TL;DR: In this paper, the authors present a summary of the work done within the European Union's Seventh Framework Programme project ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants).
Abstract: . This paper presents a summary of the work done within the European Union's Seventh Framework Programme project ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants). ECLIPSE had a unique systematic concept for designing a realistic and effective mitigation scenario for short-lived climate pollutants (SLCPs; methane, aerosols and ozone, and their precursor species) and quantifying its climate and air quality impacts, and this paper presents the results in the context of this overarching strategy. The first step in ECLIPSE was to create a new emission inventory based on current legislation (CLE) for the recent past and until 2050. Substantial progress compared to previous work was made by including previously unaccounted types of sources such as flaring of gas associated with oil production, and wick lamps. These emission data were used for present-day reference simulations with four advanced Earth system models (ESMs) and six chemistry transport models (CTMs). The model simulations were compared with a variety of ground-based and satellite observational data sets from Asia, Europe and the Arctic. It was found that the models still underestimate the measured seasonality of aerosols in the Arctic but to a lesser extent than in previous studies. Problems likely related to the emissions were identified for northern Russia and India, in particular. To estimate the climate impacts of SLCPs, ECLIPSE followed two paths of research: the first path calculated radiative forcing (RF) values for a large matrix of SLCP species emissions, for different seasons and regions independently. Based on these RF calculations, the Global Temperature change Potential metric for a time horizon of 20 years (GTP20) was calculated for each SLCP emission type. This climate metric was then used in an integrated assessment model to identify all emission mitigation measures with a beneficial air quality and short-term (20-year) climate impact. These measures together defined a SLCP mitigation (MIT) scenario. Compared to CLE, the MIT scenario would reduce global methane (CH4) and black carbon (BC) emissions by about 50 and 80 %, respectively. For CH4, measures on shale gas production, waste management and coal mines were most important. For non-CH4 SLCPs, elimination of high-emitting vehicles and wick lamps, as well as reducing emissions from gas flaring, coal and biomass stoves, agricultural waste, solvents and diesel engines were most important. These measures lead to large reductions in calculated surface concentrations of ozone and particulate matter. We estimate that in the EU, the loss of statistical life expectancy due to air pollution was 7.5 months in 2010, which will be reduced to 5.2 months by 2030 in the CLE scenario. The MIT scenario would reduce this value by another 0.9 to 4.3 months. Substantially larger reductions due to the mitigation are found for China (1.8 months) and India (11–12 months). The climate metrics cannot fully quantify the climate response. Therefore, a second research path was taken. Transient climate ensemble simulations with the four ESMs were run for the CLE and MIT scenarios, to determine the climate impacts of the mitigation. In these simulations, the CLE scenario resulted in a surface temperature increase of 0.70 ± 0.14 K between the years 2006 and 2050. For the decade 2041–2050, the warming was reduced by 0.22 ± 0.07 K in the MIT scenario, and this result was in almost exact agreement with the response calculated based on the emission metrics (reduced warming of 0.22 ± 0.09 K). The metrics calculations suggest that non-CH4 SLCPs contribute ~ 22 % to this response and CH4 78 %. This could not be fully confirmed by the transient simulations, which attributed about 90 % of the temperature response to CH4 reductions. Attribution of the observed temperature response to non-CH4 SLCP emission reductions and BC specifically is hampered in the transient simulations by small forcing and co-emitted species of the emission basket chosen. Nevertheless, an important conclusion is that our mitigation basket as a whole would lead to clear benefits for both air quality and climate. The climate response from BC reductions in our study is smaller than reported previously, possibly because our study is one of the first to use fully coupled climate models, where unforced variability and sea ice responses cause relatively strong temperature fluctuations that may counteract (and, thus, mask) the impacts of small emission reductions. The temperature responses to the mitigation were generally stronger over the continents than over the oceans, and with a warming reduction of 0.44 K (0.39–0.49) K the largest over the Arctic. Our calculations suggest particularly beneficial climate responses in southern Europe, where surface warming was reduced by about 0.3 K and precipitation rates were increased by about 15 (6–21) mm yr−1 (more than 4 % of total precipitation) from spring to autumn. Thus, the mitigation could help to alleviate expected future drought and water shortages in the Mediterranean area. We also report other important results of the ECLIPSE project.
TL;DR: His analysis, using all 97 200 possible subsets of data comprising two runs per model and scenario, illustrates clearly that the results are sensitive to the particular dataset that is chosen and thus provides a more complete summary of the sources of variation in phase 3 of the CMIP3 ensemble.
Abstract: We thank Dr. Northrop for his thoughtful comments (Northrop 2013) on our article (Yip et al. 2011, hereafter YFSH11). His analysis, using all 97 200 possible subsets of data comprising two runs per model and scenario, illustrates clearly that the results are sensitive to the particular dataset that is chosen and thus provides a more complete summary of the sources of variation in phase 3 of the Coupled Model Intercomparison Project (CMIP3) ensemble than we presented in YFSH11. Our principal aim, of course, was to describe and illustrate the analysis of variance (ANOVA) methodology for decomposing the total uncertainty in a climate ensemble into model uncertainty, scenario uncertainty, internal variability, and model–scenario interaction. We did not attempt a comprehensive analysis of the CMIP3 ensemble, but we are glad that Dr. Northrop has extended our analysis to do so. To check the robustness of the conclusions in YFSH11 to the choice of data subset, we repeated our original analysis for each of six different subsets corresponding to all possible choices of two runs from the four runs of the Parallel Climate Model (PCM) under scenario B1. The main conclusions from YFSH11 still stand, even though they were obtained from analyzing only one of the 97 200 datasets presented by Dr. Northrop: 1) scenario uncertainty dominates all other components after 2050, 2) internal variability is constant over time but decreases sharply as a fraction of the total uncertainty, 3) scenario uncertainty dominates the model–scenario interaction uncertainty over the entire century, and 4) model– scenario interaction makes an important contribution to the total uncertainty. To make a comprehensive assessment of the variation in an ensemble, it is ideal to include all possible model runs. However, ensembles with unbalanced designs, where unequal numbers of the different model–scenario combinations are available, complicate the analysis. The ANOVA framework can easily be extended to unbalanced designs, for example, by using linear regression on model and scenario factors (Sansom et al. 2013). The results from such frameworks depend on the design of the experiments and so this needs to be considered more carefully when designing multimodel ensembles (e.g., avoiding only one run per model in future scenarios, which is prevalent in CMIP5).
TL;DR: In this paper, an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction is presented for the first time, where the authors find a relationship between summer average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach.
TL;DR: NPCP, a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations, and an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles are developed.
Abstract: Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested Parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.
TL;DR: In this article, a simple and robust framework is proposed for the partitioning of the different components of internal variability and model uncertainty in an unbalanced multimember multimodel ensemble (MM2E) of climate projections obtained for a suite of statistical downscaling models (SDMs) and global climate models (GCMs).
Abstract: A simple and robust framework is proposed for the partitioning of the different components of internal variability and model uncertainty in an unbalanced multimember multimodel ensemble (MM2E) of climate projections obtained for a suite of statistical downscaling models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of the different modeling chains using a two-way analysis of variance (ANOVA) framework. The residuals from the noise-free signals are used to estimate the large- and small-scale internal variability components associated with each considered GCM–SDM configuration. This framework makes it possible to take into account all members available from any climate ensemble of opportunity. Uncertainty is quantified as a function of lead time for projections of changes in temperature and precipitation produced for a mesoscale alpine catchment. Internal v...