Evaluation of Amip-Type Atmospheric Fields as Forcing For Mediterranean Sea and Global Ocean Reanalyses

Oceanic reanalyses are powerful products to reconstruct the historical 3D-state of the ocean and related circulation. At present a challenge is to have oceanic reanalyses covering the whole 20th century. This study describes the exercise of comparing available datasets to force Mediterranean Sea and global oceanic reanalyses from 1901 to present. In particular, we compared available atmospheric reanalyses with a set of experiments performed with an atmospheric general circulation model where sea surface temperature (SST) and sea-ice concen- tration are prescribed. These types of experiments have the advantage of covering long time records, at least for the period for which global SST is available, and they can be performed at relatively high horizontal resolutions, a very important requisite for regional oceanic re- analyses. However, they are limited by the intrinsic model biases in representing the mean atmospheric state and its variability. In this study, we show that, within some limits, the atmospheric model performance in representing the basic variables needed for the bulk-formulae to force oceanic data assimilation systems can be comparable to the differences among available atmospheric reanalyses. In the case of the Mediterranean Sea the high horizontal resolution of the set of SST-prescribed experiments combined with their good performance in rep- resenting the surface winds in the area made them the most appropriate atmospheric forcing. On the other hand, in the case of the global ocean, atmospheric reanalyses have been proven to be still preferable due to the better representation of spatial and temporal variability of surface winds and radiative fluxes. Because of their intrinsic limitations AMIP experiments cannot provide atmospheric fields alterna- tive to atmospheric reanalyses. Nevertheless, here we show how in the specific case of the Mediterranean Sea, they can be of use, if not preferable, to available atmospheric reanalyses

Climate change projections for sustainable and healthy cities

The ambition to develop sustainable and healthy cities requires city-specific policy and practice founded on a multidisciplinary evidence base, including projections of human-induced climate change. A cascade of climate models of increasing complexity and resolution is reviewed, which provides the basis for constructing climate projections—from global climate models with a typical horizontal resolution of a few hundred kilometres, through regional climate models at 12–50 km to convection-permitting models at 1 km resolution that permit the representation of urban induced climates. Different approaches to modelling the urban heat island (UHI) are also reviewed—focusing on how climate model outputs can be adjusted and coupled with urban canopy models to better represent UHI intensity, its impacts and variability. The latter can be due to changes induced by urbanisation or to climate change itself. City interventions such as greater use of green infrastructure also have an effect on the UHI and can help to reduce adverse health impacts such as heat stress and the mortality associated with increasing heat. Examples for the Complex Urban Systems for Sustainability and Health (CUSSH) partner cities of London, Rennes, Kisumu, Nairobi, Beijing and Ningbo illustrate how cities could potentially make use of more detailed models and projections to develop and evaluate policies and practices targeted at their specific environmental and health priorities. Practice relevance Large-scale climate projections for the coming decades show robust trends in rising air temperatures, including more warm days and nights, and longer/more intense warm spells and heatwaves. This paper describes how more complex and higher resolution regional climate and urban canopy models can be combined with the aim of better understanding and quantifying how these larger scale patterns of change may be modified at the city or finer scale. These modifications may arise due to urbanisation and effects such as the UHI, as well as city interventions such as the greater use of grey and green infrastructures. There is potential danger in generalising from one city to another—under certain conditions some cities may experience an urban cool island, or little future intensification of the UHI, for example. City-specific, tailored climate projections combined with tailored health impact models contribute to an evidence base that supports built environment professionals, urban planners and policymakers to ensure designs for buildings and urban areas are fit for future climates

Summer Monsoon Rainfall Variability Over Maharashtra, India

This paper presents the results of an analysis of the daily rainfall of 329 rain gauge stations data over Maharashtra, a state in India, during the summer monsoon season, June to September, for the 11 year period from 1998 to 2008. Mesoscale analysis of the daily rainfall data is performed by converting the station rainfall data into gridded format with 15 km resolution. Various statistics have been carried out over 35 districts of four meteorological subdivisions of the Maharashtra state to understand the spatio-temporal variability of rainfall. Variation of monthly mean rainfall for the four monsoon months and a season as whole is analyzed for different rainfall statistics such as mean rainfall, rainfall variability, rainy days, maximum daily rainfall and classification of rainy days. Seasonal rainfall is maximum over the Konkan region followed by the eastern Vidharbha region whereas Madhya Maharashtra as a rain shadow region receives less rainfall. The rainfall is highly variable over all of Maharashtra with the coefficient of variability of the daily rainfall varying between 100 and 300%. Seasonal distribution of the number of rainy days shows 90–100 over southern Konkan, 80–90 over northern Konkan, 50–60 over eastern Vidharbha, and the southeast Madhya Maharashtra has the lowest number of about 15–20 rainy days. The highest values of maximum daily rainfall are located over the Sindhudurg, Ratnagiri, Raigadh, Mumbai and Thane districts of the Konkan region followed by that over eastern Vidharbha. The rainfall data have been divided into three categories (moderate rainfall, heavy rainfall and extreme heavy rainfall) based upon seven categories used by the India Meteorological Department. Heavy rainfall zones lie over the southern Konkan region, whereas extreme heavy rainfalls occur over northern latitudes. The data used in this study is having high resolution and district wise analysis over Maharashtra state is extremely beneficial

