El Nino

 Climate Change





Climate Change

 Climate Change

     Climate change is that climate systems gradually change naturally or by human activities. Since 1980s, global warming and unusual change in the weather are prominent. Now earth is undergoing climate change to diverge from the past's. We are proceeding many studies from global scale climate to local change in Korea.

1. Basic Information

2. Our Interestings and reserch

    1) Global warming trend

    1. Global Mean Surface Temperature Rising

     According to observation data, it is clear that global mean surface temperature has been risen by 0.3∼0.6℃ since 19C (Jones,1988 ; Hansen and Lebedeff,1987,1988 ; Vinnikov et al.,1990 ; Jones and Briffa,1992 ; Parker et al., 1994). Moreover it is suggested by proxy data(Cook,1995), atmospheric- ocean coupled model(Stouffer et al., 1994), energy balance model (Wigley and Raper, 1990) that this temperature rising is beyond the standard variability.

    Hansen and Lebedeff(1987) showed that global mean temperature is rising abruptly in 1940s and 1980s. They said that the rising at early 20C is limited in middle and high latitude, but lately it extends over global scale. Cook(1995) also found that any other trend obtained from proxy data(for example, annual ring) for 1,000 years is not more than a half global mean temperature rising for 20C.

    Wigley and Raper(1990) analyzed EBM(Energy Balance Model) data which period is over 100,000 years, they also cannot find the trend which is similar to resent temperature rising. As above, from the results obtained through many other possible methods, we can know that temperature rising in 20C is not just one of the natural variations.

    2. The schematic diagram of obtaining coupled mode

     The upper panel is the schematic diagram which Kim, Maeng-Ki and In-Sik Kang(1996) suggested to grasp relationships between atmospheric circulation pattern and local climate, between atmospheric circulation pattern and global climate change. This methods is basically to obtain coupled modes between global atmospheric circulation and local temperature(or precipitation), consist of EOF and SVD analysis. The method of application is, first, that obtain the eigen mode and eigen vector by each EOF analysis of global and local climate variable. Next, the coupled mode of each eigen mode is obtained by SVD. Then we can get coupled temporal variation and eigen vector as each modal pair. And from covariance between each modal temporal variation and the original time series we can find the correlation pattern. So this method shows the correlative time variation, the amplitude and the region that has the signal related to that temporal variation.  Also this method is useful to separate from the temporal and spatial variations have diverse periods because it's distribution is divided as each mode. The equation of global and local variable is consist of the eigen vector from EOF analysis, the eigen vector from SVD, the temporal variation of coupled mode and the correlation pattern. To know the relationship between the two variable by SVD, the reason not to use the original time series, but to use each eigen mode of EOF is that the input variable of coupled mode has  independent condition each other, only less variables, but contained most variation of original time series. EOF also can filter out the noise that can not explain dynamically from the original time series.

    3. Coupled modes of temperature and sea level pressure

      The upper picture showed the correlation pattern of the first coupled mode between (a)annual mean sea level pressure and (b)global temperature, (c)temporal variability.  Solid line is SLP's variability, dashed line is global temperature's. This two temporal variabilities' correlation coefficient is 0.97 and explain about 18.5% of the total variation. The SLP correlation pattern related with the trend has negative magnitude in high latitude, positive in middle latitude. The region has strong positive signal is in and around Tibet and Iberia peninsula and the region has strong negative signal is around the poles. Also Aleutian low and high region of northern Pacific has negative signal. Specially there is strong positive signal in the high latitude of Asian Continent. About the temperature decrease in Aleutian low, Trenberth (1990) explained it the advective effect by SLP decrease. But Trenberth said that this intensify of Aleutian low come out mainly from 1977 to 1986.  As the time series of Figure (c), this variation is the trend to be progressed for 40 years. Hence the intensify of Aleutian low is the atmospheric circulation change for recent 40 years global warming. In spite of uncertain cause, it is the temperature difference between continent and ocean that play an important role in the growth of Aleutian low. (Yamazaki, 1989)  So if the temperature increase in continent by global warming is more than that in ocean, then the temperature difference between continent and ocean will be larger, in result Aleutian low will more intensify.

