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An extended range prediction system (NERP) based on the Unified global coupled modelling system has been implemented at National Centre for Medium Range Weather Forecasting (NCMRWF). Predictions of weekly anomalies for rainfall, winds and surface temperature are issued once a week for the subsequent 4 weeks. This study is an assessment of the skill of the NERP system over India during the summer monsoon period of June to September (JJAS). The week‐1 and week‐2 rainfall anomaly forecasts exhibit good prediction skill over the Indian region. The evaluations made on meteorologically homogeneous regions of India show moderate to good skill even in week‐3 and week‐4 forecasts for some regions. The variability of the monsoon seasonal cycle in the model is well represented along with the magnitude of summer monsoon rainfall. The model brings out the mean monsoon rainfall features – the peaks during JJAS occurring over the Western Ghats, the Arakan coast and the Bay of Bengal. The onset of the monsoon and the interannual variability of the northward propagation of the rainfall anomalies over the Indian longitudes match reasonably well with observations. The model has some wet bias over the equatorial Indian Ocean and mild dry bias over the Indian land region that develop during the 4‐week period. The anomaly correlations of rainfall are significant over most of the subdivisions of India till week‐3 and week‐4. Probabilistic scores of reliability Receiver Operating Characteristic (ROC) and fair continuous ranked probability score (FCRPS) show that the model exhibits good skill over most regions till week‐2 and moderate skill in weeks 3–4 over other regions. The estimates of forecast reliability are low, a likely consequence of the limited ensemble size. A case‐study of year 2018 showed that the JJAS rainfall regime shifts from wet to dry and vice versa are captured well. [ABSTRACT FROM AUTHOR]

The successful planning and execution of a major field campaign relies on the availability and reliability of weather forecasts on a range of time‐scales. Here, we describe the wide range of forecast products generated in support of a field campaign that took place in India in 2016 as part of the Interaction of Convective Organization with Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) project. We show examples of the suite of plots generated every day from the forecasts and supplied to the mission scientists, and describe how these were used to plan the flights. We highlight the benefits of having access to forecasts from a range of model resolutions and configurations; these allowed judgements to be made about uncertainty, particularly in the amount and location of deep convective rainfall, which is an important consideration for flight planning. Finally, we discuss the legacy of the forecasting activity, which has not only advanced our understanding of monsoon forecasting but also created a large database of targeted model forecast products for the whole of the 2016 monsoon season. These can be used by researchers for comparisons with in situ observations as well as future modelling studies. [ABSTRACT FROM AUTHOR]

Current state of the art weather/climate models are representation of the fully coupled aspects of the components of the earth system. Sea-ice is one of the most important components of these models. Simulation of sea-ice in these models is a challenging problem. In this study, evaluation of the hind-cast data of 14 boreal summer seasons with global coupled model HadGEM3 in its seasonal set-up has been performed over the Arctic region from 9th May start dates. Along with the biases of the sea-ice variables, related atmosphere and oceanic variables have also been examined. The model evaluation is focused on seasonal mean of sea-ice concentration, sea-ice thickness, ocean surface current, SST, ice-drift velocity and sea-ice extent. To diagnose the sea-ice biases, atmospheric variables like, 10 m wind, 2 m air temperature, sea-level pressure and ocean sub-surface temperatures were also examined. The sea-ice variables were compared with GIOMAS dataset. The atmospheric and the oceanic variables were compared with the ERA Interim and the ECMWF Ocean re-analysis (ORAP5) datasets, respectively. The model could simulate the sea-ice concentration and thickness patterns reasonably well in the Arctic Circle. However, both sea-ice concentration and thickness in the model are underestimated compared to observations. A positive (warm) bias is seen both in 2 m air temperature and SST, which are consistent with the negative sea-ice bias. Biases in ocean current and related ice drift are not related to biases in the atmospheric winds. The magnitude of the oceanic subsurface warm biases is seen to be gradually decreasing with depth, but consistent with sea-ice biases. These analyses indicate a possibility of deeper warm subsurface water in the western Arctic Ocean sector (Pacific and Atlantic exchanges) affecting the negative biases in the sea-ice at the surface. The model is able to simulate reasonably well the summer sea-ice melting process and its inter-annual variability, and has useable skill for application purpose. [ABSTRACT FROM AUTHOR]

Recent CMIP5 models predict large losses of summer Arctic sea ice, with only mitigation scenarios showing sustainable summer ice. Sea ice is inherently part of the climate system, and heat fluxes affecting sea ice can be small residuals of much larger air-sea fluxes. We discuss analysis of energy budgets in the Met Office climate models which point to the importance of early summer processes (such as clouds and meltponds) in determining both the seasonal cycle and the trend in ice decline. We give examples from Met Office modelling systems to illustrate how the seamless use of models for forecasting on time scales from short range to decadal might help to unlock the drivers of high latitude biases in climate models., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)