• Title/Summary/Keyword: monthly mean temperature of northern hemisphere

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The Spatial Distribution and Change of Frequency of the Yellow Sand Days in Korea (한국의 황사 발생 빈도 분포와 변화 분석)

  • Kim, Sunyoung;Lee, Seungho
    • Journal of Environmental Impact Assessment
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    • v.15 no.3
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    • pp.207-215
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    • 2006
  • The purpose of this paper is to analyze the spatial distribution and change of the frequency of Yellow Sand days and to examine their relationship with atmospheric circular characteristics at the surroundings of the Korean peninsula. Yellow Sand days data are used by intensity, Siberian High Index and monthly mean temperature of the Northern Hemisphere. In the Middle-western region, the occurrence frequency of Yellow Sand days was higher during the study period (1973-2004). Also, the occurrence frequency of Yellow Sand days increased to latter half 16 years compared with the first half 16 years, and be clearer in Middlewest regions. Yellow Sand days frequency increased, and the trend was distinct in the Jungbu region during the study period. Increasing trend of Yellow Sand days frequency was significant for the recent 22 years. Yellow Sand days had a negative relationship with Siberian High Index in February and March. Therefore, Siberian High Index became weaker in the spring, and possibility for the occurrence of Yellow Sand days was generating larger. Yellow Sand days had a positive relationship in monthly mean temperature of the Northern Hemisphere. Especially, the case of the strong Yellow Sand days is significant. Recently, global warming might be affecting the occurrence of strong Yellow Sand days.

Wetness or Warmth, Which is the Dominant Factor for Vegetation?

  • Suzuki, Rikie;Xu, Jianqing;Motoya, Ken
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.147-149
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    • 2003
  • The wetness, a function of precipitation and temperature etc, and the warmth, a function of temperature, are the dominant factor for global vegetation distribution. This paper employs the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index (WEI), and focuses on an essential climate-vegetation relationship at global scale. The NDVI was acquired from ‘Twenty-year global 4-minute AVHRR NDVI dataset.’ The WEI is defined as the fraction of the precipitation to the potential evaporation. The WAI was calculated by accumulating the monthly mean temperature of the portion exceeded 5$^{\circ}C$ throughout the year. Meteorological data for the WEI and WAI calculation were obtained from the ISLSCP CD-ROM. All analyses were conducted for 1 ${\times}$ 1 degree grid box on the terrestrial area of the Earth, and on annual value basis averaged in 1987 and 1988. The result of analyses demonstrated that there are two regimes in their relations, that is, a regime in which NDVIs vary depending on the WEI, and a regime in which NDVIs vary depending on the WAI. These two regimes appeared to correspond to the wetness dominant and warmth dominant vegetation, respectively. The geographical distributions of two regimes were mapped. Most of the world vegetation is categorized into wetness dominant, while warmth dominant vegetation is seen in the high-latitude area mainly to the north of 60$^{\circ}$N in the Northern Hemisphere and high-altitude areas.

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Temporal and Spatial Variability of the Middle and Lower Tropospheric Temperatures from MSU and ECMWF (MSU와 ECMWF에서 유도된 중간 및 하부 대류권 온도의 시 ${\cdot}$ 공간 변동)

  • Yoo, Jung-Moon;Lee, Eun-Joo
    • Journal of the Korean earth science society
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    • v.21 no.5
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    • pp.503-524
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    • 2000
  • Intercomparisons between four kinds of data have been done to estimate the accuracy of satellite observations and model reanalysis for middle and lower tropospheric thermal state over regional oceans. The data include the Microwave Sounding Units (MSU) Channel 2 (Ch2) brightness temperatures of NOAA satellites and the vertically weighted corresponding temperature of ECMWF GCM (1980-93). The satellite data for midtropospheric temperatures are MSU2 (1980-98) in nadir direction and SC2 (1980-97) in multiple scans, and for lower tropospheric temperature SC2R (1980-97). MSU2 was derived in this study while SC2 and SC2R were described in Spencer and Christy (1992a, 1992b). Temporal correlations between the above data were high (r${\ge}$0.90) in the middle and high latitudes, but low(r${\sim}$0.65) over the low latitude and more convective regions. Their values with SC2R which included the noises due to hydrometeors and surface emission were conspicuously low. The reanalysis shows higher correlation with SC2 than with MSU2 partially because of the hydrometeors screening. SC2R in monthly climatological anomalies was more sensitive to surface thermal condition in northern hemisphere than MSU2 or SC2. The first EOF mode for the monthly mean data of MSU and ECMWF shows annual cycle over most regions except the tropics. The mode in MSU2 over the Pacific suggests the east-west dipole due to the Walker circulation, but this tendency is not clear in other data. In the first and second modes for the Ch2 anomalies over most regions, the MSU and ECMWF data commonly indicate interannual variability due to El Ni${\tilde{n}$o and La Ni${\tilde{n}$a. The substantial disagreement between observations and model reanalysis occurs over the equatorial upwelling region of the western Pacific, suggesting uncertainties in the model parameterization of atmosphere-ocean interaction.

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