ALBERT

Description: In this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981–2008 indicates that while the predictions of rainfall anomalies have poor skill, the mean flow patterns are brought out reasonably well by the model. The model precipitation is found to be more strongly dependent on sea surface temperature over the Nino regions in the Pacific Ocean. However, the observed precipitation has a stronger links to winds at 850 hPa near the Somali coast than is evident in the model. On the basis of correlation maps, potential model predictors (specific humidity and zonal and meridional winds over different regions at different levels) are chosen for CCA for the prediction of ISMR. Using leave-three-out cross-validation technique, canonical coefficients are computed using 25 years data (as training period) for CCA model. With this, predictions from the CCA model have also been prepared for the period of 1981–2005 to evaluate the performance. In addition to the above, predictions are made for four independent years (2006–2009). An improvement in skill of the composite forecasts (obtained using all the predictors) in terms of interannual variability is noticed over some parts of east- and northeast India as well as many parts of peninsular region especially over west coast of India. Copyright © 2012 Royal Meteorological Society

Description: This study presents the results of high-resolution (30 km) climate simulations over North India using an optimized configuration of the Regional Climate Model (RegCM), driven by a global spectral model (T80 model with horizontal resolution of ~1.4°) for a period of 28 years (1982–2009). The main aim of this work is to analyze the capabilities of the RegCM to simulate the wintertime precipitation over North India in the recent past. The RegCM validation revealed a good improvement in reproducing the precipitation compared to results obtained from the T80 model. This improvement comes due to better representation of vertical pressure velocity, moisture transport, convective heating rate and temperature gradient at two different latitudinal zones. Moreover, orography in the high-resolution RegCM improves the precipitation simulation in the region where sharp orography gradient plays an important role in wintertime precipitation processes. Two bias correction (BC) methods namely mean bias-remove (MBR) and quantile mapping (QM) have been applied on the T80 driven RegCM model simulations. It was found that the QM method is more skillful than the MBR in simulating the wintertime precipitation over North India. A comparison of model-simulated and bias corrected precipitation with observed precipitation at 17 station locations has also been carried out. Overall, the results suggest that when the BC is applied on dynamically downscaled model, it has better skill in simulating the precipitation over North India and this model is a useful tool for further regional downscaling studies.

Description: ABSTRACT This study aims to analyse the skill of state-of-the-art of five general circulation models (GCMs) in predicting winter precipitation over northern India. The precipitation in winter season (December, January and February) is very important for Rabi crops in north India, particularly for wheat, as it supplements moisture and maintains low temperature for the development of the crops. The GCM outputs (seasonal mean forecasts issued in November) from various organizations are compared with the observed high-resolution gridded rainfall data obtained from India Meteorological Department (IMD). Prediction skill of such GCMs is examined for the period 1982–2009. The climatology, interannual standard deviation (ISD) and correlation coefficients have been computed for the five GCMs and compared with observation. It is found that the models are able to reproduce the climatology and ISD to varying degrees; however, skill of predictions is too low. Multi-model ensemble (MME) approaches have been employed. It is found that the weighted MME using multiple linear regression technique improves the prediction skill of winter precipitation over northern India. The teleconnection between the sea surface temperature (SST) and winter precipitation revealed that the SST over the Pacific Ocean affects the precipitation over north India in winter season. While this observed feature is represented well by some models with high fidelity, most models are unable to respond to SST variations in the Pacific Ocean in a realistic manner. Lagged correlations between the north India rainfall and SST over the Niño-3.4 region reveal that only two of the five GCMs get the observed simultaneous teleconnection correctly. Furthermore, only one of these two models has the observed phase lag with the strongest correlation as observed.

Description: Statistical downscaling approach (canonical correlation analysis [CCA]) and dynamical downscaling (regional climate model [RegCM]) nested in coarse resolution global model have been used for predicting wintertime seasonal precipitation over north India. Evaluation of results revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. It was found that quantile mapping (QM) based bias correction of the RegCM products has better skill compared to the CCA‐based statistical downscaling. Area average precipitation percentage departure (%) for T80 (in grey bars), QM‐based bias correction method applied on T80 (T80_QM, in cyan bar), CCA‐based statistical downscaling (in coral bars), QM‐based bias correction method applied on CCA (CCA_QM, in dark orchid bar), dynamical downscaling based RegCM (RCM, in sky blue bar), QM‐based bias‐corrected method for downscaled RCM (in green bars) and the observed departure (in black bars) for years (a) 2008 and (b) 2009, respectively. The main aim of the present study is to analyse the capabilities of two downscaling approaches (statistical and dynamical) in predicting wintertime seasonal precipitation over north India. For this purpose, a canonical correlation analysis (CCA) based statistical downscaling approach and dynamical downscaling approach (at 30 km) with an optimized configuration of the regional climate model (RegCM) nested in coarse resolution global spectral model have been used for a period of 28 years (1982–2009). For CCA, nine predictors (precipitation, zonal and meridional winds at 850 and 200 hPa, temperature at 200 hPa and sea surface temperatures) over three different domains were selected. The predictors were chosen based on the statistically significant teleconnection maps and physically based relationships between precipitation over the study region and meteorological variables. The validation revealed that both the downscaling approaches provided improved precipitation forecasts compared to the global model. Reasons for improved prediction by downscaling techniques have been examined. The improvement mainly comes due to better representation of orography, westerly moisture transport and vertical pressure velocity in the regional climate model. Furthermore, two bias correction methods namely quantile mapping (QM) and mean bias‐remove (MBR) have been applied on downscaled RegCM, statistically downscaled CCA as well as the global model products. It was found that when the QM‐based bias correction is applied on dynamically downscaled RegCM products, it has better skill in predicting wintertime precipitation over the study region compared to the CCA‐based statistical downscaling. Overall, the results indicate that the QM‐based bias‐corrected downscaled RegCM model is a useful tool for wintertime seasonal‐scale precipitation prediction over north India.

