Takahashi, H. G., S. A. Adachi, T. Sato, M. Hara, X. Ma, and F. Kimura, 2015: An Oceanic Impact of the Kuroshio on Surface Air Temperature on the Pacific Coast of Japan in Summer: Regional H2O Greenhouse Gas Effect, Journal of Climate, Vol. 28, No.18, September 2015: 7128-7144.[Web Page]

Fig. Regression coefficient of the simulated precipitable water (mm) and vertically integrated water vapor fluxes (kgm-1 s-1) in August on the normalized SST over REF Kuroshio during the 31-yr period from 1982 to 2012. All plotted vectors are statistically significant at the 99.9% level, as determined by correlation coefficients based on 29 degrees of freedom.


Takahashi H.G., H. Fujinami, T. Yasunari, J. Matsumoto, and S. Baimoung, 2014: Role of tropical cyclones along the monsoon trough in the 2011 Thai flood and interannual variability, Journal of Climate, November 2014. [Web Page]

Fig. (a) Precipitation time series generated from theCMAP dataset for the rainy season (May–September) over the reference region of Indochina (12.58–208N, 97.58–107.58E) from 1979–2011. The reference region is used for the regression analysis in (b),(c) and is indicated by a solid rectangle in these panels. (b)Regression ofCMAPdata during the rainy season against the normalized data (mmday21) shown in (a) from 1979 to 2011. (c) As in (b), but for the 850-hPa zonal and meridional winds and streamfunction (colors) during the rainy season. Areas with colors in (b) and plotted vectors (winds;ms21) and contours and colors (streamfunction; 106m2 s21) in(c) are statistically significant at the 90% level, as determined by correlation coefficients based on 31 degrees of freedom (df).


Yamaji M. and H.G. Takahashi, 2014: Asymmetrical interannual variation in aerosol optical depth over the tropics in terms of aerosol-cloud interaction, SOLA (Scientific Online Letters on the Atmosphere), October 2014, doi:10.2151/sola.2014-039.[Web Page]

Left Fig. Composite anomalies in aerosol optical depth in (a) SON of the El Niño years, (b) SON of the La Niña years, (c) DJF of the El Niño years, and (d) DJF of the La Niña years (95% confidence limit as determined by Student’s t-test). Gray portions indicate missing values.

Right Fig. Scatterplot between three-month mean precipitation (unit is mm day−1) and AOD (from Terra and Aqua) over the Maritime Continent (105°E−140°E, 10°S−5°N) from 2000 to 2012 in (a) SON and (b) DJF. Red, blue rhombus, and asterisk symbols are values for dry (El Niño), wet (La Niña), and neutral years respectively. Lines in (a) are least-squares regression fits to data points using values from the El Niño and La Niña years together (dotted line) and separately (solid lines).

日本海の海面水温(SST)が日本の降水量に及ぼす影響 -1KのSST上昇で7%より大きい降水量増加?-

Takahashi, H.G., N. N. Ishizaki, H. Kawase, M. Hara, T. Yoshikane, X. Ma, and F. Kimura 2013: Potential impact of sea surface temperature on winter precipitation over the Japan Sea side of Japan: A regional climate modeling study. Journal of the Meteorological Society of Japan (JMSJ), April 2013.[Web Page]

Fig. Time series of simulatedprecipitation over the reference region 1 (137-140°E, 36.5-38.5°N; shown in Fig. 1), except for the ocean. Black, pink, red, and light-blue lines indicate CTL, SST+1K, SST+2K, and SST−1K, respectively. The precipitation was accumulatedfrom 00 UTC 1 January 2006. The unit is millimeters.


Takahashi, H.G., T. Yoshikane, M. Hara, K. Takata, and T. Yasunari 2010: High-resolution modelling of the potential impact of land-surface conditions on regional climate over Indochina associated with the diurnal precipitation cycle, International Journal of Climatology, 30(13), 2004-2020, Janurary 2010, doi:10.1002/joc.2119.[PDF]

Fig. Total amount of monthly precipitation of (a) WET and (b) DRY. Differences in monthly precipitation (c) between DRY and CTL (CTL–WET) and (d) between CTL and DRY (DRY–CTL) are shown. The numbers of pentads out of 18 that calculate increase in pentad precipitation (e) between DRY and CTL (CTL–WET) and (f) between CTL and DRY (DRY–CTL) are shown. The calculation period of each experiment is three months, which is 18 pentads (90 days). The numbers of pentads that show increase in precipitation were counted at each half-degree grid. White and black lines indicate the disturbed region.



人間活動が地球の気候に及ぼす影響の一つとして、地表面改変がある。具体的には、森林伐採、耕作地化、都市化などがある。改変域とその周辺の気温や降水量など、気候への影響を調べている。人間活動により地表面が乾燥化すると、地表面が受け取ったエネルギーの配分が変わり、大気が乾燥化・高温化する。それにより気温が変化するだけでなく、蒸発量の減少による水蒸気量の変化などを通して、降水量にも影響を及ぼす。森林伐採により、伐採域で降水量が減少するのが一般的に知られているが、条件によっては増加する可能性も十分にある。また、地表面の色が変わることで、 地表面が受け取るエネルギーが変化することや、地表面の凹凸の変化などにより、大気の流れが変わることも重要である。


(降水気候学、水循環、TRMM, GPM)



Precipitation Annual Climatology (TRMM-PR) 1998-2012 [mm/day]







(TRMM, GPM, 水蒸気、高解像度気候モデル)




水蒸気は、温室効果ガスの一つであり地球の気候を決める重要な要素である。その一方で、雲や雨の源となることによっても地球の気候の形成に寄与している。この水蒸気量は、気候変動を調べるための観測データが限られている。共同研究などを通して、水蒸気変動を解析できるデータのアー カイブ、解析などを行っている。 また、水蒸気輸送は、地球の大規模なエネルギー輸送システムの一つであり、地球の気候形成に果たす役割は大きい。