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India has good reasons to be concerned about climate change as it could adversely affect the achievement of vital national development goals related to socio-economic development, human welfare, health, energy availability and use, and infrastructure. The paper attempts to develop a framework for integrated impact assessment and adaptation responses, using a recently built railroad coastal infrastructure asset in India as an example. The framework links climate change variables — temperature, rainfall, sea level rise, extreme events, and other secondary variables — and sustainable development variables — technology, institutions, economic, and other policies. The study indicates that sustainable development variables generally reduce the adverse impacts on the system due to climate change alone, except when they are inadequately applied. The paper concludes that development is a vital variable for integrated impact assessment. Well crafted developmental policies could result in a less-GHG intensive future, enhanced adaptive capacities of communities and systems, and lower impacts due to climate change. [ABSTRACT FROM AUTHOR]

This paper examines the robustness of the long-run, cointegrating, relationship between global temperatures and radiative forcing. It is found that the temperature sensitivity to a doubling of radiative forcing is of the order of 2 ± 1°C. This result is robust across the sample period of 1850 to 2000, thus providing further confirmation of the quantitative impact of radiative forcing and, in particular, CO2 forcing, on temperatures. [ABSTRACT FROM AUTHOR]

Meteorological and oceanographic data from ships of opportunity are the largest contributor to the world’s ocean surface database and thus are extensively used to estimate the change in climatic properties over the world’s oceans during the previous 150 years. The importance of these data for climate change studies underscores the need to fully understand the error associated with averages of these data. The sampling error problem is especially acute for ship data due to the fact that ships are moving platforms and, thus, report observations from constantly varying locations with time. This paper develops a theoretical framework for assessing the averaged sampling error associated with monthly, 1°×1° latitude-longitude box averaged ship data. It should be noted that the time-space distribution of ships within the averaging domain strongly affects the sampling error. This is shown in our derivation. The framework developed here can be used to improve upon existing methods for estimating the sampling error associated with three-dimensional box averages of meteorological and oceanographic data obtained from ship records. The framework is complimentary to existing methods of assessing biases and random error due to instrumentation, recording, etc. It is demonstrated mathematically that the uncertainty due to incomplete sampling is primarily a trade off between of the number of observations and their relative locations within the box as well as the inherent time-space correlation structure of the variable of interest. This work differs from other studies in that the three-dimensional interdependence of data is taken into account in deriving an expression for the sampling error. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate current knowledge and uncertainties associated with the impact of increasing greenhouse gas and aerosol concentrations on the West African monsoon. For this purpose, coupled and time-slice simulations are used. A global measure of the monsoon changes is defined in order to avoid regional biases and to try and obtain significant results. The position and width of the monsoon in latitude are the main focuses. There is almost no agreement between the Coupled General Circulation models from the Coupled models Inter-Comparison project—Phase II in regard to the impact of climate change on the monsoon. Moreover, very simple discriminations between the models seem inappropriate to get a better signal. The role of the different forcings in time-slice simulations is then investigated. The sea surface temperature (SST) and particularly the pattern of the SST are shown to be the most important forcing. This accounts for the diversity of the results either from the coupled or the forced simulations with different SST changes. With a fixed SST, but of a smaller magnitude in AMJ, there are still uncertainties, coming first from the Atmospheric General Circulation models and the way they balance greenhouse gas and global SST increase. Finally the uncertainty due to the Land Surface models (LSMs) is not negligible. The greenhouse gas and the LSMs are shown to have more impact in August, when the monsoon is at its highest latitude on the continent. [ABSTRACT FROM AUTHOR]