Towards improving the framework for probabilistic forecast evaluation

The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are contrasted and then used to interpret decadal probability hindcasts of global mean temperature. The Continuous Ranked Probability Score (CRPS), Proper Linear (PL) score, and IJ Good’s logarithmic score (also referred to as Ignorance) are compared; although information from all three may be useful, the logarithmic score has an immediate interpretation and is not insensitive to forecast busts. Neither CRPS nor PL is local; this is shown to produce counter intuitive evaluations by CRPS. Benchmark forecasts from empirical models like Dynamic Climatology place the scores in context. Comparing scores for forecast systems based on physical models (in this case HadCM3, from the CMIP5 decadal archive) against such benchmarks is more informative than internal comparison systems based on similar physical simulation models with each other. It is shown that a forecast system based on HadCM3 out performs Dynamic Climatology in decadal global mean temperature hindcasts; Dynamic Climatology previously outperformed a forecast system based upon HadGEM2 and reasons for these results are suggested. Forecasts of aggregate data (5-year means of global mean temperature) are, of course, narrower than forecasts of annual averages due to the suppression of variance; while the average “distance” between the forecasts and a target may be expected to decrease, little if any discernible improvement in probabilistic skill is achieved.

The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.

The effectiveness of protected areas in the conservation of species with changing geographical ranges

A cornerstone of conservation is the designation and management of protected areas (PAs): locations often under conservation management containing species of conservation concern, where some development and other detrimental influences are prevented or mitigated. However, the value of PAs for conserving biodiversity in the long term has been questioned given that species are changing their distributions in response to climatic change. There is a concern that PAs may become climatically unsuitable for those species that they were designated to protect, and may not be located appropriately to receive newly-colonizing species for which the climate is improving. In the present study, we analyze fine-scale distribution data from detailed resurveys of seven butterfly and 11 bird species in Great Britain aiming to examine any effect of PA designation in preventing extinctions and promoting colonizations. We found a positive effect of PA designation on species' persistence at trailing-edge warm range margins, although with a decreased magnitude at higher latitudes and altitudes. In addition, colonizations by range expanding species were more likely to occur on PAs even after altitude and latitude were taken into account. PAs will therefore remain an important strategy for conservation. The potential for PA management to mitigate the effects of climatic change for retracting species deserves further investigation.

Integrating archaeology and palaeoecology to understand Jê landscapes in southern Brazil

Around AD 1000, the southern Brazilian highlands witnessed a convergence of phenomena: climatic change, the abrupt expansion of Araucaria forest and the appearance of large pit-houses and monumental mound and enclosure complexes, which signal fundamental socio-political and ideological change amongst southern proto-Jê (SPJ) groups. These developments raise intriguing questions regarding the relationships between people, vegetation and climate over the last 2000 years.

The distributional and nutritional impacts and mitigation potential of emission-based food taxes in the UK

Agriculture and food production are responsible for a substantial proportion of greenhouse gas emissions. An emission based food tax has been proposed as one option to reduce food related emissions. This study introduces a method to measure the impacts of emission based food taxes at a household level which involves the use of data augmentation to account for the fact that the data record purchases and not consumption. The method is applied to determine the distributional and nutritional impacts of an emission based food tax across socio-economic classes in the UK. We find that a tax of £2.841/tCO2e on all foods would reduce food related emissions by 6.3% and a tax on foods with above average levels of emissions would reduce emissions by 4.3%. The tax burden falls disproportionately on households in the lowest socio-economic class because they tend to spend a larger proportion of their food expenditure on emission intensive foods and because they buy cheaper products and therefore experience relatively larger price increases.