Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe

Vector-borne diseases are among those most sensitive to climate because the ecology of vectors and the development rate of pathogens within them are highly dependent on environmental conditions. Bluetongue (BT), a recently emerged arboviral disease of ruminants in Europe, is often cited as an illustration of climate''s impact on disease emergence, although no study has yet tested this association. Here, we develop a framework to quantitatively evaluate the effects of climate on BT''s emergence in Europe by integrating high-resolution climate observations and model simulations within a mechanistic model of BT transmission risk. We demonstrate that a climate-driven model explains, in both space and time, many aspects of BT''s recent emergence and spread, including the 2006 BT outbreak in northwest Europe which occurred in the year of highest projected risk since at least 1960. Furthermore, the model provides mechanistic insight into BT''s emergence, suggesting that the drivers of emergence across Europe differ between the South and the North. Driven by simulated future climate from an ensemble of 11 regional climate models, the model projects increase in the future risk of BT emergence across most of Europe with uncertainty in rate but not in trend. The framework described here is adaptable and applicable to other diseases, where the link between climate and disease transmission risk can be quantified, permitting the evaluation of scale and uncertainty in climate change''s impact on the future of such diseases.     

Simulating climate and energy policy with agent-based modelling: The Energy Modelling Laboratory (EMLab)

We present an approach to simulate climate and energy policy for the EU, using a flexible and modular agent-based modelling approach and a toolbox, called the Energy Modelling Laboratory (EMLab). The paper shortly reviews core challenges and approaches for modelling climate and energy policy in light of the energy transition. Afterwards, we present an agent-based model of investment in power generation that has addressed a variety of European energy policy questions. We describe the development of a flexible model core as well as modules on carbon and renewables policies, capacity mechanisms, investment behaviour and representation of intermittent renewables. We present an overview of modelling results, ongoing projects, a case study on current reforms of the EU ETS, and we show their relevance in the EU context.     

Are major economies on track to achieve their pledges for 2020? An assessment of domestic climate and energy policies

Many of the major greenhouse gas emitting countries have planned and/or implemented domestic mitigation policies, such as carbon taxes, feed-in tariffs, or standards. This study analyses whether the most effective national climate and energy policies are sufficient to stay on track for meeting the emission reduction proposals (pledges) that countries made for 2020. The analysis shows that domestic policies of India, China and Russia are projected to lead to lower emission levels than the pledged levels. Australia's and the EU's nationally legally binding policy framework is likely to deliver their unconditional pledges, but not the conditional ones. The situation is rather unclear for Japan, South Korea, Brazil and Indonesia. We project that policies of Canada and the USA will reduce 2020 emission levels, but additional policies are probably needed to deliver their pledges in full. The analysis also shows that countries are implementing policies or targets in various areas to a varying degree: all major countries have set renewable energy targets; many have recently implemented efficiency standards for cars, and new emission trading systems are emerging.     

Correlating indoor and outdoor temperature and humidity in a sample of buildings in tropical climates

The incidence of several respiratory viral infections has been shown to be related to climate. Because humans spend most of their time indoors, measures of indoor climate, rather than outdoor climate, may be better predictors of disease incidence and transmission. Therefore, understanding the relationship between indoor and outdoor climate will help illuminate their influence on the seasonality of diseases caused by respiratory viruses. Indoor-outdoor relationships between temperature and humidity have been documented in temperate regions, but little information is available for tropical regions, where seasonal patterns of respiratory viral diseases differ. We have examined indoor-outdoor correlations of temperature, relative humidity (RH), and absolute humidity (AH) over a 1-year period in each of seven tropical cities. Across all cities, the average monthly indoor temperature was 25 ± 3°C (mean ± standard deviation) with a range of 20–30°C. The average monthly indoor RH was 66 ± 9% with a range of 50–78%, and the average monthly indoor AH was 15 ± 3 g/m³ with a range of 10–23 g/m³. Indoor AH and RH were linearly correlated with outdoor AH when the air conditioning (AC) was off, suggesting that outdoor AH may be a good proxy of indoor humidity in the absence of AC. All indoor measurements were more strongly correlated with outdoor measurements as distance from the equator increased. Such correlations were weaker during the wet season, especially when AC was in operation. These correlations will provide insight for assessing the seasonality of respiratory viral infections using outdoor climate data, which is more widely available than indoor data, even though transmission of these diseases mainly occurs indoors.     

Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?

