저작자표시-비영리-변경금지 2.0 대한민국 이용자는 아래의 조건을 따르는 경우에 한하여 자유롭게
l 이 저작물을 복제, 배포, 전송, 전시, 공연 및 방송할 수 있습니다. 다음과 같은 조건을 따라야 합니다:
l 귀하는, 이 저작물의 재이용이나 배포의 경우, 이 저작물에 적용된 이용허락조건 을 명확하게 나타내어야 합니다.
l 저작권자로부터 별도의 허가를 받으면 이러한 조건들은 적용되지 않습니다.
저작권법에 따른 이용자의 권리는 위의 내용에 의하여 영향을 받지 않습니다. 이것은 이용허락규약(Legal Code)을 이해하기 쉽게 요약한 것입니다.
Disclaimer
저작자표시. 귀하는 원저작자를 표시하여야 합니다.
비영리. 귀하는 이 저작물을 영리 목적으로 이용할 수 없습니다.
변경금지. 귀하는 이 저작물을 개작, 변형 또는 가공할 수 없습니다.
이학석사학위논문
Response of atmospheric blocks to the arctic amplification in the idealized
model experiments
역학 모델 실험에서 북극 온난화가 중위도 저지고 기압에 미치는 영향
2017년 2월
서울대학교 대학원
지구환경과학부
유 영 은
Abstract
This study explores the responses of jet and atmospheric blocks to Arctic amplification (AA) in a simple atmospheric general circulation model. External heat is forced in the pole to mimic the AA. As the temperature increases in the pole, it changes the eddies, especially in midlatitudes, and the jet moves towards the equator. Meanwhile, the storm track moves equatorward along with the jet. We do not confirm that the jet gets wavier while in the midlatitudes where the jet gets stronger, whereas it is identified that the jet gets wavier at high latitudes. Though the jet gets weaker and wavier in high latitudes, no clear blocking increase is found. Rather, blocks are shown to increase in the midlatitudes, regarded as in relation to the equatorward shift of the storm track.
Keywords:Arctic amplification, jet, atmospheric block
Student Number:2015-20468
Contents
Abstract ... i
List of Table ... iii
List of Figures... iii
1. Introduction...1
2. Model configurations ...4
2.1. Equilibrium experiments ...4
2.2. Transient experiment...6
3. Methods ...8
3.1. Waviness index...8
3.2. Blocking index...9
4. Results ... 11
4.1. Response of jet ... 11
4.2. Response of waviness... 14
4.3. Response of blockings... 14
5. Conclusions... 17
6. Further studies ... 18
References ... 19
Table... 24
Figures ... 25
List of Table
Table 1.The number of detected blocks.
List of Figures
Figure 1. (a) Prescribed zonal mean equilibrium temperature, and (b) model
responses of the mean zonal wind (green) and temperature (black) for control simulation without heating.
Figure 2. (a) Zonally symmetric forced heat profile, (b) time-averaged zonal mean temperature (contour; 10 K intervals), and (c) time-averaged zonal mean zonal wind (contour; 10 m s-1interval). The differences from the control experiments are displayed by shading and colored contours in (b) and (c), respectively.
Figure 3. Eddy momentum convergence (black) and its difference (colored) from control simulations for (a) high, (b) low, and (c) mixed frequency eddies.
They respectively describe deep, medium, and shallow AA from top to bottom.
The contour intervals are respectively (a) 2×10-5 m s-2and 3×10-6m s-2, (b, c) 2×10-5m s-2and 2×10-6m s-2for the black and colored contours.
Figure 4. Zonal difference of the mean zonal wind at 300 hPa between the transient and control experiments. The contour interval is 0.2 m s-1.
Figure 5.(a, c) Time-averaged zonal mean waviness index and zonal wind at
500 hPa (b, d) and their differences with control simulation. Black, blue, green,
and red lines respectively indicate control, AA-shallow, AA-medium, and AA- deep experiments.
Figure 6.The same as in Fig. 5, but for blocked areas per a day as a function of latitude.
Figure 7.(a) The number of blocks by duration and (b, d) the differences with control simulation. Black, blue, green, and red bars respectively indicate control, AA-shallow, AA-medium, and AA-deep experiments.
