Climate predictions play a crucial role in risk management and adaptation to the effects of climate variability and change across many regions worldwide. General Circulation Models (GCMs) serve as powerful tools for forecasting regional climate patterns. The Pakistan Meteorological Department releases monthly and seasonal forecast products during the last week of each month. For multi-model ensemble seasonal predictions, outputs from the following models are utilized.

Prior to constructing the Multi-Model Ensemble (MME), each model undergoes systematic hindcast-based evaluation against observed datasets over the hindcast period. Only models meeting predefined skill thresholds are retained for operational ensemble generation. This objective screening ensures that ensemble guidance is derived exclusively from skillful and independently performing systems. The operational outlook is produced within an MME framework that integrates both deterministic and probabilistic guidance.

Deterministic forecasts are generated as equally weighted ensemble mean anomalies from the selected models. For each model, ensemble mean anomalies are calculated relative to its own hindcast climatology to reduce systematic biases and ensure inter-model consistency. These anomaly forecasts are then combined using a Simple Composite Method, which mitigates individual model errors and enhances reliability, particularly in regions where inter-model agreement is strong.

Probabilistic forecasts are derived from the full pooled ensemble distribution of all participating models. Tercile-based probabilities (below-normal, near-normal, and above-normal) are estimated using a Gaussian fitting approach, with model contributions weighted according to the square root of their ensemble size (being inversely proportional to the model’s outlook uncertainty) to reflect ensemble robustness and reduce random error influence. This methodology provides an objective quantification of forecast uncertainty and spread, thereby strengthening the operational value of ensemble guidance for risk-informed decision-making.

References

Gohar Ali, Furrukh Bashir, Nilofar Yaqeen, Mohib Ullah Khan (2026). Multi-Model Ensemble Approach for Seasonal Rainfall Forecasting over Pakistan: Methodology and Performance Assessment. Pakistan Journal of Meteorology. (Under Review).

Min, Y. M., Kryjov, V. N., & Oh, S. M. (2014). Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real-time forecasts (2008–2013). Journal of Geophysical Research, 119(21), 12,132-12,150. https://doi.org/10.1002/2014JD022230

Min, Y. M., Kryjov, V. N., & Park, C. K. (2009). A probabilistic multimodel ensemble approach to seasonal prediction. Weather and Forecasting, 24(3), 812–828. https://doi.org/10.1175/2008WAF2222140.1

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