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Many atmospheric constituents besides carbon dioxide (CO(2)) contribute to global warming, and it is common to compare their influence on climate in terms of radiative forcing, which measures their impact on the planetary energy budget. A number of recent studies have shown that many radiatively active constituents also have important impacts on the physiological functioning of ecosystems, and thus the 'ecosystem services' that humankind relies upon. CO(2) increases have most probably increased river runoff and had generally positive impacts on plant growth where nutrients are non-limiting, whereas increases in near-surface ozone (O(3)) are very detrimental to plant productivity. Atmospheric aerosols increase the fraction of surface diffuse light, which is beneficial for plant growth. To illustrate these differences, we present the impact on net primary productivity and runoff of higher CO(2), higher near-surface O(3), and lower sulphate aerosols, and for equivalent changes in radiative forcing. We compare this with the impact of climate change alone, arising, for example, from a physiologically inactive gas such as methane (CH(4)). For equivalent levels of change in radiative forcing, we show that the combined climate and physiological impacts of these individual agents vary markedly and in some cases actually differ in sign. This study highlights the need to develop more informative metrics of the impact of changing atmospheric constituents that go beyond simple radiative forcing., (© 2011 The Royal Society)

The future of tropical forests has become one of the iconic issues in climate-change science. A number of studies that have explored this subject have tended to focus on the output from one or a few climate models, which work at low spatial resolution, whereas society and conservation-relevant assessment of potential impacts requires a finer scale. This study focuses on the role of climate on the current and future distribution of humid tropical forests (HTFs). We first characterize their contemporary climatological niche using annual rainfall and maximum climatological water stress, which also adequately describe the current distribution of other biomes within the tropics. As a first-order approximation of the potential extent of HTFs in future climate regimes defined by global warming of 2°C and 4°C, we investigate changes in the niche through a combination of climate-change anomaly patterns and higher resolution (5 km) maps of current climatology. The climate anomalies are derived using data from 17 coupled Atmosphere-Ocean General Circulation Models (AOGCMs) used in the Fourth Assessment of the Intergovernmental Panel for Climate Change. Our results confirm some risk of forest retreat, especially in eastern Amazonia, Central America and parts of Africa, but also indicate a potential for expansion in other regions, for example around the Congo Basin. The finer spatial scale enabled the depiction of potential resilient and vulnerable zones with practically useful detail. We further refine these estimates by considering the impact of new environmental regimes on plant water demand using the UK Met Office land-surface scheme (of the HadCM3 AOGCM). The CO(2)-related reduction in plant water demand lowers the risk of die-back and can lead to possible niche expansion in many regions. The analysis presented here focuses primarily on hydrological determinants of HTF extent. We conclude by discussing the role of other factors, notably the physiological effects of higher temperature.

Predictions of future climate change require complex computer models of the climate system to represent the full range of processes and interactions that influence climate. The Met Office Hadley Centre uses 'families' of models as part of the Met Office Unified Model Framework to address different classes of problems. The HadGEM family is a suite of state-of-the-art global environment models that are used to reduce uncertainty and represent and predict complex feedbacks. The HadCM3 family is a suite of well established but cheaper models that are used for multiple simulations, for example, to quantify uncertainty or to test the impact of multiple emissions scenarios.