Increasing risk of Amazonian drought due to decreasing aerosol pollution

The Amazon rainforest plays a crucial role in the climate system, helping to drive atmospheric circulations in the tropics by absorbing energy and recycling about half of the rainfall that falls on it. This region (Amazonia) is also estimated to contain about one-tenth of the total carbon stored in land ecosystems, and to account for one-tenth of global, net primary productivity. The resilience of the forest to the combined pressures of deforestation and global warming is therefore of great concern, especially as some general circulation models (GCMs) predict a severe drying of Amazonia in the twenty-first century. Here we analyse these climate projections with reference to the 2005 drought in western Amazonia, which was associated with unusually warm North Atlantic sea surface temperatures (SSTs). We show that reduction of dry-season (July–October) rainfall in western Amazonia correlates well with an index of the north–south SST gradient across the equatorial Atlantic (the 'Atlantic N–S gradient'). Our climate model is unusual among current GCMs in that it is able to reproduce this relationship and also the observed twentieth-century multidecadal variability in the Atlantic N–S gradient, provided that the effects of aerosols are included in the model. Simulations for the twenty-first century using the same model3, 8 show a strong tendency for the SST conditions associated with the 2005 drought to become much more common, owing to continuing reductions in reflective aerosol pollution in the Northern Hemisphere

Detection of a direct carbon dioxide effect in continental river runoff records

Continental runoff has increased through the twentieth century1, 2 despite more intensive human water consumption3. Possible reasons for the increase include: climate change and variability, deforestation, solar dimming4, and direct atmospheric carbon dioxide (CO2) effects on plant transpiration5. All of these mechanisms have the potential to affect precipitation and/or evaporation and thereby modify runoff. Here we use a mechanistic land-surface model6 and optimal fingerprinting statistical techniques7 to attribute observational runoff changes1 into contributions due to these factors. The model successfully captures the climate-driven inter-annual runoff variability, but twentieth-century climate alone is insufficient to explain the runoff trends. Instead we find that the trends are consistent with a suppression of plant transpiration due to CO2-induced stomatal closure. This result will affect projections of freshwater availability, and also represents the detection of a direct CO2 effect on the functioning of the terrestrial biosphere

Impact of vegetation changes on the dynamics of the atmosphere at the Last Glacial Maximum

Much work is under way to identify and quantify the feedbacks between vegetation and climate. Palaeoclimate modelling may provide a mean to address this problem by comparing simulations with proxy data. We have performed a series of four simulations of the Last Glacial Maximum (LGM, 21,000 years ago) using the climate model HadSM3, to test the sensitivity of climate to various changes in vegetation: a global change (according to a previously discussed simulation of the LGM with HadSM3 coupled to the dynamical vegeta- tion model TRIFFID); a change only north of 35°N; a change only south of 35°N; and a variation in stomatal opening induced by the reduction in atmospheric CO2 concentration. We focus mainly on the response of temperature, precipitation, and atmosphere dynamics. The response of continental temperature and precipita- tion mainly results from regional interactions with veg- etation. In Eurasia, particularly Siberia and Tibet, the response of the biosphere substantially enhances the glacial cooling through a positive feedback loop between vegetation, temperature, and snow-cover. In central Africa, the decrease in tree fraction reduces the amount of precipitation. Stomatal opening is not seen to play a quantifiable role. The atmosphere dynamics, and more specifically the Asian summer monsoon system, are significantly altered by remote changes in vegetation: the cooling in Siberia and Tibet act in concert to shift the summer subtropical front southwards, weaken the easterly tropical jet and the momentum transport asso- ciated with it. By virtue of momentum conservation, these changes in the mid-troposphere circulation are associated with a slowing of the Asian summer monsoon surface flow. he pattern of moisture convergence is slightly altered, with moist convection weakening in the western tropical Pacific and strengthening north of Australia

Vegetation and climate variability: a GCM modelling study

Vegetation is known to interact with the other components of the climate system over a wide range of timescales. Some of these interactions are now being taken into account in models for climate prediction. This study is an attempt to describe and quantify the climate–vegetation coupling at the interannual timescale, simulated with a General Circulation Model (HadSM3) coupled to a dynamic global vegetation model (TRIFFID). Vegetation variability is generally strongest in semi-arid areas, where it is driven by precipitation variability. The impact of vegetation variability on climate is analysed by using multivariate regressions of boundary layer fluxes and properties, with respect to soil moisture and vegetation fraction. Dynamic vegetation is found to significantly increase the variance in the surface sensible and latent heat fluxes. Vegetation growth always causes evapotranspiration to increase, but its impact on sensible heat is less straightforward. The feedback of vegetation on sensible heat is positive in Australia, but negative in the Sahel and in India. The sign of the feedback depends on the competing influences, at the gridpoint scale, of the turbulent heat exchange coefficient and the surface (stomatal) water conductance, which both increase with vegetation growth. The impact of vegetation variability on boundary layer potential temperature and relative humidity are shown to be small, implying that precipitation persistence is not strongly modified by vegetation dynamics in this model. We discuss how these model results may improve our knowledge of vegetation–atmosphere interactions and help us to target future model developments

Use of atmospheric radiation measurement program data from Barrow, Alaska for evaluation and development of snow-albedo parameterizations

Snow albedo is determined from the ratio of out-going to incoming solar radiation using three years of broadband shortwave radiometer data obtained from the Barrow, Alaska, Atmospheric Radiation Measurement (ARM) site. These data are used for the evaluation of various types of snow-albedo parameterizations applied in numerical weather prediction or climate models. These snow-albedo parameterizations are based on environmental conditions (e.g., air or snow temperature), snow related characteristics (e.g., snow depth, snow age), or combinations of both. The ARM data proved to be well suited for snow-albedo evaluation purposes for a low-precipitation tundra environment. The evaluation confirms that snow-age dependent parameterizations of snow albedo work well during snowmelt, while parameterizations considering meteorological conditions often perform better during snow accumulation. Current difficulties in parameterizing snow albedo occur for long episodes of snow-event free conditions and episodes with a high frequency of snow events or strong snowfall. In a further step, the first two years of the ARM albedo dataset is used to develop a snow-albedo parameterization, and the third year’s data serves for its evaluation. This parameterization considers snow depth, wind speed, and air temperature which are found to be significant parameters for snow-albedo modeling under various conditions. Comparison of all evaluated snow-albedo parameterizations with this new parameterization shows improved snow-albedo prediction