ASSESSMENT OF POSSIBLE CHANGES IN THE EL NINO AND LA NINA REPEATABILITY BY THE END OF THE XXI CENTURY USING THE CMIP6 MODELS

The quality of SST anomalies revealed in the equatorial Pacific associated with El Niño (EN) and La Ni-ña (LN) in the CMIP6 project models (KIOST-ESM, MIROC-ES2L and INM-CM4-8) was evaluated by comparing with real events in the period 1950 to 2014 using the ERSSTv5 data sets. It is shown that the ensemble model estimation of the number, intensity and duration of EN and LN corresponds quite well to real conditions. On this basis, the corresponding model ensemble calculations of their future possible changes in 2021-2085 relative to the historical 1950-2014 period were carried out for two possible sce-narios: business-as-usual (SSP2-4.5) and negative (SSP5-8.5).

Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.

Performance of IMD multi-model ensemble and WRF (ARW) based sub-basin wise rainfall forecast for Mahanadi basin during flood season 2009 and 2010

egkunh ds csflu esa 2009 o 2010 ds ck<+ ds ekSle ds nkSjku micsfluokj o"kkZ ds iwokZuqeku rFkk 2010 esa ck<+ ds ekSle ds le; izpkyukRed ¼9 fd-eh- × 9 fd-eh-½ fun'kZ ¼vkb-Z ,e- Mh-½ dk vkdyu djus ds fy, Hkkjr ekSle foKku foHkkx ¼vkb-Z ,e- Mh-½ ds izpkyukRed cgq&fun'kZ bUlSacy ¼,e-,e-bZ-½ ¼27 fd-eh- × 27 fd-eh-½ ds vk/kkj ij o"kkZ ds iwokZuqeku dk mi;ksx fd;k x;k gSA micsflu Lrj ij ,e-,e-bZ- vkSj MCY;w-vkj-,Q- ds dk;Z fu"iknu dk foLr`r v/;;u fd;k x;k gSA blls irk pyk gS fd lkekU;r% Hkkjh o"kkZ dh ?kVukvksa dks ekWMyksa }kjk de djds vkdfyr fd;k tkrk gSA Operational Multi-model Ensemble (MME) (27 km × 27 km) based rainfall forecast of India Meteorological Department (IMD) are utilized to compute rainfall forecast sub-basin wise for Mahanadi basin during flood season 2009 & 2010 and operational WRF (ARW) (9 km × 9 km) model (IMD) during flood season 2010. The performance of the MME and WRF at the sub-basin level are studied in detail. It is observed that generally heavy rainfall events are under estimated by the models.

Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012

The Numerical Weather Prediction models, Multi-model Ensemble (MME) (27 km × 27 km) and WRF (ARW) (9 km × 9 km) operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin wise rainfall forecast. The sub-basin wise operational Quantitative Precipitation Forecast (QPF) have been issued by 10 field offices named Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin wise NWP model rainfall forecast for 122 sub basins under these 10 FMOs for the flood season 2012 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of the MME and WRF (ARW) models rainfall at the sub-basin level have been studied in detail. The performance of WRF (ARW) and MME models is compared in the heavy rainfall case over the river basins (Mahanadi etc.) falls under FMO, Bhubaneswar and it is found that WRF (ARW) model gives better result than MME. It is also found that performance of WRF (ARW) is little better than MME when compared over all the flood prone river sub basins of India. For high rainfall categories (51-100, >100 mm), generally these leads to floods, the success rate of model rainfall forecasts are less and false alarms are more. The NWP models are able to capture the rainfall events but there is difference in magnitudes of sub basin wise rainfall estimates.

Eurodelta multi-model simulated and observed PM trends in Europe in the period of 1990–2010

The Eurodelta-Trends multi-model experiment, aimed to assess the efficiency of emission mitigation measures in improving air quality in Europe during 1990–2010, was designed to answer a series of questions regarding European pollution trends. i.e. were there significant trends detected by observations? do the models manage to reproduce observed trends? how close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to PM pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000–2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the models simulated trends could be regarded as an indicator for modelling uncertainty. The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5, with reduction by between 2 and 6 μg m−3 m−3 (or between 10 and 30 %) from 2000 to 2010. Compared to PM2.5, relative PM10 trends are weaker due to large inter-annual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is a reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30–40 % over most of Europe, increasing to 50–60 % in northern and eastern parts of EDT domain. Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble simulated trends are −0.24 and −0.22 μg m−3 year−1 for PM10 and PM2.5, which are somewhat weaker than the observed trends of −0.35 and −0.40 μg m−3 year−1, respectively, partly due to models underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are −1.7 and −2.0 % year−1 from the model ensemble and −2.1 and −2.9 % year−1 from the observations, respectively. The observations identify significant trends for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries. The strongest decreasing PM trends and the largest number of sites with significant trends is found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. One important reason for that is the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modeled versus observed PM trends are limited the regions where the sites are located. The analysis reveals a considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in SO4−2 concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH4+ and NO3− to PM10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appears to be a dominant factor in bringing down PM10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties and the effect of inter-annual meteorological variability on the trend analysis.