Figure 8.Time-averaged zonal mean eddy kinetic energy (contour; interval of 50 m2s-2) for (a) total, (b) high frequency, and (c) high frequency eddies. From top to bottom, each row displays the eddy kinetic energy of CTRL, AA-Deep, AA-Medium, and AA-Shallow, respectively. Shading shows the difference from the control simulation.
1. Introduction
Global warming has meant that temperatures have increased steeply in most parts of the world. In particular, temperatures in the Arctic’s lower troposphere have increased by approximately twice the rate of other regions as a result of various mechanisms such as increased sensible and latent heat from the sea surface because of sea ice decline, the ice-albedo feedback effect, and moist intrusion into the Arctic (Kinnard et al., 2011; Lesins et al., 2012;
Park et al., 2015; Serreze et al., 2009; Screen and Simmonds, 2010; Woods and Caballero, 2016). Furthermore, it seems that the steep warming in the Arctic, called Arctic Amplification (AA), is connected to recent more frequent extreme weather phenomena in the midlatitudes. Many studies have attempted to determine the impact of AA on the midlatitudes circulation and weather systems (Barnes and Polvani, 2015; Cohen et al., 2014; Francis and Vavrus, 2012; Liu et al., 2012; Overland et al., 2015; Screen, 2014).
Francis and Vavrus (2012) proposed that with AA, the eastward progression of Rossby waves in the upper-level flow slows, which induces more frequent severe weather events, because the jets are weakened and wave amplitudes are increased. In addition, Liu et al. (2012) suggested that rapid temperature increases in the Arctic made atmospheric blocks occur more frequently in the midlatitudes. As atmospheric blocks are abnormal quasi- stationary systems, they stay in place for several days, obstruct weather
systems, and thus lead to severe weather events. In summary, they hypothesized that AA would increase extreme weather events in the midlatitudes. This hypothesis proposed by Francis and Vavrus (2012) and Liu et al. (2012) called FVL12, remains controversial.
This hypothesis has many pros and cons. One of them is from the criteria that define how the waves are wavy. Difficulties in defining the wave amplitude mean that various types of waviness indices were considered. Using different methodologies from that of Francis and Vavrus (2012) meant that Barnes (2013), Barnes et al. (2014), and Screen and Simmonds (2013) documented no significant trends. They concluded that in the result shown in Francis and Vavrus (2012), the notion that waves seemed to be amplified was unclear due to their definition. The other problem is that the observed data was over too short a period to verify the waviness trends, atmospheric blocks, or extremes. Some previous studies have reported that the responses to AA were still in the range of natural variability and thus remained uncertain (Screen et al., 2013; Rinke et al., 2013). Since the observation data was over too short a period to analyze the impacts of AA on atmospheric circulation such as the jet, storm tracks, and extremes, several studies have tried to investigate the impacts of AA via model experiments (Cohen et al., 2014; Deser et al., 2016; Hassanzadeh et al., 2014; Screen et al., 2013; Wu and Smith, 2016).
In response to AA, the jet moved equatorward in AMIP and idealized dynamic
core experiments (Deser et al., 2016; Wu and Simth, 2016). Furthermore, Hassanzadeh et al. (2014) conducted a series of AA simulations for an idealized dynamic core by changing the meridional temperature gradient in equilibrial temperatures and determined that the atmospheric blocks decreased in accordance with the variance decrease of 500 hPa geopotential height in response to decreasing meridional temperature gradient.
In this study, we revisit this issue step-by-step to determine the dynamical linkage between AA and extreme phenomena in the midlatitudes.
We perform idealized model experiments with external forcing in the Arctic to examine the impact of AA on atmospheric circulation and blocks. The specific applied model configurations will be introduced in detail in Section 2. The waviness and blocking indexes will be shown in Section 3. Section 4 analyzes the change of jet, waviness, and blocks in response to AA. Finally, Section 5 summarizes key results.
