Simulated Arctic Warming

2021 ◽  
Vol 34 (10) ◽  
pp. 3799-3819
Author(s):  
Hyung-Gyu Lim ◽  
Jong-Yeon Park ◽  
John P. Dunne ◽  
Charles A. Stock ◽  
Sung-Ho Kang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
The Arctic ◽  
Biogeochemical Model ◽  
Biological Feedback ◽  
Arctic Warming ◽  
The Arctic Ocean ◽  
N Fluxes ◽  
The Impact ◽  
Chlorophyll Concentrations

AbstractHuman activities such as fossil fuel combustion, land-use change, nitrogen (N) fertilizer use, emission of livestock, and waste excretion accelerate the transformation of reactive N and its impact on the marine environment. This study elucidates that anthropogenic N fluxes (ANFs) from atmospheric and river deposition exacerbate Arctic warming and sea ice loss via physical–biological feedback. The impact of physical–biological feedback is quantified through a suite of experiments using a coupled climate–ocean–biogeochemical model (GFDL-CM2.1-TOPAZ) by prescribing the preindustrial and contemporary amounts of riverine and atmospheric N fluxes into the Arctic Ocean. The experiment forced by ANFs represents the increase in ocean N inventory and chlorophyll concentrations in present and projected future Arctic Ocean relative to the experiment forced by preindustrial N flux inputs. The enhanced chlorophyll concentrations by ANFs reinforce shortwave attenuation in the upper ocean, generating additional warming in the Arctic Ocean. The strongest responses are simulated in the Eurasian shelf seas (Kara, Barents, and Laptev Seas; 65°–90°N, 20°–160°E) due to increased N fluxes, where the annual mean surface temperature increase by 12% and the annual mean sea ice concentration decrease by 17% relative to the future projection, forced by preindustrial N inputs.

scholarly journals How relevant is the deposition of mercury onto snowpacks? – Part 2: A modeling study

2012 ◽  
Vol 12 (1) ◽  
pp. 2647-2706 ◽  
Author(s):  
D. Durnford ◽  
A. Dastoor ◽  
A. Ryzhkov ◽  
L. Poissant ◽  
M. Pilote ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Atmospheric Mercury ◽  
The Arctic ◽  
Hudson Bay ◽  
Arctic Circle ◽  
Surface Level ◽  
The Arctic Ocean ◽  
Simulated Concentration ◽  
The Impact

Abstract. An unknown fraction of mercury that is deposited onto snowpacks is revolatilized to the atmosphere. Determining the revolatilized fraction is important since mercury that enters the snowpack meltwater may be converted to highly toxic bioaccumulating methylmercury. In this study, we present a new dynamic physically-based snowpack/meltwater model for mercury that is suitable for large-scale atmospheric models for mercury. It represents the primary physical and chemical processes that determine the fate of mercury deposited onto snowpacks. The snowpack/meltwater model was implemented in Environment Canada's atmospheric mercury model GRAHM. For the first time, observed snowpack-related mercury concentrations are used to evaluate and constrain an atmospheric mercury model. We find that simulated concentrations of mercury in both snowpacks and the atmosphere's surface layer agree closely with observations. The simulated concentration of mercury in both in the top 30 cm and the top 150 cm of the snowpack, averaged over 2005–2009, is predominantly below 6 ng l−1 over land south of 66.5° N but exceeds 18 ng l−1 over sea ice in extensive areas of the Arctic Ocean and Hudson Bay. The average simulated concentration of mercury in snowpack meltwater runoff tends to be higher on the Russian/European side (>20 ng l−1) of the Arctic Ocean than on the Canadian side (<10 ng l−1). The correlation coefficient between observed and simulated monthly mean atmospheric surface-level GEM concentrations increased significantly with the inclusion of the new snowpack/meltwater model at two of the three stations (midlatitude, subarctic) studied and remained constant at the third (arctic). Oceanic emissions are postulated to produce the observed summertime maximum in concentrations of surface-level atmospheric GEM at Alert in the Canadian Arctic and to generate the summertime volatility observed in these concentrations at both Alert and Kuujjuarapik on subarctic Hudson Bay, Canada. We find that the fraction of deposited mercury that is revolatilized from snowpacks increases with latitude from 28% between 30 and 45° N, to 51% from 45 to 66.5° N, to 70% polewards of 66.5° N on an annual basis. Combining this latitudinal gradient with the latitudinally increasing coverage of snowpacks causes yearly net deposition as a fraction of gross deposition to decrease from 98% between 30 and 45° N to 85% between 45 and 66.5° N to 44% within the Arctic Circle. The yearly net deposition and net accumulation of mercury at the surface within the Arctic Circle north of 66.5° N are estimated at 153 and 117 Mg, respectively. We calculate that 63 and 45 Mg of mercury are deposited annually to the Arctic Ocean directly and indirectly via melting snowpacks, respectively. For terrestrial surfaces within the Arctic Circle, we find that 24 and 21 Mg of mercury are deposited annually directly and indirectly via melting snowpacks, respectively. Within the Arctic Circle, multi-season snowpacks gained an estimated average of 136 kg of mercury annually on land but lost an average of 133 kg annually over sea ice, possibly as a result of increased melting caused by rising temperatures. The developed snowpack/meltwater model can be used for investigating the impact of climate change on the snowpack/atmosphere exchange of mercury.