     The upper figure showed the relationship of the first coupled modes (a)sea level pressure, (b)temperature in Korea, and (c)time series. In figure (c), the solid line is sea level pressure's time series and the dashed line is temperature in Korea's. The first coupled mode between SLP of northern hemisphere and tem- perature in Korea is the trend mode containing interdecadal variation and explains about 30% of the total variability. The correlation coefficient of the two time series is very high, 0.96. SLP decrease in high latitude, increase in middle latitude.  Exceptionally, SLP of Aleutian low region in Northern Pacific decrese. These pattern is similar to previous SLP's pattern related global temperature. This means the atmospheric circulation change with global warming is closely related with temperature rising in Korea. In other words, warming in Korea acompanies with global warming. Such a temperature change pattern related with atmospheric circulation change is positive in whole region, but the east side of Korea has larger magnitude than the west. That is, the east side of Korea has stronger signal than the west side. Therfore globla warming pattern-global atmospheric circulation pattern-warming pattern in Korea are all highly related each other, and these pattern induced by the increase of green house gases.

    4. The linearity between rising trend of temperauture and Eigenmode

     Through this study, we can know that the signal of global warming and warming in Korea are connected with the similar atmospheric circulation pattern. Here, For convenience, we will call it the local scale mode ( R mode) the warming pattern in Korea that is obtained by the coupled mode between the NH SLP and temperature in Korea.  If the warming in Korea is accompanied by global warming, the warming in Korea that is obtained from global warming (G mode) and the warming in Korea that is obtained from R mode must correspond. The x axis and the y axis is the linear trend of global temperature in each grid point and the correlation pattern to be obtained by coupled mode, respectively.  Figure (a) is the distribution of global warming trend, figure (b) is the distribution of the warming pattern in Korea. Hence each point in figure indicates the grid point of global temperature and the station point of Korea. Interestingly, global warming pattern and the warming in Korea are correspond with the correlation pattern and the linearity trend, respectively. The reason of this correspondence is that the coupled mode is closely related to the trend. In the case of global temperature, the increase of global mean temperature for the recent 40 years is 0.30℃ and the region of positive or negative trend spread out broadly. To the contrary in the case of Korea, All part has positive signal and the mean temperature increase is about 0.64℃. The warming trend in Korea to differ from global warming is 0.25℃/40 years. There is a remarkable contrast between this magnitude and the trend of (b). This difference induced by urbanization. The difference between many stations in Korea is large, too, 1∼1.2℃/40 years. But if the temperature increase by urbanization (0.36℃) is removed, the warming in Korea(0.28℃) is similar to global mean. And because the locality within Korea is removed, it will converge around the mean. Therfore the local difference of total temperature rising in Korea results in urbanization and the locality by green house is smaller than the locality by urbanization.

    2) Warming and climate variability in Korea

        1. Warming trend in Korea

      Variabilities of Temperature in Korea
      1. Mean temperature increases continuously since observation strated.
      2. the first eigen mode of temperature : warming trend
      3. EOF modes have been undergone change from around 1960s

      Estimations of warming trend in Korea after removing the urban effects

        2. Analysis of climate variability

      Estimation of the Warming Trend in Korea
      1. Eigen value analysis of temperture differences with Metropolitan area and town city
      2. Estimations of warming trend in Korea after removing the urban effects

    3) Atmospheric long-term variations in vertical structures
       of temperature

       The long-term variation from interannual to interdecadal is a matter of grave concern. Especially, the interannual variability related ENSO and trend of surface temperature is studied dynamically and physically, broadly and deeply. To the contrary, the study about from decadal to interdecadal variation is active just recently. Ghil and Vautard(1991), Kang(1996) showed there are 10 or 20 years period variations in atmospheric temperature. We, Climate Dynamics Laboratory, studied these long-term variations of atmosphere and ocean related to climate change. The Analysis of atmospheric long-term variations in vertical structure of temperature from troposphere to stratosphere follows.

