Articles written in Journal of Earth System Science

    • Arctic summer sea-ice seasonal simulation with a coupled model: Evaluation of mean features and biases

      Saheed P P Ashis K Mitra Imranali M Momin Rajagopal E N Helene T Hewitt Ann B Keen Sean F Milton

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      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.

    • Summer monsoon climate simulations over BIMSTEC countries using RegCM4 regional climate model

      Prasanta Kumar Bal Ashis K Mitra

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      Regional climate models are useful by downscaling from global climate model simulations for climate studies and climate applications at a regional scale. The South Asian monsoon region is one of the most challenging regions towards understanding the monsoon variability by implementing various climate simulations. A model version of the RegCM4 regional climate model provided by the International Centre for Theoretical Physics (ICTP) is used in this study to simulate the climate of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries (India, Bangladesh, Bhutan, Nepal, Myanmar, Thailand and Sri Lanka) during the periods 1985–2000, which shows the present-day climate simulations. The simulation is carried out with RegCM4.3 with the lateral boundary forcing provided by the European Center for Medium Range Weather Forecast Reanalysis (Era-Interim) at 25 km horizontal resolution. The convective scheme Grell with closures Arakawa–Schubert is investigated during the south-west monsoon seasons (June–September, JJAS). The results indicate that the JJAS surface mean temperature has a large cold bias of 2–6$^{\circ}$C over the coastal regions near India, Myanmar, Thailand, Sri Lanka and Bangladesh when compared with the Climate Research Unit (CRU) observed dataset. The model is able to produce the spatial rainfall distribution pattern of the observed CRU and Global Precipitation Climatology Project (GPCP) averaged seasonal rainfall in most of the land regions over the BIMSTEC countries during this period, but with underestimation. Further, the south-westerly prevailing wind pattern at 850 hPa pressure level is also well captured by the model but with a higher intensity over Sri Lanka, Western India, Nepal and Thailand. The westerly is stronger over the Bay of Bengal in the model simulations than the observed Era-Reanalysis data sets obtained from ECMWF. Also, the model is able to capture the mean monsoon circulation features. This model may be used for a few of the BIMSTEC regions for climate study applications.

    • Unified model rainfall forecasts over India during 2007–2018: Evaluating extreme rains over hilly regions


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      Prediction of heavy/extreme rains is still a challenge, even for the most advanced state-of-the-art high-resolution Numerical Weather Prediction (NWP) modelling systems. Hydrological models use the rainfall forecasts from the NWP models as input. This study evaluates the performance of the UK Met Office Unified Model (UM) in predicting the rainfall exceeding 80th and 90th percentiles. Such high rainfall amounts occur over the Western Ghats (WGs) and North East (NE) India mainly due to the forced ascent of air parcels. Apart from the significant upgrades in the UM's dynamical core, the model features an increased horizontal grid (40–10 km) and vertical resolution (50–70 levels). The prediction skill of heavy rainfall events improves with an increased horizontal resolution of the model. The probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are the verification metrics used. As per these metrics, model rainfall forecasts have improved during 2007–2018 (increase in CSI from 0.29 to 0.38, POD from 0.45 to 0.55, and decrease in FAR from 0.55 to 0.45). Additionally, to verify extreme and rare events, the symmetric extremal dependence index (SEDI) is also used. SEDI also shows an increase from 0.47 to 0.62 and 0.16 to 0.41 over WGs and NE India during the study period, suggesting an improved skill of predicting heavy rains over the mountains. The improved forecast performance is consistent and relatively higher over WGs than over NE states.

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