scholarly journals How relevant is the deposition of mercury onto snowpacks? – Part 2: A modeling study

2012 ◽  
Vol 12 (19) ◽  
pp. 9251-9274 ◽  
Author(s):  
D. Durnford ◽  
A. Dastoor ◽  
A. Ryzhkov ◽  
L. Poissant ◽  
M. Pilote ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Atmospheric Mercury ◽  
The Arctic ◽  
Hudson Bay ◽  
Arctic Circle ◽  
Surface Level ◽  
The Arctic Ocean ◽  
Simulated Concentration ◽  
The Impact

Abstract. An unknown fraction of mercury that is deposited onto snowpacks is revolatilized to the atmosphere. Determining the revolatilized fraction is important since mercury that enters the snowpack meltwater may be converted to highly toxic bioaccumulating methylmercury. In this study, we present a new dynamic physically-based snowpack/meltwater model for mercury that is suitable for large-scale atmospheric models for mercury. It represents the primary physical and chemical processes that determine the fate of mercury deposited onto snowpacks. The snowpack/meltwater model was implemented in Environment Canada's atmospheric mercury model GRAHM. For the first time, observed snowpack-related mercury concentrations are used to evaluate and constrain an atmospheric mercury model. We find that simulated concentrations of mercury in both snowpacks and the atmosphere's surface layer agree closely with observations. The simulated concentration of mercury in both in the top 30 cm and the top 150 cm of the snowpack, averaged over 2005–2009, is predominantly below 6 ng L−1 over land south of 66.5° N but exceeds 18 ng L−1 over sea ice in extensive areas of the Arctic Ocean and Hudson Bay. The average simulated concentration of mercury in snowpack meltwater runoff tends to be higher on the Russian/European side (>20 ng L−1) of the Arctic Ocean than on the Canadian side (<10 ng L−1). The correlation coefficient between observed and simulated monthly mean atmospheric surface-level gaseous elemental mercury (GEM) concentrations increased significantly with the inclusion of the new snowpack/meltwater model at two of the three stations (midlatitude, subarctic) studied and remained constant at the third (arctic). Oceanic emissions are postulated to produce the observed summertime maximum in concentrations of surface-level atmospheric GEM at Alert in the Canadian Arctic and to generate the summertime volatility observed in these concentrations at both Alert and Kuujjuarapik on subarctic Hudson Bay, Canada. We find that the fraction of deposited mercury that is revolatilized from snowpacks increases with latitude from 39% between 30 and 45° N, to 57% from 45 to 60° N, 67% from 60 to 66.5° N, and 75% polewards of 66.5° N on an annual basis. Combining this latitudinal gradient with the latitudinally increasing coverage of snowpacks causes yearly net deposition as a fraction of gross deposition to decrease from 98% between 30 and 45° N to 89% between 45 and 60° N, 73% between 60 and 66.5° N, and 44% within the Arctic Circle. The yearly net deposition and net accumulation of mercury at the surface within the Arctic Circle north of 66.5° N are estimated at 153 and 117 Mg, respectively. We calculate that 58 and 50 Mg of mercury are deposited annually to the Arctic Ocean directly and indirectly via melting snowpacks, respectively. For terrestrial surfaces within the Arctic Circle, we find that 29 and 16 Mg of mercury are deposited annually directly and indirectly via melting snowpacks, respectively. Within the Arctic Circle, multi-season snowpacks on land and over sea ice gained, on average, an estimated 0.1 and 0.4 Mg yr−1 mercury, respectively, from 2000–2005. The developed snowpack/meltwater model can be used for investigating the impact of climate change on the snowpack/atmosphere exchange of mercury.