2. Model configurations
2.1. Equilibrium experiments
The dynamic core of the Geophysical Fluid Dynamics Laboratory (GFDL) was used to verify the FVL12 hypothesis in this study. Even though this model is integrated with only primitive equations and omits other schemes such as physical and chemical processes, it is well known that it can provide a reasonable simulation of atmospheric circulation (e.g. Held and Suarez, 1994).
In addition, it does not include hydrosphere, cryosphere, and biosphere, which interact with atmosphere in complex manners. The FVL12 hypothesis argues the dynamical linkage is not relevant with clouds and others, between the Arctic and the atmosphere. We consider it suitable to understand the FVL12 hypothesis dynamically from this perspective.
This model is forced to the prescribed equilibrium temperature via Newtonian relaxation. The prescribed equilibrium temperature is defined as follows (Fig. 1a).
= 200 K, 315 K − 60 − 10 log − 10
, , respectively represent the latitude, pressure, and reference pressure.
κ is R/ = 2/7. The equilibrium temperature is represented as a function of the latitude and pressure. The above equation does not describe the stratospheric temperature profile and is determined as isotherms (200 K) in
the upper level, and the equation in the second set of brace represents the tropospheric temperature profile. The second term in the equation represents the meridional temperature decrease, and the third term is related to the vertical static stability. The parameters used in these two terms are the same as those in Held and Suarez (1994). The last term stands for the seasonality, herein winter. Since the impact of AA is significant in autumn and winter in the FVL12 hypothesis, the thermal equator was set to 5 °S to mimic the boreal winter climatology. Although this model does not have any physical and chemical processes and is only composed of primitive equation sets, it simulates the winter background climatology well (Fig. 1b).
The model had a horizontal resolution of T85 (~1.4 ° × 1.4 °), 40 vertical layers with a lid of 0.0027 sigma, and was interpolated to 2.5° × 2.5°
for analysis. Five thousand days were integrated with random initial vorticity perturbations, and 4,500 days after spin-up was used for the analysis. The attenuation coefficients used were those of Chen and Zurita-Gotor (2008), and the other physical parameters were same as in Held and Suarez (1994).
In addition, we assumed a flat surface with no topography. In a real atmosphere, there exists stationary waves, primarily formed on topography and land-sea contrast, and they play a crucial role in atmospheric blocks.
Nevertheless, preferentially assuming the simplest case requires that we exclude the effects of topography.
Besides additional zonally symmetric heat (Q) is forced to the polar region to simulate AA. Hassanzadeh et al. (2014) performed similar experiments as those of this study. However, they simulated AA by changing the meridional equilibrium temperature gradient from 40 K to 80 K by 10 K steps, and additional heat was thus forced over the whole hemisphere.
Compared to Hassanzadeh et al. (2014), heat was only forced in the Arctic. The forced thermal profile is described below (Fig. 2a).
= ∗ sin − 90
− 90 ∙ 2 sin
− 1
− 1 ∙ 2
In the above equation, represents the intensity of the forced heat,yand σ are the latitude and sigma levels, and and are the edge points where the additional heat became 0. In this study, is set to 0.5 K day-1, which is stronger than AA in real atmosphere. is tested for 0.6 (shallow heating), 0.3 (medium heating) and 0.1 (deep heating) for fixed at 60 °N.
2.2. Transient experiment
A transient experiment is conducted to examine how fast the midlatitude circulation reacts to AA. The transient experiment consists of 200 ensembles that are integrated over 80 days from different initial conditions.
Initial conditions for each ensemble come from the control experiment at intervals of 50 days after spin-up. The intensity of the heat rate is set to 1 K
day-1, which is stronger than the equilibrium experiments, to obtain distinct results. Within the first five days, deep heating ( =0.1) is gradually turned on in the Arctic. After that, the heat is continually forced. The other parameters are the same as in the equilibrium experiments.
3. Methods
3.1. Waviness index
Previous studies designed several waviness indices to quantify how the waves became wavier with a weakened jet (e.g. Francis and Vavrus, 2012;
Barnes, 2013; Francis and Vavrus, 2015). However, the designed methodologies are essentially based on geometric features. For example, Francis and Vavrus (2012) analyzed the trend of the maximum latitudes of ridge peaks and the number of the grid points that exceeded the specific latitudes for certain isopleths to demonstrate that the waves had been amplified. Barnes (2013) also similarly evaluated the seasonal and daily differences between the maximum and minimum latitudes for certain isopleths. While these methodologies look similar, their results are in conflict.
