Evaluation of the forecast skill of North American Multi-Model Ensemble for monthly and seasonal precipitation forecasts over Iran

Abstract

North American Multi-Model Ensemble (NMME) precipitation forecast skill over Iran is evaluated using Taylor diagrams and ranked probability skill scores (RPSS) as determined over a 29-year test period (1991–2019). The forecast skill for both monthly (October through June for lead-times of 0.5–3.5 months) and seasonal (October–December [OND], January–March [JFM], and April–June [AMJ] for lead-times of 1.5–3.5 months) timescales is evaluated using six NMME models as well as multi-model ensemble means (MMM). The latest versions of these models for forecasting Iran's precipitation have not been evaluated thus far. The Global Precipitation Climatology Center (GPCC) version 2020 dataset is used to verify the models. Among individual NMME models, Geophysical Fluid Dynamics Laboratory-Seamless System for Prediction and Earth System Research (GFDL-SPEAR) has generally the highest forecast skill. Both Taylor diagrams and RPSS of most of the models have indicated that the highest forecast skill is found for the month of November such that the Pearson correlation for both SPEAR and MMM is statistically significant for all lead-times. For both monthly and seasonal timescales, the temporal Pearson correlation (TPC) between the observed and forecasts of the MMM is higher than the TPC of the individual models. The spatial Pearson correlation (SPC) and normalized centred root mean square error (NCRMSE) of the SPEAR is close to MMM, but the normalized standard deviation (NSD) of the SPEAR is closer to one compared to the MMM for months from November to March and two seasons (OND and JFM seasons). The MMM precipitation forecasts are underestimated over the northern regions and Zagros mountains for JFM and OND seasons for both 1.5- and 2.5-month lead-times. The degree to which the forecast skill of MMM is dependent on the El Niño–Southern Oscillation (ENSO) connections with precipitation over Iran is examined. Significant Spearman correlations between simultaneous observed Niño3.4 index and Iran precipitation are found for OND, but not for JFM and AMJ. The MMM reproduces the observed ENSO teleconnections to the tropical Pacific in OND, consistent with forecast skill in that season. However, the MMM also produces forecast skill in JFM and AMJ when the ENSO influence is marginal, showing that ENSO is not the only source of skill in the models.