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This study analyses the prediction of Indian monsoon low-pressure systems (LPSs) on an extended time scale of 15 days by models of the Subseasonal-to-Seasonal (S2S) prediction project. Using a feature-tracking algorithm, LPSs are identified in the eleven S2S models during a common re-forecast period of June September 1999-2010, and then compared with 290 and 281 LPSs tracked in ERA-Interim and MERRA-2 reanalysis datasets. The results show that all S2S models under-simulate the frequency of LPSs. They are able to represent transits, genesis and lysis of LPSs; however, large biases are observed in the AustralianBureau of Meteorology, China Meteorological Administration (CMA) and Hydrometeorological Centre of Russia (HMCR) models. The CMA model exhibits large LPS track position error and the intensity of LPSs is overestimated (underestimated) by most models when verified against ERA-Interim (MERRA-2). The European Centre for Medium-Range Weather Forecasts and UK Met Office models have the best ensemble spread-error relationship for the track position and intensity, whereas the HMCR model has the worst. Most S2S models are underdispersive - more so for the intensity than the position. We find the influence of errors in the LPS simulation on the pattern of total precipitation biases in all S2S models. In most models, precipitation biases increase with forecast lead time over most of the monsoon core zone. These results demonstrate the potential for S2S models at simulating LPSs, thereby giving the possibility of improved disaster preparedness and water resources planning.

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The structure of strong Indian monsoon low‐pressure systems (LPSs) up to forecast lead times of 15 days in 11 models of the Subseasonal‐to‐Seasonal (S2S) Prediction Project is analysed. Strong LPS (SLPS) tracks are obtained from a catalogue of LPSs tracked in all ensemble members of the S2S models during a common reforecast period of June–September 1999–2010. SLPSs, which have a minimum intensity equal to at least the upper‐quartile intensity of all LPSs, are then composited to generate horizontal and vertical structures of several dynamic and thermodynamic fields. The evolution of fields with forecast lead time and during LPS lifecycle is analysed. Furthermore, the simulation of the lower‐tropospheric monsoon circulation, precipitation biases, and the precipitation contribution of LPSs are analysed. All S2S models and the multimodel mean simulate the lower‐tropospheric monsoon circulation, but prominent dry biases are observed in the Australian Bureau of Meteorology and Environment and Climate Change Canada models. The precipitation contribution of LPSs to the summer mean precipitation is smaller in all S2S models than in tracks derived from ERA‐Interim reanalysis. The location and amplitude of the lower‐tropospheric cold core and the location of maximum precipitation are not well simulated by many models, particularly by the Hydrometeorological Centre of Russia model, in which the cold core is missing altogether. The structure of relative vorticity anomaly in all S2S models and the multimodel mean is shallower and weaker than in ERA‐Interim and MERRA‐2 reanalyses. Though the cold core intensifies through the LPS lifecycle in all models, the warm core features a midlife maximum, except in models such as Australian Bureau of Meteorology and China Meteorological Administration. These results demonstrate the potential for S2S models at simulating the structure of SLPSs, benefiting stakeholders that use S2S models for forecasting.

<p>Indian monsoon low-pressure systems (LPSs) are synoptic-scale cyclonic vortices that produce around half of the summer monsoon rainfall over India, and often cause catastrophic floods. Thus, accurate predictions of LPSs are crucial for disaster management and long-term planning. To improve the skill of LPS forecasts, it is important to understand how seasonal forecast models simulate the structure and behaviour of these weather systems. Here we examine in detail the simulation of the structure of LPSs by eleven models of the Subseasonal-to-Seasonal (S2S) prediction project. We use a feature-tracking algorithm to identify LPSs in all S2S models during a common re-forecast period of June–September 1999­­–2010. We then generate composite horizontal and vertical structures and compare them with those of LPSs in ERA-Interim reanalysis. <br><br>The results suggest that LPSs have the weakest intensity as well as precipitation in the Bureau of Meteorology (BoM), Australia, Hydrometeorological Centre of Russia (HMCR) and Japan Meteorological Agency models. Most S2S models simulate the warm-over-cold core structure that is commonly observed in LPSs, except for the BoM and HMCR models, which simulate weak positive temperature anomalies near the LPS centre in the lower troposphere. The vertical structure of relative vorticity is shallower and weaker in all S2S models than in ERA-Interim. In most S2S models, LPS composites feature a drier middle and upper troposphere than in ERA-Interim. There is a strong positive correlation between precipitation and the 925 hPa temperature anomaly in most S2S models and ERA-Interim supporting the hypothesis that evaporative cooling from precipitation and reduced insolation due to significant cloud cover are responsible for the lower-tropospheric cold core.</p>