Introduction |

In predicting the state of global water systems to 2025, it is apparent that water resources are more vulnerable to rising water demands than greenhouse warming (Vorosmarty et al. 2000). Long lead-time predictions faciliates the best practical managemet of water resources by employing integrated operations and regulations.

To develop the optimal management methodology, we wanted to know how we can predict seasonal mean water resource variables potentially on a seasonal lead-time scale, where the variables targeted are precipitation minus evaporation (P-E), terrestrial water storage, total runoff, and snow water equivalence. Then, we investigate the potential predictability based on an ensemble climate simulations of an atmospheric global climate model (AGCM).

Potential Predictabilility |

The potential predictability as the maximum predictability that can be reached,
is defined using a perfect model when the SST and sea ice cover are perfectly
predicted. The standard statistical tool, "alysis of variance,’" is used
with data from an ensemble of climate simulations, to separate the total
atmospheric variance (σ_{TOT}^2) of a time-averaged quantity into
two components; variability forced by the lower boundary conditions of
SST and sea ice (σ_{SST}^2) and random internal variability
(σ_{INT}^2). The variance ratio (R) is used as a measure of the
potential predictability as

R=σ_{SST}^2/sigma;_{TOT}^2.

Since the SST prediction never reaches the perfect, actual potential
predictability is always below this value.
The unity of R represent the perfect prediction.

Figure 1 presents the geographical distribu- tion of the variance ratio of P-E. The variance ratio was high in low latitudes, and low in mid and high latitudes. The variance ratio over land was lower than over oceans in the same lati- tude. Only the variance ratios in the Sahel and northern (tropical) part of South America re- mained high in both seasons. The variance ra- tios in India, Indochina and the Middle East were higher in JJA, but over Japan and the eastern part of China they were higher in DJF. The variance ratio was generally small outside the Tropical zone, because the random variability is larger than the SST-forced variability.

Figure 2 depicts the variance ratio of snow water equivalence for snow melting season, March-April-May. The ratios were generally low. However, the values in the Tibetan Plateau and in the coastal areas of the Gulf of Alashka in this season reach 0.2, although the ratios for P-E were low. This high ratios stem from the high variance ratio of surface air temperature through a teleconnection, Pacific-North-America pattern, excited by El Nino and La Nina phenomena.

Fig. 1: The variance ratio of P-E for December-January-February, computed from the ensemble of six 1959-1998 runs.

Fig. 2: The variance ratio of snow water equivalence for March-April-May, computed from the ensemble of six 1959-1998 runs.

References

- T. Nakaegawa, M. Sugi, and K. Matsumaru, 2003. A Long-term Numerical Study
of the Potential Predictability of Seasonal Mean Fields of Water
Resource Variables using MRI/JMA-AGCM, Journal of Meteorological
Society of Japan, 81(5), 1041-1056
*Click here for Full Paper* - T. Nakaegawa, S. Kusunoki, M. Sugi, A. Kitoh, C. Kobayashi, and K. Takano
A study of dynamical seasonal prediction of potential water
resources based on an atmospheric GCM experiment with prescribed
sea-surface temperature. Hydrological Sciences Journal, 52(1),
152-165, doi: 10.1623/hysj.52.1.152.
*Click here for Full Paper*

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