In June 2015, China announced its post-2020 reduction targets, its central element being the intention to peak CO₂ emissions by 2030 or earlier. China has implemented several policies to reduce its greenhouse gas (GHG) emissions. This study provides emission projections for China up to 2030 given current policies and a selected set of enhanced policies, and compares the results with projected CO₂ emission trajectories that are consistent with the announced target for 2030. The projections are based on existing scenarios and energy system and land use model calculations. We project that the 2030 CO₂ emission level consistent with a peak in CO₂ emissions by 2030 ranges from 11.3 to 11.8 GtCO₂. The corresponding total GHG emission level ranges from 13.5 to 14.0 GtCO₂e in 2030. Current policies are likely not to be sufficient to achieve the 2030 targets, as our projected total GHG emission level under current policies ranges from 14.7 to 15.4 GtCO₂e by 2030. However, an illustrative set of enhancement policy measures, all of which are related to national priorities, leads to projected GHG emission levels from 13.1 to 13.7 GtCO₂e by 2030 – and thus below the levels necessary for peaking CO₂ emissions before 2030.     

Seasonality of meningitis in Africa and climate forcing: aerosols stand out

Bacterial meningitis is an ongoing threat for the population of the African Meningitis Belt, a region characterized by the highest incidence rates worldwide. The determinants of the disease dynamics are still poorly understood; nevertheless, it is often advocated that climate and mineral dust have a large impact. Over the last decade, several studies have investigated this relationship at a large scale. In this analysis, we scaled down to the district-level weekly scale (which is used for in-year response to emerging epidemics), and used wavelet and phase analysis methods to define and compare the time-varying periodicities of meningitis, climate and dust in Niger. We mostly focused on detecting time-lags between the signals that were consistent across districts. Results highlighted the special case of dust in comparison to wind, humidity or temperature: a strong similarity between districts is noticed in the evolution of the time-lags between the seasonal component of dust and meningitis. This result, together with the assumption of dust damaging the pharyngeal mucosa and easing bacterial invasion, reinforces our confidence in dust forcing on meningitis seasonality. Dust data should now be integrated in epidemiological and forecasting models to make them more realistic and usable in a public health perspective.     

Climate and Bordeaux wine quality: identifying the key factors that differentiate vintages based on consensus rankings

The climate of Bordeaux, France, was examined to determine if climatic factors can distinguish between consensus vintage rankings, developed using eight ratings sources, of red and sweet white wines from 1961 to 2009. Climate variables were computed for the growing season and average plant phenological stages and were compared between the 10 highest and lowest ranked vintages. Good vintages exhibited higher heat accumulation during the growing season and a general lack of rainfall, particularly during veraison. Most climate factors were consistent for both red and sweet white wines. Mean maximum temperature during the growing season was an important discriminator between good and poor vintages for both reds and whites, although sweet white wines were also affected by growing season precipitation and temperatures during the vine's dormant period. In general, consensus vintage quality is consistent between reds and whites (Spearman's ρ?=?0.66, p?climate change studies to those climate variables that have the most impact on wine quality.     

Large lakes in climate models: A Great Lakes case study on the usability of CMIP5

Large lakes have an impact on regional weather. In addition, they can be both sensitive to and influence regional climate changes. In the climate models that are used to investigate future climate changes, lakes are greatly simplified and sometimes absent. At the regional scale, this can have strong implications for the quality of the model information about the future. Through our work with climate information users in the Laurentian Great Lakes region, we have found that basic credibility of the information requires the underlying climate models simulate lake-atmosphere-land interactions. We are not aware of efforts within the scientific community to make known how individual large lakes are represented in models and how those representations translate to the quality of the data for particular regions. We share our framework for identifying how the Laurentian Great Lakes are represented in the Coupled Model Intercomparison Project (CMIP) version 5 climate models. We found that most CMIP5 models do not simulate the Great Lakes in a way that captures their impact on the regional climate, which is a credibility issue for their projections. We provide a perspective on the usability of CMIP5 for practitioners in the Great Lakes region and offer recommendations for alternative options.     

Spatio-temporal patterns of birch regrowth in a Western Norwegian treeline ecotone

The study investigates spatio-temporal patterns of birch regrowth in a mountain valley in which intensive livestock grazing was formerly practised. Vegetation changes were identified through analysis of three sets of aerial photographs. The results of GIS analyses, one-way ANOVA and PCA showed that regrowth patterns are complex. In the early phase of succession, grazing history has a strong impact on where new forest establishes, both at a distance from the seasonal farmsteads where grazing intensity prior to abandonment was highest, and close to previously existing forest. Sprouting is most likely to be the dominating regeneration strategy in this early phase. In a later phase of succession, the impact of grazing history on forest distribution patterns decreases, as exemplified by the increasing distance from existing forest. The largest amounts of birch forest then establish in warm locations with moderate moisture. Protection against wind appears to be important for birch establishment.