In other words, it is sensitive to define the waviness.
Here, we adopted the local-finite amplitude wave activity of 500 hPa geopotential height (Chen et al., 2015; Huang and Nakamura, 2016; Nakamura and Solomon, 2010). We considered this more reasonable because the local- finite amplitude wave activity is based on dynamical properties; it is defined as follows. The areas (S) enclosed by the isopleths are calculated for a certain isopleth at 500 hPa geopotential height. If the areas integrated from the pole to the certain latitude are same as S, the latitude is called the equivalent latitude.
Waviness is then defined as the sum of the area enclosed by the isopleth and
the equivalent latitude line, and could represent the local wave amplification;
this is explained in more detail in Chen et al. (2015).
3.2. Blocking index
The hybrid blocking index (Dunn-Sigouin et al., 2013) is used to detect atmospheric blocks. It combines the two classic blocking indices: the Dole–Gordon (DG) index (Dole and Gordon, 1983) and the Tibaldi–Molteni index (Tibaldi and Molteni, 1990). The DG index identifies the atmospheric blocks as a pattern of persistent geopotential anomalies that block weather systems. It gives us a general 2D (longitude–latitude) index. However, it has disadvantages in that it misjudges the quasi-stationary eddies as blocks and cannot be applied to short-term data, e.g. numerical weather prediction data, because of the difficulty defining anomalies. However, the TM index is determined as the reversal of the meridional geopotential height gradient at 500 hPa. Though the TM index is a 1D index as a function of longitude, there is no requirement to define anomalies, which makes it applicable to weather forecasting. A hybrid blocking index could avoid misdetection and capture the spatial structure of blocks by combining these two indices.
Blocks are recognized as follows. First, closed positive anomalies that satisfy the minimum value (A) and the minimum spatial scale (S) are identified as potential blocks. Closed anomalies must be overlapped by at least the
minimum area (O) for two days and be maintained for a minimum period (D).
The meridional geopotential height gradient should be reversed on more than one day for at least one point of potential blocks. When all of these conditions are satisfied, it is defined as atmospheric blocks. In this study, climatology is defined as time-mean quantity and anomalies are the deviation from the climatology. The criteria used in the definition are set to (A) = 1.5σ, (S) = 2.5*106 km2, (O) = 50%, and (D) = 5 where σ is the standard deviation of geopotential height anomalies over [30-90 °N].
4. Results
We conducted a series of idealized experiments with AA forcing to test the linkage suggested by the FVL12 hypothesis. Again, FVL12 addressed that AA would cause the jet stream to slow down and thus result in amplified waves and increased blocks. First, we confirmed the response of the jet, such as the change of location and speed. Then, the wave and block responses are identified depending on the jet changes.
The prescribed zonally symmetric equilibrium temperature and time- averaged zonal mean zonal wind and temperature are shown in Fig. 1. We used a dry idealized dynamic core in experiments, and did not describe the stratosphere and other physical processes. Nevertheless, it generated quite a similar temperature profile to that of the real troposphere (Fig. 1b).
4.1. Response of jet
Figure 2 gives a forced heat profile, and the difference in temperature and zonal wind between AA and the control experiments. The surface temperatures at the poles are increased by approximately 5 K in reaction to the external heat forcing. The lower troposphere of the midlatitudes and high latitudes becomes warmer and shows a similar but wider structure compared to the externally forced heat profile. Though the heat is embedded in the polar region, the temperature response is not limited to the pole; it might be seen to
influence the lower-middle layer and the upper troposphere of the midlatitudes, and even the equator. Further study is required to understand how the increase in polar temperature affects the whole northern hemisphere.
The jet moves towards the equator compared to the control experiment as the temperature rises in the Arctic, and shows a northern annular mode (NAM) pattern (Fig. 2b). While the jet’s transition is not sensitive to the heating height, the total amount of forced heat could affect the jet strength. Even considering the stratosphere, the tropospheric jet moves equatorward in response to externally forced heat in the polar region (Wu and Smith, 2016).
