No abstract

Analysis is performed on a set of diagnostic numerical experiments designed to isolate local Indian Ocean forcing versus remote forcing from the Pacific via the Indonesian throughflow on decadal variability of subsurface temperature, sea level, and thermocline depth of the South Indian Ocean. It is found that the vertical structure of decadal temperature variability varies from decade‐to‐decade, with maximum variation peaking in the vicinity of the thermocline. The decadal‐scale temperature variations in the tropical southwestern Indian Ocean between 5°S and 17°S are primarily associated with the vertical displacements of the thermocline. Prior to the early 1990s, decadal variations in sea level and thermocline depth can be described in terms of a baroclinic Sverdrup balance, forced by Ekman pumping velocity associated with windstress curl acting on the Indian Ocean. Beginning in the early 1990s, decadal variability of the equatorial Pacific trades forces thermocline variations that modify the sea level and thermocline depth across the tropical South Indian Ocean basin. Farther south, between 20°S and 30°S, oceanic internal variability makes significant contributions to decadal variability of the thermocline. The anomalies along the western coast of Australia are primarily driven by regional forcing acting on the Indian Ocean prior to the 1990s, and signals originating from the equatorial Pacific make a greater contribution thereafter.

The relative importance of local versus remote forcing on intraseasonal-to-interannual sea level and thermocline variability of the tropical south Indian Ocean (SIO) is systematically examined by performing a suite of controlled experiments using an ocean general circulation model and a linear ocean model. Particular emphasis is placed on the thermocline ridge of the Indian Ocean (TRIO; 5°–12°S, 50°–80°E). On interannual and seasonal time scales, sea level and thermocline variability within the TRIO region is primarily forced by winds over the Indian Ocean. Interannual variability is largely caused by westward propagating Rossby waves forced by Ekman pumping velocities east of the region. Seasonally, thermocline variability over the TRIO region is induced by a combination of local Ekman pumping and Rossby waves generated by winds from the east. Adjustment of the tropical SIO at both time scales generally follows linear theory and is captured by the first two baroclinic modes. Remote forcing from the Pacific via the oceanic bridge has significant influence on seasonal and interannual thermocline variability in the east basin of the SIO and weak impact on the TRIO region. On intraseasonal time scales, strong sea level and thermocline variability is found in the southeast tropical Indian Ocean, and it primarily arises from oceanic instabilities. In the TRIO region, intraseasonal sea level is relatively weak and results from Indian Ocean wind forcing. Forcing over the Pacific is the major cause for interannual variability of the Indonesian Throughflow (ITF) transport, whereas forcing over the Indian Ocean plays a larger role in determining seasonal and intraseasonal ITF variability.

This paper demonstrates that an operational forecast model can skillfully predict week-3–4 averages of temperature and precipitation over the contiguous United States. This skill is demonstrated at the gridpoint level (about 1° × 1°) by decomposing temperature and precipitation anomalies in terms of an orthogonal set of patterns that can be ordered by a measure of length scale and then showing that many of the resulting components are predictable and can be predicted in observations with statistically significant skill. The statistical significance of predictability and skill are assessed using a permutation test that accounts for serial correlation. Skill is detected based on correlation measures but not based on mean square error measures, indicating that an amplitude correction is necessary for skill. The statistical characteristics of predictability are further clarified by finding linear combinations of components that maximize predictability. The forecast model analyzed here is version 2 of the Climate Forecast System (CFSv2), and the variables considered are temperature and precipitation over the contiguous United States during January and July. A 4-day lagged ensemble, comprising 16 ensemble members, is used. The most predictable components of winter temperature and precipitation are related to ENSO, and other predictable components of winter precipitation are shown to be related to the Madden–Julian oscillation. These results establish a scientific basis for making week-3–4 weather and climate predictions.

The vertical structure of temperature change in the Indian Ocean (IO) during 1961–2000 indicates that a region of tropical thermocline cooling accompanies the upper level warming. Results from data analysis and ocean general circulation model experiments suggest that the cooling signals exceed the cross‐data and cross‐model differences. Spatial patterns of the temperature trend above 200 m resemble the negative IO dipole structure, with the strongest cooling occurring in the western‐central basin south of the equator. The upper thermocline cooling is mainly caused by enhanced Ekman pumping velocity, which shoals the thermocline. The enhanced upwelling is consistent with the strengthened Southern subtropical cell. Enhanced equatorial westerly winds contribute to the negative dipole pattern. Remote forcing from the Pacific may contribute to the cooling below 200 m and further south.