Kerb and urban increment of highly time-resolved trace elements in PM10, PM2.5 and PM1.0 winter aerosol in London during ClearfLo 2012

Ambient concentrations of trace elements with 2 h time resolution were measured in PM10–2.5, PM2.5–1.0 and PM1.0–0.3 size ranges at kerbside, urban background and rural sites in London during winter 2012. Samples were collected using rotating drum impactors (RDIs) and subsequently analysed with synchrotron radiation-induced X-ray fluorescence spectrometry (SR-XRF). Quantification of kerb and urban increments (defined as kerb-to-urban and urban-to-rural concentration ratios, respectively), and assessment of diurnal and weekly variability provided insight into sources governing urban air quality and the effects of urban micro-environments on human exposure. Traffic-related elements yielded the highest kerb increments, with values in the range of 10.4 to 16.6 for SW winds (3.3–6.9 for NE) observed for elements influenced by brake wear (e.g. Cu, Sb, Ba) and 5.7 to 8.2 for SW (2.6–3.0 for NE) for other traffic-related processes (e.g. Cr, Fe, Zn). Kerb increments for these elements were highest in the PM10–2.5 mass fraction, roughly twice that of the PM1.0–0.3 fraction. These elements also showed the highest urban increments (~ 3.0), although no difference was observed between brake wear and other traffic-related elements. All elements influenced by traffic exhibited higher concentrations during morning and evening rush hours, and on weekdays compared to weekends, with the strongest trends observed at the kerbside site, and additionally enhanced by winds coming directly from the road, consistent with street canyon effects. Elements related to mineral dust (e.g. Al, Si, Ca, Sr) showed significant influences from traffic-induced resuspension, as evidenced by moderate kerb (3.4–5.4 for SW, 1.7–2.3 for NE) and urban (~ 2) increments and increased concentrations during peak traffic flow. Elements related to regional transport showed no significant enhancement at kerb or urban sites, with the exception of PM10–2.5 sea salt (factor of up to 2), which may be influenced by traffic-induced resuspension of sea and/or road salt. Heavy-duty vehicles appeared to have a larger effect than passenger vehicles on the concentrations of all elements influenced by resuspension (including sea salt) and wearing processes. Trace element concentrations in London were influenced by both local and regional sources, with coarse and intermediate fractions dominated by traffic-induced resuspension and wearing processes and fine particles influenced by regional transport.

The Climate-system Historical Forecast Project: do stratosphere-resolving models make better seasonal climate predictions in boreal winter?

Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics (“high-top”) and models that do not (“low-top”). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (Dec-Mar) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Niño-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on intra-seasonal to seasonal timescales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Niño and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Niño conditions suggests a possible role for a stratospheric pathway.

On the ability of statistical wind-wave models to capture the variability and long-term trends of the North Atlantic winter wave climate

A dynamical wind-wave climate simulation covering the North Atlantic Ocean and spanning the whole 21st century under the A1B scenario has been compared with a set of statistical projections using atmospheric variables or large scale climate indices as predictors. As a first step, the performance of all statistical models has been evaluated for the present-day climate; namely they have been compared with a dynamical wind-wave hindcast in terms of winter Significant Wave Height (SWH) trends and variance as well as with altimetry data. For the projections, it has been found that statistical models that use wind speed as independent variable predictor are able to capture a larger fraction of the winter SWH inter-annual variability (68% on average) and of the long term changes projected by the dynamical simulation. Conversely, regression models using climate indices, sea level pressure and/or pressure gradient as predictors, account for a smaller SWH variance (from 2.8% to 33%) and do not reproduce the dynamically projected long term trends over the North Atlantic. Investigating the wind-sea and swell components separately, we have found that the combination of two regression models, one for wind-sea waves and another one for the swell component, can improve significantly the wave field projections obtained from single regression models over the North Atlantic.