In classical dynamics, this negative NAM pattern is understood by the change of convergence latitude in the momentum flux (Thompson et al., 2003).
The zonal-mean zonal momentum equation is expressed as below:
[ ]= [ ] − [ ∗ ∗]
−[ ]
Square brackets represent the zonal mean, asterisks represent deviation from the zonal mean, and τ is surface dissipation in this model. The tendency and meridional wind term could be omitted if we take the time average and vertically integrate it to this equation. Therefore, the change of zonal mean zonal wind is briefly proportional to the change of eddy momentum flux convergence. In summary, it could be expressed as follows:
〈[ ]〉 ∝ 〈− [ ∗ ∗]
〉
Angle brackets and overbars respectively represent the vertical integration and time average.
Figure 3 displays the change of eddy momentum flux convergence and its difference with control experiments for high, low, and mixed frequency eddies. Here, the Lanczos filtering is applied with cut-off for eight days. In Fig.
3a, the momentum flux convergence zones correspond with the jet maximum.
In addition, increases and decreases of the momentum flux converge with maximum value as the center matches up with the jet shifts. Therefore, the changes of high frequency eddies are responsible for the jet shifts. Meanwhile, low frequency eddies seem to contribute to making the jet both weaken and widen (Fig. 3b). While they contribute to jet weakening and shifting equatorward in the accelerated jet range, they cause the jet to strengthen and move poleward in the decelerated jet range. In contrast to the high and low frequency eddies, mixed frequency eddies do not affect the zonal winds (Fig.
3c).
We carried out the transient experiment to determine how fast the circulation reacts to the AA. Figure 4 shows the response of the jet to forced heat in the polar regions as a function of time. The jet reacts instantly as the heat increases, but it responds unstably until day 30. It first moves
equatorward over 10 days, then it moves slightly poleward until day 20, and then it moves equatorward again. After day 30, it seems to be stably shifted towards the equator, instantly revealing that the atmospheric circulations are modulated by AA.
4.2. Response of waviness
As shown in Section 4.1, the jet moves equatorward in response to arctic warming. The equatorward shift of jet weakens its northern flank. Here, we examine the waviness index to determine whether the weakened jet actually gets actually wavy.
The time-averaged zonal mean waviness index and zonal wind at 500 hPa are shown in Fig 5. The jet weakens in high latitudes and the jet becomes slightly stronger in the midlatitudes, as mentioned in Section 4.1. Waviness shows two maxima: One in the midlatitudes and one in the high latitudes. In comparison with the change of jet, waviness does not increase in the midlatitudes where jet gets stronger; the waviness actually increases for weaker jets at high latitude. Therefore, it could be concluded that waves get wavier as the jet weakens at high latitudes.
4.3. Response of blockings
Atmospheric block is a type of low frequency wave that is an
abnormal quasi-stationary system at the midlatitudes. It obstructs weather systems and can stay in place for several days. It eventually induces severe weather events such as cold surges, heat waves, and precipitation. Here, we examined the response of atmospheric blocks, which cause extreme weather events, to AA.
First, we evaluated whether atmospheric blocks would occur more frequently. Figure 6 shows the time-averaged zonal mean blocked area per a day as a function of the latitude and zonal wind at 500 hPa, and their differences from the control experiment. The key result is that the blocked area increased with a stronger jet in AA experiments compared to control experiments. In addition, there is no hint that the blocks occur more frequently with a weakened jet. Furthermore, it is shown that the total number of blocks is increased by approximately 10% with AA (Table 1).
The duration of blocks is as important as the frequency. If the blocks are maintained for longer periods, extreme weather events could continue for several days, thus badly damaging human society and ecosystems. Therefore, we assessed the block duration variation in response to AA. Figure 7 displays the number of blocks according to the duration. Even though there seem to be nonlinear increases in duration and heat amount, an increase in the number of blocks is found for all durations.
There are several possible reasons why atmospheric blocks are
increased. Atmospheric blocks are caused by the resonance between low frequency waves, and they are generated upon receiving energy from a background flow, such as barotropic energy conversion, or from energy supplied by high frequency eddies.