Description: The Indo–Gangetic foreland basin has some of the highest rates of groundwater extraction in the world, focused in the states of Punjab and Haryana in northwest India. Any assessment of the effects of extraction on groundwater variation requires understanding of the geometry and sedimentary architecture of the alluvial aquifers, which in turn are set by their geomorphic and depositional setting. To assess the overall architecture of the aquifer system, we used satellite imagery and digital elevation models to map the geomorphology of the Sutlej and Yamuna fan systems, while aquifer geometry was assessed using 243 wells that extend to ∼200 m depth. Aquifers formed by sandy-channel bodies in the subsurface of the Sutlej and Yamuna fans have a median thickness of 7 and 6 m, respectively, and follow heavy-tailed thickness distributions. These distributions along with evidence of persistence in aquifer fractions as determined from compensation analysis, indicate persistent reoccupation of channel positions, and suggest that the major aquifers consist of stacked, multi-storied channel bodies. The percentage of aquifer material in individual boreholes decreases down-fan, although the exponent on the aquifer-body thickness distribution remains similar, indicating that the total number of aquifer bodies decrease down-fan but that individual bodies do not thin appreciably, particularly on the Yamuna fan. The interfan area and the fan-marginal zone have thinner aquifers and a lower proportion of aquifer material, even in proximal locations. We conclude that geomorphic setting provides a first-order control on the thickness, geometry, and stacking pattern of aquifer bodies across this critical region.

Description: Ship-borne observations of spectral aerosol optical depth (AOD) have been carried out over the entire Bay of Bengal (BoB) as part of the W-ICARB cruise campaign during the period 27 December 2008–30 January 2009. The results reveal a pronounced temporal and spatial variability in the optical characteristics of aerosols mainly due to anthropogenic emissions and their dispersion controlled by local meteorology. The highest aerosol amount, with mean AOD500〉0.4, being even above 1.0 on specific days, is found close to the coastal regions in the western and northern parts of BoB. In these regions the Ångström exponent is also found to be high (~1.2–1.25) indicating transport of strong anthropogenic emissions from continental regions, while very high AOD500 (0.39±0.07) and α380–870 values (1.27±0.09) are found over the eastern BoB. Except from the large α380–870 values, an indication of strong fine-mode dominance is also observed from the AOD curvature, which is negative in the vast majority of the cases, suggesting dominance of an anthropogenic-pollution aerosol type. On the other hand, clean maritime conditions are rather rare over the region, while the aerosol types are further examined through a classification scheme based on the relationship between α and dα. It was found that even for the same α values the fine-mode dominance is larger for higher AODs showing the strong continental influence over the marine environment of BoB. Furthermore, there is also an evidence of aerosol-size growth under more turbid conditions indicative of coagulation and/or humidification over specific BoB regions. The results obtained using OPAC model show significant fraction of soot aerosols (~6 %–8 %) over the eastern and northwestern BoB, while coarse-mode sea salt particles are found to dominate in the southern parts of BoB.

Description: Aerosol particles can contribute to the Arctic amplification (AA) by direct and indirect radiative effects. Specifically, black carbon (BC) in the atmosphere, and when deposited on snow and sea ice, has a positive warming effect on the top-of-atmosphere (TOA) radiation balance during the polar day. Current climate models, however, are still struggling to reproduce Arctic aerosol conditions. We present an evaluation study with the global aerosol-climate model ECHAM6.3-HAM2.3 to examine emission-related uncertainties in the BC distribution and the direct radiative effect of BC. The model results are comprehensively compared against the latest ground and airborne aerosol observations for the period 2005–2017, with a focus on BC. Four different setups of air pollution emissions are tested. The simulations in general match well with the observed amount and temporal variability in near-surface BC in the Arctic. Using actual daily instead of fixed biomass burning emissions is crucial for reproducing individual pollution events but has only a small influence on the seasonal cycle of BC. Compared with commonly used fixed anthropogenic emissions for the year 2000, an up-to-date inventory with transient air pollution emissions results in up to a 30 % higher annual BC burden locally. This causes a higher annual mean all-sky net direct radiative effect of BC of over 0.1 W m−2 at the top of the atmosphere over the Arctic region (60–90∘ N), being locally more than 0.2 W m−2 over the eastern Arctic Ocean. We estimate BC in the Arctic as leading to an annual net gain of 0.5 W m−2 averaged over the Arctic region but to a local gain of up to 0.8 W m−2 by the direct radiative effect of atmospheric BC plus the effect by the BC-in-snow albedo reduction. Long-range transport is identified as one of the main sources of uncertainties for ECHAM6.3-HAM2.3, leading to an overestimation of BC in atmospheric layers above 500 hPa, especially in summer. This is related to a misrepresentation in wet removal in one identified case at least, which was observed during the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) summer aircraft campaign. Overall, the current model version has significantly improved since previous intercomparison studies and now performs better than the multi-model average in the Aerosol Comparisons between Observation and Models (AEROCOM) initiative in terms of the spatial and temporal distribution of Arctic BC.