Regional Climate Projections

Satellite view of the Himalayas The GCMs also most likely have significant problems describing the precipitation over Tibet, due to large small-scale spatial geographical features and distorted albedo feedbacks. The net effect may therefore be an increase in the rainfall associated with the Monsoon.

The Asian climate is also influenced by ENSO, but uncertainties in how ENSO will be affected by AGW cascades to the Asian climate.

There are, however, indications that heat waves will become more frequent and more intense. Furthermore, the MMD models suggest a decrease in the December-February precipitation and an increase in the remaining months. The models also project more intense rainfall over large areas in the future.

North America.

North America The general picture is that the GCMs provide realistic representation of the mean SLP and T(2m) over North America, but that they tend to over-estimate the rainfall over the western and northern parts.

The MMD results project strongest winter-time warming in the north and summer-time warming in the southwest USA. The annual mean precipitation is, according to AR4, likely to increase in the north and decrease in southwest.

A stronger warming over land than over sea may possibly affect the sub-tropical high-pressure system off the west coast, but there are large knowledge gaps associated with this aspect.

The projections are associated with a number of uncertainties concerning dynamical features such as ENSO, the storm track system (the GCMs indicate a pole-ward shift, an increase in the number of strong cyclones and a reduction in the medium strength storms poleward of 70N & Canada), the polar vortex (the GCMs suggest an intensification), the Great Plains low-level jet, the North American Monsoon system, ocean circulation and the future evolution in the snow-extent and sea-ice. Some of these phenomena are not well-represented by the GCMs, as their spatial resolution is too coarse. The same goes for tropical cyclones (hurricanes), for which the frequency, intensity and track-statistics remain uncertain.

A number of RCM-based studies provide further regional details (North American Regional Climate Change Assessment Program). Despite improvements, AR4 also states that RCM simulations are sensitive to the choice of domain, the parameterisation of moist convection processes (representation of clouds and precipitation), and that there are biases in the RCM results when GCM are provided as boundary conditions rather than re-analyses.

Furthermore, most RCM simulations have been made for time slices that are too short to provide a proper statistical sample for studying natural variability. There are no references to ESD for North America in the AR4 chapter except for in the discussion on the projections for the snow.

Latin America.

Latin America AR4 states that the annual precipitation is likely to decrease in most of Central America and southern Andes. However, there may be pronounced local effects from the mountains, and changes in the atmospheric circulation may result in large local variations.

The projections of the seasonal mean rainfall statistics for eg the Amazon forest are highly uncertain. One of the greatest sources of uncertainty is associated with how the character of ENSO may change, and there are large inter-model differences within the MMD as to how ENSO will be affected by AGW. Furthermore, most GCMs have small signal-to-noise ratio over most of Amazonia. Feedbacks from land use and land cover (including carbon cycle and dynamic vegetation) are not well-represented in most of the models.

Tropical cyclones also increase the uncertainty for Central America, and in some regions the tropical storms can contribute a significant fraction to the rainfall statistics. However, there has been little research on climate extremes and projection of these in Latin America.

According to AR4, deficiencies in the MMD models have a serious impact on the representation of local low-latitude climates, and the models tend to simulate ITCZs which are too weak and displaced too far to the south. Hence the rainfall over the Amazon basin tends to be under-estimated in the GCMs, and conversely over-estimated along the Andes and northeastern Brazil.

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