scholarly journals The Impact of the Siberian High and Aleutian Low on the Sea-Ice Cover of the Sea of Okhotsk

1990 ◽  
Vol 14 ◽  
pp. 226-229 ◽  
Author(s):  
Claire L. Parkinson
Keyword(s):  
Sea Ice ◽  
Atmospheric Pressure ◽  
Sea Of Okhotsk ◽  
Ice Cover ◽  
Passive Microwave ◽  
Aleutian Low ◽  
Siberian High ◽  
The Sea Of Okhotsk ◽  
Ice Conditions ◽  
The Impact

Comparison of monthly averaged sea-ice distributions in the Sea of Okhotsk with atmospheric pressure data during the four winters having passive-microwave sea-ice coverage from the Nimbus 5 satellite, 1973–76, revealed a strong apparent relationship between the extent of the sea-ice cover and the influence of the Siberian High atmospheric pressure system. Examination of data for the years 1978–86, having passive-microwave coverage from the Nimbus 7 satellite, reveals that the strong correspondence found for 1973–76 between Okhotsk sea-ice extents and the Siberian High was not maintained in the 1978–86 period. A weaker correspondence continued, however, between the sea ice and the combined Siberian High/Aleutian Low system. A Siberian High/Aleutian Low index was created, and the correlation coefficient between that index and sea-ice extents in the midwinter month of February is 0.97 for the 1973–76 period and 0.52 for the 1978–86 period. Primary reasons for the lack of a consistently strong monthly averaged ice/atmosphere correspondence are: the various oceanographic influences on the sea-ice cover, the failure of monthly averages to reflect fully the important shorter-term interactions between the ice and the atmosphere, and the fact that ice conditions in one month are influenced by ice conditions in previous months.

scholarly journals Reassessing seasonal sea ice predictability of the Pacific-Arctic sector using a Markov model

2021 ◽  
Author(s):  
Yunhe Wang ◽  
Xiaojun Yuan ◽  
Haibo Bi ◽  
Mitchell Bushuk ◽  
Yu Liang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Markov Model ◽  
Bering Sea ◽  
Sea Of Okhotsk ◽  
Regional Model ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
The Sea Of Okhotsk ◽  
The Bering Sea

