Identifying the sources of seasonal potential predictability using Fractional Integral Statistical Model with a Variance Decomposition Method

<p>It is well recognized that climate predictability has three origins: (i)climate memory (inertia of the climate system) that accumulated from the historical conditions, (ii) responses to external forcings, and (iii) dynamical interactions of multiple processes in the climate system. However, how to systematically identify these predictable sources is still an open question. Here, we combine a recently developed Fractional Integral Statistical Model (FISM) with a Variance Decomposition Method (VDM), to systematically estimate the potential sources of predictability. With FISM, one can extract the memory component from the considered variable. For the residual parts, VDM can then be applied to extract the slow varying covariance matrix, which contains signals related to external forcings and dynamical interactions of multiple processes in climate. To show the improvement of our methodology, we have tested it on realistic data, using monthly temperature observations over China during 1960-2017.  It is found that the climate memory component contributes a large portion of the seasonal predictability in the temperature records. Our results offer the potential for more skillful seasonal predictions compared with the results obtained using FISM or VDM alone.</p>

Spatial Coherence and Seasonal Predictability of Monsoon Onset over Indonesia

The seasonal potential predictability of monsoon onset during the August–December season over Indonesia is studied through analysis of the spatial coherence of daily station rainfall and gridded pentad precipitation data from 1979 to 2005. The onset date, defined using a local agronomic definition, exhibits a seasonal northwest-to-southeast progression from northern and central Sumatra (late August) to Timor (mid-December). South of the equator, interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, together with local-scale noise. The high spatial coherence of onset is similar to that of the September–December seasonal total, while postonset amounts averaged over 15–90 days and September–December amount residuals from large-scale onset show much less spatial coherence, especially across the main islands of monsoonal Indonesia. The cumulative rainfall anomalies exhibit also their largest amplitudes before or near the onset date. This implies that seasonal potential predictability over monsoonal Indonesia during the first part of the austral summer monsoon season is largely associated with monsoon onset, and that there is much less predictability within the rainy season itself. A cross-validated canonical correlation analysis using July sea surface temperatures over the tropical Pacific and Indian Oceans (20°S–20°N, 80°–280°E) as predictors of local-scale onset dates exhibits promising hindcast skill (anomaly correlation of ∼0.80 for the spatial average of standardized rain gauges and ∼0.70 for standardized gridded pentad precipitation data).