We investigated the changes of eddy kinetic energy of total, high frequency, and low frequency eddies with control experiments (Fig. 8). In AA experiments, the total kinetic energy was increased in the midlatitudes and decreased in high latitudes in a manner similar to that of the jet. In addition, both the total eddy kinetic energy and the low and high frequency eddy kinetic energies showed similar variation. In the above section, we find out that the blocks, which are a type of low frequency variation, occur more frequently at the midlatitudes. Thus, increasing the low frequency eddy kinetic energy at the midlatitudes is considered due to the increased blocks at the midlatitudes.
High frequency eddy kinetic energy moves equatorward along with the jet in a manner that corresponds with the shift of the storm track. This means that as the jet moves equatorward with AA, the storm track also moves towards the equator. The storm track is one of the most important factors for blocking formation. On this basis, we conclude that high frequency eddies that move towards the equator cause more blocks.
5. Conclusions
The aim of this study is to understand the impact of AA on midlatitude circulation and extreme weather events. We tried to demonstrate the hypothesis proposed by FVL12, which suggested the slower jet and amplified waves that result from AA cause more frequent blocks in the midlatitudes and the eventual increase in extreme weather, by simulating AA-like warming in the spectral dynamic core.
We first focused on the changes in the jet. As seen in previous studies, the jet showed a negative Northern Annular Mode (NAM)–like pattern, meaning an equatorward shift. It was partly understood by the change in high- frequency eddies due to AA-like warming. The storm track moved equatorward along with the equatorward-shifted jet. While the wave amplitudes did not change with a stronger jet in the midlatitudes, they were amplified with a weaker jet in the high latitudes. However, in spite of the weaker jet and amplified waves in the high latitudes, the atmospheric blocks slightly decreased. Unlike in the high latitudes, they increased in the midlatitudes and were accompanied by a shift of storm tracks. This means that the increase in blocks that cause severe weather events was involved in complicated interactions between eddies and warming in the Arctic.
6. Further studies
We conducted a series of idealized model experiments with many assumptions in this study. First, we modeled the world without a stratosphere.
The stratosphere both reacts with and affects the troposphere. Moreover, there is a strong jet in the polar stratosphere, which induces a positive NAM pattern in the troposphere when it breaks. Therefore, it is important to investigate stratospheric responses and troposphere–stratosphere interactions in response to AA. In addition, we assumed an arbitrary heat profile and a flat surface. Therefore, sensitivity tests with regard to heat intensity, boundaries, profiles, and topography are needed.
We confirmed that the jet moved equatorward in response to AA by carrying out idealized model experiments. The eddies reacted to externally forced heat and affected the jet to shift in this process. It is desirable to determine how eddies modulate atmospheric circulation.
Furthermore, we verified that the atmospheric conditions would occur more frequently with AA in the midlatitudes. We hypothesized that increasing the blocks might be related to the changes of high frequency eddies, that is to say the shifting of the storm track. Related dynamics were investigated to confirm the hypothesis.
References
Barnes, E. A., 2013: Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes. Geophysical Research Letters, 40, 4734-4739.
Barnes, E. A., E. Dunn-Sigouin, G. Masato, and T. Woollings, 2014: Exploring recent trends in Northern Hemisphere blocking. Geophysical Research Letters, 41, 638-644.
Barnes, E. A. and L. M. Polvani, 2015: CMIP5 projections of Arctic amplification, of the North American/North Atlantic circulation, and of their relationship. Journal of Climate, 28, 5254-5271.
Cohen, J., J. A. Screen, J.C. Furtado, M. Barlow, D. Whittleston, D. Coumou, and J.
Jones, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nature Geoscience, 7, 627-637.
Chen, G. and P. Zurita-Gotor, 2008: The tropospheric jet response to prescribed zonal forcing in an idealized atmospheric model. Journal of the Atmospheric Sciences, 65, 2254-2271.
Chen, G., J. Lu, D. A. Burrows, and L. R. Leung, 2015: Local finite-amplitude wave activity as an objective diagnostic of midlatitude extreme weather, Geophysical Research Letters, 42, 952–10,960
Deser, C., L. Sun, R. A. Tomas, J. Screen, 2016: Does ocean-coupling matter for the northern extra-tropical response to projected Arctic sea ice loss?