Abstract. In this study, a regional linear Markov model is developed to assess seasonal sea ice predictability in the Arctic Pacific sector. Unlike an earlier pan-Arctic Markov model that was developed with one set of variables for all seasons, the regional model consists of four seasonal modules with different sets of predictor variables, accommodating seasonally-varying driving processes. A series of sensitivity tests are performed to evaluate the predictive skill in cross-validated experiments and to determine the best model configuration for each season. The prediction skill, as measured by the percentage of grid points with significant correlations (PGS), increased by 75 % in the Bering Sea and 16 % in the Sea of Okhotsk relative to the pan-Arctic model. The regional Markov model's skill is also superior to the skill of an anomaly persistence forecast. Sea ice concentration (SIC) trends significantly contribute to the model skill. However, the model retains skill for detrended sea ice extent predictions up to 6 month lead times in the Bering Sea and the Sea of Okhotsk. We find that surface radiative fluxes contribute to predictability in the cold season and geopotential height and winds play an indispensable role in the warm-season forecast, contrasting to the thermodynamic processes dominating the pan-Arctic predictability. The regional model can also capture the seasonal reemergence of predictability, which is missing in the pan-Arctic model.

scholarly journals The Impact of the Siberian High and Aleutian Low on the Sea-Ice Cover of the Sea of Okhotsk

1990 ◽  
Vol 14 ◽  
pp. 226-229 ◽  
Author(s):  
Claire L. Parkinson
Keyword(s):  
Sea Ice ◽  
Atmospheric Pressure ◽  
Sea Of Okhotsk ◽  
Ice Cover ◽  
Passive Microwave ◽  
Aleutian Low ◽  
Siberian High ◽  
The Sea Of Okhotsk ◽  
Ice Conditions ◽  
The Impact

Comparison of monthly averaged sea-ice distributions in the Sea of Okhotsk with atmospheric pressure data during the four winters having passive-microwave sea-ice coverage from the Nimbus 5 satellite, 1973–76, revealed a strong apparent relationship between the extent of the sea-ice cover and the influence of the Siberian High atmospheric pressure system. Examination of data for the years 1978–86, having passive-microwave coverage from the Nimbus 7 satellite, reveals that the strong correspondence found for 1973–76 between Okhotsk sea-ice extents and the Siberian High was not maintained in the 1978–86 period. A weaker correspondence continued, however, between the sea ice and the combined Siberian High/Aleutian Low system. A Siberian High/Aleutian Low index was created, and the correlation coefficient between that index and sea-ice extents in the midwinter month of February is 0.97 for the 1973–76 period and 0.52 for the 1978–86 period. Primary reasons for the lack of a consistently strong monthly averaged ice/atmosphere correspondence are: the various oceanographic influences on the sea-ice cover, the failure of monthly averages to reflect fully the important shorter-term interactions between the ice and the atmosphere, and the fact that ice conditions in one month are influenced by ice conditions in previous months.

scholarly journals Evaluation of Wave-Ice Parameterization Models in WAVEWATCH III® Along the Coastal Area of the Sea of Okhotsk During Winter

2021 ◽  
Vol 8 ◽  
Author(s):  
Shinsuke Iwasaki ◽  
Junichi Otsuka
Keyword(s):  
Sea Ice ◽  
Correlation Coefficient ◽  
Coastal Area ◽  
Empirical Formula ◽  
Sea Of Okhotsk ◽  
Open Ocean ◽  
Wave Fields ◽  
Wavewatch Iii ◽  
The Sea Of Okhotsk ◽  
The Impact

Ocean surface waves tend to be attenuated by interaction with sea ice. In this study, six sea ice models in the third-generation wave model WAVEWATCH III® (WW3) were used to estimate wave fields over the Sea of Okhotsk (SO). The significant wave height (Hs) and mean wave period (Tm) derived from the models were evaluated with open ocean and ice-covered conditions, using SO coastal area buoy observations. The models were validated for a period of 3 years, 2008–2010. Additionally, the impact of sea ice on wave fields was demonstrated by model experiments with and without sea ice. In the open ocean condition, the root-mean square error (RMSE) and correlation coefficient for hourly Hs are 0.3 m and 0.92, and for hourly Tm 0.97 s and 0.8. In contrast, for the ice-covered condition, the averaged RMSE and correlation coefficient from all models are 0.44 m (1.6 s) and 0.8 (0.6) for Hs (Tm), respectively. Therefore, except for the bias, the accuracy of model results for the ice-covered condition is lower than for the open water condition. However, there is a significant difference between the six sea ice models. For Hs, the empirical formula whereby attenuation depends on the frequency relatively agrees with the buoy observation. For Tm, the empirical formula that is a function of Hs is better than those of other simulations. In addition, the simulations with sea ice drastically improved the wave field bias in coastal areas compared to the simulations without sea ice. Moreover, sea ice changed the monthly Hs (Tm) by more than 1 m (3 s) in the northwestern part of the SO, which has a high ice concentration.

