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.

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


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      For Earth’s climate system, the study of the seasonal variability of sea-ice is important as the sea-ice has a significant impact on the net radiative flux, which can inCuence the mean seasonal behaviours of the atmosphere and ocean. In this study, the seasonal hindcast of 14 austral winter seasons is conducted toassess the skill of a coupled model in simulating the seasonal Antarctic sea-ice and its connection with the other ocean and atmospheric variables. The GloSea4 set-up of the HadGEM3 coupled model is used for the seasonal simulations at the NCMRWF. The model could reproduce the sea-ice extent over the Antarctic for the Austral winter seasons with an average correlation value of 0.98. However, there are moderate biases in the sea-ice concentration. The sea-ice thickness in the model generally shows negative bias, which is not seen to be related to the surface air temperature biases in the coupled system. The moderate positive (warm) biases in the sea surface temperature extending into the upper ocean (30 m), combined with the sea-ice drift bias pattern away from the sea-ice region are the main reasons for the underestimation of sea-ice thickness in the model. The sea surface current bias pattern shows a poleward component that brings the warm water from the warm biased locations of the exterior region into the seaiceregion and explains the presence of warmer waters in the sea-ice regions. The anti-clockwise bias in the surface wind is seen to impact the surface current, Antarctic circumpolar current (ACC), having a similar anti-clockwise current bias. Despite these moderate biases in the model, the inter-annual variability ofsea-ice extent is having a reasonably good skill. The model is suitable for extended/seasonal prediction of sea-ice during Austral winter for Antarctic.

    • Improved skill of NCMRWF Unified Model (NCUM-G) in forecasting tropical cyclones over NIO during 2015–2019


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      Operational forecasting of tropical cyclone (TC) track and intensity in the India Meteorological Department (IMD) relies more and more on the numerical weather prediction (NWP) model guidance from national and international agencies particularly, on the medium range (24–120 h). Any improvement in TC forecasts by the NWPmodels enhances the operational forecaster’s confidence and capability. The real-time information from the National Centre for Medium Range Weather Forecasting (NCMRWF) global NWP model (NCUM-G) is routinely used by operational forecasters at IMD as model guidance. The present study documents the improved skill of NCUM-G in forecasting the North Indian Ocean (NIO) TCs during 2015–2019, based on a collection of 1810 forecasts involving 22 TC cases. The study highlights three significant changes in the modelling system during the recent five years, namely (i) increased grid resolution from 17 to 12 km, (ii) use of hybrid 4D-Var data assimilation (DA), and (iii) increased volume of assimilated data. The study results indicate a consistent improvement in the NCUM-G model forecasts during the premonsoon (April–May,AM)and post-monsoon (October–December,OND)TCseasons. In addition to a 44% reduction in the initial position error, the study also reports a statistically significant decrease in the direct position error (DPE) and error in the intensity forecast, resulting in a forecast gain of 24 hrs. Comparing NWP models with IMDs official track error shows that NCUM-G and ECMWF model forecasts feature lower DPE than IMD in 2019, particularly at higher (96, 108, and 120 h) lead times.

    • Evaluation of five high-resolution global model rainfall forecasts over India during monsoon 2020


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      This study aims to evaluate the performance of five global medium-range operational NWP model rainfall forecasts, namely NCUM, UKMO, IMD GFS, NCEP GFS and ECMWF to provide an intercomparison of rainfall forecasts over India in terms of skill in predicting daily rainfall (24-hr accumulated rainfall). Veri- fication and intercomparison of rainfall forecasts over India during monsoon 2020 (JJAS) are carried out using both (i) standard traditional verification methods (POD, FAR, RMSE, etc.) and (ii) advanced spatial verification methods (MODE, FSS). The evaluation also includes assessment of large-scale mean patterns, temporal evolution of spells during the season, dominant modes using spectral analysis, basin-scale rainfall time series and isolated heavy rainfall cases. Our analysis suggests that some of the key large-scale aspects of monsoon (seasonal mean, active/break spells, and northward propagation) are realistically represented in all the models, with slight discrepancies. In addition, the spectral analysis of rainfall is in association with observed rainfall in Day-1 forecast and deteriorates with lead times. Synoptic variance in NCUM on longer leading times is closer to observations. While the standard categorical verification over India as a whole (spatial averaged) suggests that ECMWF forecast skill is relatively high among the Bve models, the veri-fication over the sub-regions shows mixed results with no clear unique higher performer among the models. In addition, basin-scale verification of rainfall forecasts for five rivers over the Indian subcontinent shows a fairly good amount of skill in terms of CC and RMSE up to Day-3 with comparable scores among the models. The advanced spatial verification metrics, like MODE and FSS, applied to the models show varying skills with different attributes. However, for FSS, forecast skill was high (low) for lower (higher) rainfall thresholds of 20 mm/day (100 mm/day). Though different models with different spatial resolutions show reasonable skill scores for larger regions, for high-impact heavy rainfall events, which are generally localised, the models have very comparable poor skill with no clear edge by a model among the five models.

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