jma_logo Welcome to TCC wmo_logo
Home
World Climate
Climate System Monitoring
El Niño Monitoring
NWP Model Prediction
Global Warming
Climate in Japan
Training Module
Press release
Links
HOME > Climate System Monitoring > Explanation

Explanation on the Products for Climate System Monitoring

   For most of the products in the "Climate System Monitoring" webpage, the base period for the normal is 1981 - 2010, unless otherwise noted. Anomalies are deviations from the normal. Standard deviations are calculated for the time period of the normal.

Extratropical Circulation

   Atmospheric circulation data are based on the results of six-hourly global objective analysis conducted at 00, 06, 12 and 18 UTC of the Japanese 55-year Reanalysis (JRA-55) (Kobayashi et al. 2015).
   Wave activity flux (Takaya and Nakamura 2001) indicates the propagation of Rossby wave packets.
   Kinetic energy per unit mass of high frequency variation at 500 hPa indicates the activity of synoptic scale disturbances. It is defined as half the sum of squares of zonal and meridional components of 2-8 day band pass filtered (Duchon 1979) wind speed.
   Zonal averaged northward flux of eastward momentum in the upper troposphere and zonal averaged northward heat flux at 850 hPa are calculated after Newell et al. (1972).
   The empirical orthogonal function (EOF) analysis is conducted with covariance matrix of latitude weighted seasonal mean 500 hPa height north of 30°N from 1958 to 2012. The eigen-vectors of the first and second modes of EOF analysis indicate the primarily and secondary dominant spatial patterns of 500 hPa height anomalies, respectively. Three month and five day mean EOF scores are calculated by projecting the corresponding 500 hPa height anomalies onto the eigen-vectors. The amplitudes of the eigen-vectors are normalized and represent the values when the variance of the seasonal mean score for the normal period is one.



Tropical Circulation and Convection

   The data for monitoring tropical circulations are also the JRA-55 data.
   Convective activity is inferred from outgoing longwave radiation (OLR). It can be assumed that lower values of OLR indicate more enhanced convective activity, except for the middle latitudes in winter season and for high-altitude areas. The original OLR data are derived from the observations by the National Oceanic and Atmospheric Administration's (NOAA) polar orbital satellites, and provided by the Climate Prediction Center (CPC)/NOAA and the Earth System Research Laboratory (ESRL)/NOAA. The OLR data from CPC are used as preliminary data, and the interpolated OLR data from ESRL are used as final data.
   OLR maps represent the strength of longwave radiation from the earth's surface under clear sky conditions into space or from the top of clouds under cloudy conditions into space for monitoring of convective activity. It can be assumed that lower values of OLR indicate more enhanced convective activity under cloudy conditions. It should be noted that lower values of OLR can represent lower surface temperatures, but not the top of clouds, in the middle latitudes in winter season and in high-altitude areas (e.g., the Tibetan Plateau).
   Stream function is defined as follows:

      uψ = -δψ/δy,    vψ = δψ/δx (ψ:stream function, uψ, vψ:rotational components of winds )

and its anomaly shows the intensity of anticyclonic or cyclonic circulation compared to the normal.
   Velocity potential, indicating large-scale divergence or convergence, is defined as follows:

      divVχ=∇2χ ( χ:velocity potential, Vχ:divergent wind )



Tropical Atmospheric and Oceanic Monitoring Indices

   Monthly mean sea level pressure anomalies for Darwin, Australia and Tahiti in the southern Pacific are based on CLIMAT reports. The Southern Oscillation Index (SOI) is defined as the normalized difference between Tahiti and Darwin of monthly mean sea level pressure anomalies normalized by their standard deviations.
   Equatorial zonal wind indices are defined as area-averaged zonal wind anomalies normalized by their standard deviations.
   OLR indices are defined as area-averaged OLR anomalies with reverse sign, normalized by their standard deviations. It should be noted that positive and negative values of the OLR indices indicate enhanced and suppressed convective activity, respectively, compared to the normal.
   Asian summer monsoon OLR indices (SAMOI) are derived from OLR anomalies from May to October. SAMOI (A), (N) and (W) indicate the overall activity of the Asian summer monsoon, its northward shift and westward shift, respectively. The SAMOIs definitions are as follows:

   SAMOI(A)=W+E, SAMOI(N)=N-S, SAMOI(W)=W-E

W, E, N and S indicate area-averaged OLR anomalies for the respective areas shown in Figure 1, normalized by their standard deviations.
   The El Niño monitoring indices are regional mean value of 1° x 1° SST analyses for the four El Niño monitoring regions.
   The data of pressure-corrected sea level anomaly are provided by Sea Level Center, University of Hawaii. The base period for the normal is 1975 - 1995.


Fig. The areas of Summer Asian Monsoon OLR Indices (SAMOI)
Fig. The areas of Summer Asian Monsoon OLR Indices (SAMOI)


Oceanographic Conditions

   Sea surface temperatures (SSTs) are based on COBE-SST (JMA 2006) datasets in 1° × 1° grid boxes. The SST data are smoothed in the latitudinal and longitudinal directions using a simple filter. Ocean heat content (OHC) is determined using five day mean datasets of MOVE/MRI.COM-G (Usui et al. 2006) and MOVE/MRI.COM-G2.
   Time series of SST, subsurface temperature, OHC and so on along the equator, 6°N and 6°S for the last three or five years are shown in order to monitor the tropical oceanographic conditions related to El Niño/La Niña events.
   The depth of 20°C isotherm along equator is roughly equivalent to the depth of thermocline there. The surface zonal wind stresses is used to drive the ocean general circulation model of the MOVE-G and the MOVE-G2. The value of OHC is equivalent to averaged temperature from the surface to the depth of 300 m.



Snow Cover

   The number of days of snow cover is determined based on observations by the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) on board the Defense Meteorological Satellite Program (DMSP) polar-orbiting satellites. The algorithm for the analysis was developed by JMA (JMA 2011).
   The number of days of snow cover is also analyzed using CLIMAT and SYNOP reports.



References

  • Duchon, C. E., 1979: Lanczos Filtering in One and Two Dimensions, J. Applied Met., 18, 1016-1022.
  • JMA, 2006: Characteristics of Global Sea Surface Temperature Data (COBE-SST), Monthly Report on Climate System, Separated Volume No. 12.
  • JMA, 2011: Data Report on Climate System 2010.
  • Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA-55 Reanalysis: General Specifications and Basic Characteristics. J. Meteorol. Soc. Japan, 93, 5-48.
  • Newell, R. E., J. W. Kidson, D.G. Vincent, and G. J. Boer, 1972: The General Circulation of the Tropical Atmosphere and Integrations with Extratropical Latitudes, Vol.1, The MIT Press.
  • Takaya, K., and H. Nakamura, 2001: A Formulation of a Phase-Independent Wave-Activity Flux for Stationary and Migratory Quasigeostrophic Eddies on a Zonally Varying Basic Flow, J.Atom.Sci., 58, 608-627.
  • Usui, N., S. Ishizaki, Y. Fujii, H. Tsujino, T. Yasuda and M. Kamachi, 2006: Meteorological Research Institute multivariate ocean variational estimation (MOVE) system. Advances in Space Research, 37, 806-822.