Mineral dust increases the habitability of terrestrial planets but confounds biomarker detection.

Identification of habitable planets beyond our solar system is a key goal of current and future space missions. Yet habitability depends not only on the stellar irradiance, but equally on constituent parts of the planetary atmosphere. Here we show, for the first time, that radiatively active mineral dust will have a significant impact on the habitability of Earth-like exoplanets. On tidally-locked planets, dust cools the day-side and warms the night-side, significantly widening the habitable zone. Independent of orbital configuration, we suggest that airborne dust can postpone planetary water loss at the inner edge of the habitable zone, through a feedback involving decreasing ocean coverage and increased dust loading. The inclusion of dust significantly obscures key biomarker gases (e.g. ozone, methane) in simulated transmission spectra, implying an important influence on the interpretation of observations. We demonstrate that future observational and theoretical studies of terrestrial exoplanets must consider the effect of dust.

Model complexity versus ensemble size: allocating resources for climate prediction.

A perennial question in modern weather forecasting and climate prediction is whether to invest resources in more complex numerical models or in larger ensembles of simulations. If this question is to be addressed quantitatively, then information is needed about how changes in model complexity and ensemble size will affect predictive performance. Information about the effects of ensemble size is often available, but information about the effects of model complexity is much rarer. An illustration is provided of the sort of analysis that might be conducted for the simplified case in which model complexity is judged in terms of grid resolution and ensemble members are constructed only by perturbing their initial conditions. The effects of resolution and ensemble size on the performance of climate simulations are described with a simple mathematical model, which is then used to define an optimal allocation of computational resources for a range of hypothetical prediction problems. The optimal resolution and ensemble size both increase with available resources, but their respective rates of increase depend on the values of two parameters that can be determined from a small number of simulations. The potential for such analyses to guide future investment decisions in climate prediction is discussed.

Detection of human influence on twentieth-century precipitation trends.

Human influence on climate has been detected in surface air temperature, sea level pressure, free atmospheric temperature, tropopause height and ocean heat content. Human-induced changes have not, however, previously been detected in precipitation at the global scale, partly because changes in precipitation in different regions cancel each other out and thereby reduce the strength of the global average signal. Models suggest that anthropogenic forcing should have caused a small increase in global mean precipitation and a latitudinal redistribution of precipitation, increasing precipitation at high latitudes, decreasing precipitation at sub-tropical latitudes, and possibly changing the distribution of precipitation within the tropics by shifting the position of the Intertropical Convergence Zone. Here we compare observed changes in land precipitation during the twentieth century averaged over latitudinal bands with changes simulated by fourteen climate models. We show that anthropogenic forcing has had a detectable influence on observed changes in average precipitation within latitudinal bands, and that these changes cannot be explained by internal climate variability or natural forcing. We estimate that anthropogenic forcing contributed significantly to observed increases in precipitation in the Northern Hemisphere mid-latitudes, drying in the Northern Hemisphere subtropics and tropics, and moistening in the Southern Hemisphere subtropics and deep tropics. The observed changes, which are larger than estimated from model simulations, may have already had significant effects on ecosystems, agriculture and human health in regions that are sensitive to changes in precipitation, such as the Sahel.

Aspects of climate change prediction relevant to crop productivity.

Projected changes in surface climate are reviewed at a range of temporal scales, with an emphasis on tropical northern Africa--a region considered to be particularly vulnerable to climate change. Noting the key aspects of 'weather' affecting crop yield, we then consider relevant and projected change using output from a range of state of the art global climate models (GCMs), and for different future emission scenarios. The outputs from the models reveal significant inter-model variation in the change expected by the end of the twenty-first century for even the lowest IPCC emission scenario. We provide a set of recommendations on future model diagnostics, configurations and ease of use to close further the gap between GCMs and smaller-scale crop models. This has the potential to empower countries to make their own assessments of vulnerability to climate change induced periods of food scarcity.