Geophysical Research Letters.
Dole, R. M. and N. D. Gordon, 1983: Persistent anomalies of the extratropical Northern Hemisphere wintertime circulation: Geographical distribution and regional persistence characteristics. Monthly Weather Review, 111, 1567-1586.
Dunn-Sigouin, E. and S. W. Son, 2013: Northern Hemisphere blocking frequency and duration in the CMIP5 models. Journal of Geophysical Research: Atmospheres, 118, 1179-1188.
Francis, J. A. and S. J. Vavrus, 2012: Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophysical Research Letters, 39.
Francis, J. A. and S. J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic warming. Environmental Research Letters, 10, 014005.ers, 39.
Hassanzadeh, P., Z. Kuang, B. F. Farrell, 2014: Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM. Geophysical Research Letters, 41, 5223-5232.
Held, I. M. and M. J. Suarez, 1994: A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. Bulletin of the american Meteorological society, 75, 1825-1830.
Huang, C. S. and N. Nakamura, 2016: Local finite-amplitude wave activity as a
diagnostic of anomalous weather events. Journal of the Atmospheric Sciences, 73, 211-229.
Kinnard, C., C. M. Zdanowicz, D. A. Fisher, E. Isaksson, A. de Vernal, L. G.
Thompson, 2011: Reconstructed changes in Arctic sea ice over the past 1,450 years. Nature, 479, 509-512.
Lesins, G., T. J. Duck, and J. R. Drummond, 2012: Surface energy balance framework for Arctic amplification of climate change. Journal of Climate, 25, 8277-8288.
Liu, J., J. A. Curry, H. Wang, M. Song, R. M. Horton, 2012: Impact of declining Arctic sea ice on winter snowfall. Proceedings of the National Academy of Sciences, 109, 4074-4079.
Nakamura, N. and A. Solomon, 2010: Finite-amplitude wave activity and mean flow adjustments in the atmospheric general circulation. Part I:
Quasigeostrophic theory and analysis. Journal of the Atmospheric Sciences, 67, 3967-3983.
Overland, J., J. A. Francis, R. Hall, E. Hanna, S. J. Kim, and T. Vihma, 2015: The Melting Arctic and Midlatitude Weather Patterns: Are They Connected? Journal of Climate, 28, 7917-7932.
Park, H. S., S. Lee, S. W. Son, S. B. Feldstein, and Y. Kosaka, 2015: The Impact of Poleward Moisture and Sensible Heat Flux on Arctic Winter Sea Ice Variability. Journal of Climate, 28, 5030-5040.
Rinke, A., K. Dethloff, W. Dorn, D. Handorf, and J. C. Moore, 2013: Simulated Arctic atmospheric feedbacks associated with late summer sea ice anomalies. Journal of Geophysical Research: Atmospheres, 118, 7698-7714.
Screen, J. A., 2014: Arctic amplification decreases temperature variance in northern mid-to high-latitudes. Nature Climate Change, 4, 577-582.
Screen, J. A. and I. Simmonds, 2010: The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464, 1334-1337.
Screen, J. A., I. Simmonds, C. Deser, and R. Tomas, 2013: The atmospheric response to three decades of observed Arctic sea ice loss. Journal of Climate, 26, 1230-1248.
Serreze, M. C. and J. A. Francis, 2006: The Arctic amplification debate.Climatic change, 76, 241-264.
Thompson, D. W., S. Lee, M. P. Baldwin, 2003: Atmospheric processes governing the northern hemisphere annular mode/North Atlantic oscillation. The North Atlantic Oscillation: climatic significance and environmental impact, 81-112.
Tibaldi, S. and F. Molteni, 1990: On the operational predictability of blocking.Tellus A, 42, 343-365.
Woods, C. and R. Caballero, 2016: The role of moist intrusions in winter Arctic warming and sea ice decline. Journal of Climate
Wu, Y. and K. L. Smith, 2016: Response of Northern Hemisphere Midlatitude Circulation to Arctic Amplification in a Simple Atmospheric General Circulation Model. Journal of Climate, 29, 2041-2058.