On improving the ability of a high-resolution atmospheric general circulation model for dynamical seasonal prediction of the extreme seasons of the Indian summer monsoon

The paper is devoted to examine the ability of a high-resolution National Center for Environmental Prediction (NCEP) T170/L42 Atmospheric General Circulation Model (AGCM), for exploring its utility for long-range dynamical prediction of seasonal Indian summer monsoon rainfall (ISMR) based on 5-members ensemble for the hindcast mode 20-year (1985-2004) period with observed global sea surface temperatures (SSTs) as boundary condition and 6-year (2005-2010) period in the forecast-mode with NCEP Coupled Forecast System (CFS) SSTs as boundary condition. ISMR simulations are examined on five day (pentad) rainfall average basis. It is shown that the model simulated ISMR, based on 5-members ensemble average basis had limited skill in simulating extreme ISMR seasons (drought/excess ISMR). However, if the ensemble averaging is restricted to similar ensemble members either in the overall run of pentad-wise below (B) and above (A) normal rainfall events, as determined by the departure for the threshold value given by coefficient of variability (CV) for the respective pentads based on IMD observed climatology, or during the season as a whole on the basis of percentage anomaly of ISMR from the seasonal climatology, the foreshadowing of drought/excess monsoon seasons improved considerably. Our strategy of improving dynamical seasonal prediction of ISMR was based on the premise that the intra-seasonal variability (ISV) and intra-annual variability (IAV) are intimately connected and characterized by large scale perturbations westward moving (10-20 day) and northward moving (30-60 day) modes of monsoon ISV during the summer monsoon season. As such the cumulative excess of B events in the simulated season would correspond to drought season and vice-versa. The paper also examines El Niño-Monsoon connections of the simulated ISMR series and they appear to have improved considerably in the proposed methodology. This strategy was particularly found to improve for foreshadowing of droughts. Based on results of the study a strategy is proposed for using the matched signal for simulated ISMR based on excess B over A events and vice-versa for drought or excess ISMR category. The probability distribution for the forecast seasonal ISMR on category basis is also proposed to be based on the relative ratio of similar ensemble members and total ensembles on percentage basis. The paper also discusses that extreme monsoon season are produced by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modes in a combined manner and hence stresses to improve prediction of IOD mode in ocean-atmosphere coupled model just as it has happened for the prediction ENSO mode six to nine months in advance

Influence of Land Use Land Cover on Cyclone Track Prediction – A Study During Aila Cyclone

Land-surface processes are one of the important drivers for weather and climate systems over the tropics. Realistic representation of land surface processes in mesoscale models over the region will help accurate simulation of numerical forecasts. The present study examines the influence of Land Use/ Land Cover Change (LULC) on the forecasting of cyclone intensity and track prediction using Mesoscale Model (MM5). Gridded land use/land cover data set over the Indian region compatible with the MM5 model were generated from Indian Remote Sensing Satellite (IRS-P6). Advanced Wide Field Sensor (AWiFS) for the year 2007-2008. A case study of simulation of ‘Aila’ cyclone has been considered to see the impact of these two sets of LULC data with the use of MM5 model. Results of the study indicated that incorporation of current land use/land cover data sets in mesoscale model provides better forecasting of cyclonic track

Mesoscale characteristics and prediction of an unusual extreme heavy precipitation event over India using a high resolution mesoscale model

Numerical prediction experiments using a high resolution mesoscale model NCAR MM5 were performed to simulate an unusual extreme precipitation event that occurred over west coast region of India on 26 July 2005. During this event, unprecedented precipitation of 90–100 cm was recorded over northeast parts of Mumbai City, India causing enormous losses while southern parts received only 10 cm. Model prediction with analysis nudging for 12 h followed by 36 h of integration produced the best simulation with 55 cm of precipitation in 24 h and with the location over north Mumbai agreeing with the observations. Model diagnostics of the vorticity, divergence, vertical velocity and lower tropospheric moisture convergence show the mesoscale characteristics of the convective system with a horizontal extent of 50 km2 and of a sudden cloud burst for 3–6 h followed by few shorter rain spells. The model simulates the veering of the wind with height due to warm air advection to favor convection but a moist layer at lower levels capped by dry air inhibited convection. The simulated circulation features indicate that a mesoscale convective system formed in the monsoon westerlies due to passage of a synoptic disturbance across the east coast strengthening the monsoon flow, and dry air incursion at middle levels suppressed convection and contributing to increase of potential instability at lower levels. All this helped the sudden initiation of deep convection and cloud burst with heavy precipitation rate. Stretching term associated with vorticity contributes most for the increase of cyclonic vorticity indicating the interaction of convection with mesoscale circulation

Influence of Land Use Land Cover on Cyclone Track Prediction – A Study During Aila Cyclone