       Figure 1 presents the global annual mean temperature of 10 levels. There is rising trend in troposphere, From 100hPa to 1000hPa, but in lower stratosphere, 70~30hPa, temperature dropped . In Upper atmosphere temperature again assume an upward curve. Especially Abrupt phase change in late 1970s is worth noticing. Figure 2 is the results of Multi-channel Singular Spectrum Analysis of vertical 17 level , each mode is actually the sum of a pair off eigen vector because in MSSA analysis the eigen mode is the pair as the same period.
       The first mode is the mode related to the trend, dominant in tropopause and lower stratosphere and the tren in each level is alike previously stated. The second mode is the mode which period is about 17~20 year, the phase of troposphere and stratosphere is in phase. Such a character also is found in the decadal variation. The difference of 10 year oscillation and 20 year oscillation is the center of variation. In the 20 year oscillation the center is located in 100∼70hPa surface, 10hPa surface. But in the 10 year oscillation the center is located in 100hPa surface and 30hPa surface. The noticeable finding is that the trend and 10, 20 year oscillation, all, change phase from negative to positive in late 1970s. The abrupt phase change in late 1970s, as previous stated, is the composite of the oscillations has diverse period.

       The left figure, figure 3, is the result to calculate the linear trend of the meridional- vertical structure of zonal mean temperature difference. In troposphere, the temperature rising is remarkable in the lower tropospher, middle latitude of SH and around low latitude tropopause. In stratosphere the center of temperature sinking trend is located in the lower stratosphere. Figure 3b) represent the ratio of the linear trend RMS to total RMS. Through this figure, we can explain the linearity is the most part in the total variation because the region that dominant linear trend in figure 3a) is more than 40%. The right figure 4 is the result that first applicate band pass filter by 8~35 year period and next accomplish EOF analysis. Figure a) is the first eigen vector, b) is the time series related to a).

       Figure c), d) is the same except to the second eigen vector. The first eigen vector is about 20 year oscillation and troposphere and stratosphere is in pahse, the center of the variation is located in around 100hPa. The second eigen vector is about 10 year oscillation and the center of the variation is located in 30~20hPa surface.

       Figure 5 is the result that first filter by the short period below 8 years and accomplish EOF analysis. Figure a), b) is ENSO mode, figure c), d) is QBO mode. The character of ENSO mode is, like Lau et al.(1998) stated, troposphere and stratosphere is out of phase in low latitude.

    4) Scenario of warming in Korea

        1. Prediction using the last 75 years warming trend

      - Total amount of temperature increasing : around 1.1℃
      - Urban effects : around 0.4℃ during the last 40 years
      - Warming in Korea : around 0.7℃ (F-test : 99.9% trend)
      - the recent 75 years correspond to 20-CO2 year substitude
        for 1% per a year
      - Prediction : 0.7℃ × 3.5℃ = 2.45℃

        2. Prediction using the results of analysis of observation data
          from surface and upper air.

      - Surface Temperature: increase aournd 0.8℃ per 20 years
      - Temperature of observation at 30hPa : decrease around -1.9℃
        per 20 years
      - Temperature of model results at 30hPa : decrease around -5.0℃
      - Apply rates of observed temperature changes of upper with lower   air to model temperature.
      - Prediction : (+0.8℃ over -1.9℃) × (-5.0℃) ≈ 2.1℃

        3. Prediction using Climate Models

      - GFDL GCM : temperature increas around 2.3℃
      - SNU GCM : temperature increas around 2.1℃

        4. Prediction using the results form climate models, and observations.

      - Prediction of warming using various predictors
      - Prediction : 0.7℃ × 3.5℃ = 2.45℃
      - IPCC reported that the aerosol effects mitigated increasing amount   of mean global
         → around 2.0℃ (aerosol mitigation effect about 15%)
      - Correction value of warming scenario in Korea
         → increase about 2.0℃±0.8℃

    5) Green House gases circulation modeling

        1. Construct a simplified Carbon-cycle model

        2. Reproduce the CO2 concentration of atmosphere in the past

      ● Limitations
      - Observed CO2 concenturation in the atmosphere
      - Changes of Carbon isotope concenturation (13C, 14C)
      - An amount of the emissions of CO2 by use of Fossil Fuel
      ● Estimations
      - An amount of the emission of CO2 by deforestation

        3. Understand the Carbon Cycle

        4. Assess national contribution to increase CO2 concentration in the past

        5. Summary and application

      - Construct the simplified global carbon cylce model
      - Understand carbon cycle dynamics
      - National contribution to increase CO2 in the past
      - Useful for the Prediction of the greenhouse gas changes
      - Foundations of the three dimension carbon cycle model
        → prediction system for the global warming

3. Our Research

4. Links