Table 1The number of detected blocks.
CTRL AA-Shallow AA-Medium AA-Deep
442 444 471 471
Figure 1 (a) Prescribed zonal mean equilibrium temperature, and (b) model responses of the mean zonal wind (green) and temperature (black) for control simulations without heating.
Figure 2 (a) Zonally symmetric forced heat profile, (b) time-averaged zonal mean temperature (contour; 10 K intervals), and (c) time-averaged zonal mean zonal wind (contour; 10 m s-1interval). The differences from the control experiments are displayed by shading and colored contours in (b) and (c), respectively.
Figure 3 Eddy momentum convergence (black) and its difference (colored) from control simulations for (a) high, (b) low, and (c) mixed frequency eddies.
They respectively describe deep, medium, and shallow AA from top to bottom.
The contour intervals are respectively (a) 2×10-5m s-2and 3×10-6m s-2, (b, c) 2×10-5m s-2and 2×10-6m s-2for the black and colored contours.
Figure 4 Zonal difference of the mean zonal wind at 300 hPa between the transient and control experiments. The contour interval is 0.2 m s-1.
Figure 5(a, c) Time-averaged zonal mean waviness index and zonal wind at 500 hPa (b, d) and their differences with control simulation. Black, blue, green, and red lines respectively indicate control, AA-shallow, AA-medium, and AA-deep experiments.
Figure 6The same as in Fig. 5, but for blocked areas per a day as a function of latitude.
Figure 7(a) The number of blocks by duration and (b, d) the differences with control simulation. Black, blue, green, and red bars respectively indicate control, AA-shallow, AA-medium, and AA-deep experiments.
Figure 8Time-averaged zonal mean eddy kinetic energy (contour; interval of 50 m2s-2) for (a) total, (b) high frequency, and (c) high frequency eddies. From top to bottom, each row displays the eddy kinetic energy of CTRL, AA-Deep, AA-Medium, and AA-Shallow, respectively. Shading shows the difference from the control simulation.
초록
역학 모델 실험에서 북극 온난화가 중위도 저지고기압에 미치는 영향
유 영 은 지구환경과학부 석사과정 서울대학교
지구 온난화와 더불어 북극에서는 다른 지역에 비해 2배 이상 빠르 게 온난화가 진행되고 있다. 이러한 급격한 북극의 기온 증가가 극 및 중위도의 대기 순환장에 미치는 영향에 대하여 활발히 연구가 진행되고 있다. 특히, Francis and Vavrus (2012)와 Liu et al. (2012)는 북극 온 난화로 인해 제트가 느려지며 사행하게 되며 이로 인해 중위도에 저지고 기압과 같은 이상기후현상이 증가할 것이라고 가설을 제시하였다. 최근 들어 빈번해진 이상기후현상으로 인해 이 가설에 대한 찬반논란이 계속 되고 있다.
본 연구에서는 역학 모델을 위의 가설을 검증하였다. 역학 모델을 이용하여 극 지역에 강제적으로 열을 가해주고, 제트의 변화와 사행, 이 에 따른 저지고기압의 추세를 분석하였다. 극에 강제로 가해준 열은 중 위도 에디를 변화시켰으며, 이로 인해 제트가 적도 쪽으로 이동하였다.
또한 제트의 이동과 더불어 저기압 경로도 같이 적도 쪽으로 이동함을
확인하였다. 그리고 제트가 강해진 중위도 지역에서는 제트가 사행하는 것을 확인할 수 없었으나, 제트가 약해진 고위도 지역에서는 제트가 북 극 온난화로 인해 사행하는 것을 확인하였다. 그러나 제트가 약해지고 사행하는 고위도 지역에서는 저지고기압이 증가하지 않았으나 오히려 제 트가 강해지고 사행하지 않았던 중위도 지역에서 저지고기압이 증가하였 다. 이는 저기압 경로가 적도 쪽으로 이동하게 되며 중위도에서 저지고 기압이 증가한 것으로 분석되었다.
주요어: 북극 온난화, 제트, 저지고기압 학 번: 2015-20468