Land-surface processes are one of the important drivers for weather and climate systems over the tropics. Realistic representation of land surface processes in mesoscale models over the region will help accurate simulation of numerical forecasts. The present study examines the influence of Land Use/ Land Cover Change (LULC) on the forecasting of cyclone intensity and track prediction using Mesoscale Model (MM5). Gridded land use/land cover data set over the Indian region compatible with the MM5 model were generated from Indian Remote Sensing Satellite (IRS-P6). Advanced Wide Field Sensor (AWiFS) for the year 2007-2008. A case study of simulation of ‘Aila’ cyclone has been considered to see the impact of these two sets of LULC data with the use of MM5 model. Results of the study indicated that incorporation of current land use/land cover data sets in mesoscale model provides better forecasting of cyclonic track

Regional scale prediction of the onset phase of the Indian southwest monsoon with a high-resolution atmospheric model

A nonhydrostatic atmospheric model with a resolution of 30 km is used to make predictions of the rainfall during the onset phase of the southwest monsoon (SWM) of 2003. Model predictions of the pentad rainfall time series indicate good predictions up to lead time of 5 days. The correlation coefficients (CCs) between the model-predicted and observed rainfall at different locations, representative of the five homogeneous regions of SWM rainfall, over the Indian subcontinent show correlations significant at 90% level up to 5 days lead time with values above 0.32. The spatial distribution of the model-predicted pentad rainfall show an advancement of the Arabian Sea and the Bay of Bengal branches of SWM over the Indian subcontinent up to 5 days lead time. Copyright © 2008 Royal Meteorological Societ

An assessment of cumulus parameterization schemes in the short range prediction of rainfall during the onset phase of the Indian Southwest Monsoon using MM5 Model

The performance of cumulus parameterization schemes in the short range prediction of rainfall during onset phase of the Indian Southwest Monsoon (ISM) was evaluated using Fifth-Generation Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5). MM5 model was used to predict rainfall at 30 km resolution up to 72 h over the Indian subcontinent for each day during the period 1–30 June 2002, which corresponds to the onset phase of the ISM. Experiments were performed with 5 different cumulus parameterization schemes of Anthes–Kuo (AK), Grell (GR), Betts–Miller (BM), Kain–Fritsch (KF) and Kain–Fritsch2 (KF2). Rainfall prediction assessment was made over five zones through comparison with corresponding APHRODITE gridded precipitation data and for selected location with station observations by analyzing the statistical parameters of correlation coefficient, mean absolute error and Hanssen–Kuipers score. Monthly mean zone-wise rainfall was well predicted by GR and AK schemes up to 48 hours and slight overestimation beyond. GR scheme predicted higher rainfall over west coast, central parts of India and low rainfall over southeast peninsula. BM and KF schemes showed overestimation with prediction of rainfall over dry southeast peninsula. All the schemes underestimated the coefficient of variability (CV) over all the five zones. AK and GR schemes had the mean and CV nearer to the APHRODITE observations, with AK scheme slightly better than GR scheme over Zones 1, 2 and 3 while GR scheme had the best agreement over Zones 4 and 5. GR scheme had also shown higher CC values and lower MAE over most of the zones up to 72 h, while BM had the least predictability with lower CC and HK scores and higher MAE over most of the zones. Over Western Ghats, the uncertainty limits could be higher than shown due to dominant heavy rains. Of the ten stations selected for verification, GR scheme had shown better prediction with significant positive CC values at nine of the ten stations and consistently lower MAE values and higher HK scores. Further analysis has shown that GR scheme predicted higher grid-scale and nighttime rainfall agreeing with earlier studies concerning monsoon rainfall. All other schemes predicted the features contrarily with higher convective and daytime rainfall. GR scheme alone was found to have provided the best prediction considering the mean monthly, daily zone-wise and station rainfall predictions. The present study concludes that GR cumulus parameterization scheme is the most suitable at 30 km resolution

Dynamical simulation of Indian summer monsoon circulation, rainfall and its interannual variability using a high resolution atmospheric general circulation model

This paper discusses the simulations of Indian summer monsoon (ISM) using a high-resolution National Center for Environmental Prediction (NCEP) T170/L42 model for a 20-year period (1985–2004) with observed Sea Surface Temperature (SSTs) as boundary conditions and using five initial conditions in the first week of May. Good agreement is found between the observed and simulated climatologies. Interannual variability (IAV) of the ISM rainfall as simulated in individual ensemble members and as provided by ensemble average shows that the two series are found to agree well; however, the simulation of the actual observed year-to-year variability is poor. The model simulations do not show much skill in the simulation of drought and excess monsoon seasons. One aspect which has emerged from the study is that where dynamical seasonal prediction has specific base for the large areal and temporal averages, the technique is not to be stretched for application on short areal scale such as that of a cluster of a few grid point. Monsoon onset over Kerala (MOK) coast of India and advance from Kerala coast to northwest India is discussed based on ensemble average and individual ensemble member basis. It is suggested that the model is capable of realistically simulating these processes, particularly if ensemble average is used, as the intermember spread in the ensemble members is large. In short, the high-resolution model appears to provide better climatology and its magnitude of IAV, which compares favourably with observations, although year-to-year matching of the observed and simulated seasonal/monthly rainfall totals for India as a whole is not good. Copyright © 2010 Royal Meteorological Societ