scholarly journals Standardization of electromagnetic–induction measurements of Sea-ice thickness in polar and Subpolar Seas

2006 ◽  
Vol 44 ◽  
pp. 240-246 ◽  
Author(s):  
K. Tateyama ◽  
K. Shirasawa ◽  
S. Uto ◽  
T. Kawamura ◽  
T. Toyota ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Electromagnetic Induction ◽  
Sea Water ◽  
Chukchi Sea ◽  
The Arctic ◽  
Average Error ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
The Sea Of Okhotsk

AbstractElectromagnetic–induction (EM) instruments can be used to estimate Sea-ice thickness because of the large contrast in the conductivities of Sea ice and Sea water, and are currently used in investigations of Sea-ice thickness. In this Study we analyze Several Snow, ice and Sea-water Samples and attempt to derive an appropriate formula to transform the apparent conductivity obtained from EM measurements to the total thickness of Snow and ice for all regions and Seasons. This was done to Simplify the EM tuning procedure. Surface EM measurement transects with the instrument at varying heights above the ice were made in the Chukchi Sea, off East Antarctica, in the Sea of Okhotsk and in Saroma-ko (lagoon). A Standardized transformation formula based on a one-dimensional multi-layer model was developed that also considers the effects of water-filled gaps between deformed ice, a Saline Snow Slush layer, and the increase in the footprint Size caused by increasing the instrument height. The overall average error in ice thickness determined with the Standardized transform was <7%, and the regional average errors were 2.2% for the Arctic, 7.0% for the Antarctic, 6.5% for the Sea of Okhotsk and 4.4% for Saroma-ko.

scholarly journals Contributors to linkage between Arctic warming and East Asian winter climate

2021 ◽  
Author(s):  
Xinping Xu ◽  
Shengping He ◽  
Yongqi Gao ◽  
Botao Zhou ◽  
Huijun Wang
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Initial Conditions ◽  
Wave Train ◽  
East Asian ◽  
Surface Air Temperature ◽  
General Circulation Models ◽  
The Arctic ◽  
Winter Climate ◽  
Arctic Warming

AbstractPrevious modelling and observational studies have shown discrepancies in the interannual relationship of winter surface air temperature (SAT) between Arctic and East Asia, stimulating the debate about whether Arctic change can influence midlatitude climate. This study uses two sets of coordinated experiments (EXP1 and EXP2) from six different atmospheric general circulation models. Both EXP1 and EXP2 consist of 130 ensemble members, each of which in EXP1 (EXP2) was forced by the same observed daily varying sea ice and daily varying (daily climatological) sea surface temperature (SST) for 1982–2014 but with different atmospheric initial conditions. Large spread exists among ensemble members in simulating the Arctic–East Asian SAT relationship. Only a fraction of ensemble members can reproduce the observed deep Arctic warming–cold continent pattern which extends from surface to upper troposphere, implying the important role of atmospheric internal variability. The mechanisms of deep Arctic warming and shallow Arctic warming are further distinguished. Arctic warming aloft is caused primarily by poleward moisture transport, which in conjunction with the surface warming coupled with sea ice melting constitutes the surface-amplified deep Arctic warming throughout the troposphere. These processes associated with the deep Arctic warming may be related to the forcing of remote SST when there is favorable atmospheric circulation such as Rossby wave train propagating from the North Atlantic into the Arctic.

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