Heavy metal distribution in karst soils from Croatia and Slovakia

With the use of the optimised three-step BCR sequential-extraction procedure it was possible to assess the mobility of selected elements in soil profiles from Croatian and Slovakian karst terrains. The soils in the Croatian karst were enriched in Cr, Ni, V, Mn, Cu, Cd and Mo, while soils from the Slovak Karst had high Pb and Zn concentrations. It was determined that the elements were most readily mobilised from the topsoil and the degree of mobility decreased with depth. Cr and Ni were mainly bound to the residual fraction, and Pb in the oxidisable fraction. Cu mobility was high in samples treated with agrochemicals throughout the soil profile.     

A transition from CMIP3 to CMIP5 for climate information providers: the case of surface temperature over eastern North America

The release of new data constituting the Coupled Model Intercomparison Project—Phase 5 (CMIP5) database is an important event in both climate science and climate services issues. Although users’ eagerness for a fast transition from CMIP3 to CMIP5 is expected, this change implies some challenges for climate information providers. The main reason is that the two sets of experiments were performed in different ways regarding radiative forcing and hence continuity between both datasets is partially lost. The objective of this research is to evaluate a metric that is independent of the amount and the evolution of radiative forcing, hence facilitating comparison between the two sets for surface temperature over eastern North America. The link between CMIP3 and CMIP5 data sets is explored spatially and locally (using the ratio of local to global temperatures) through the use of regional warming patterns, a relationship between the grid-box and the global mean temperature change for a certain time frame. Here, we show that local to global ratios are effective tools in making climate change information between the two sets comparable. As a response to the global mean temperature change, both CMIP experiments show very similar warming patterns, trends, and climate change uncertainty for both winter and summer. Sensitivity of the models to radiative forcing is not assessed. Real inter-model differences remain the largest source of uncertainty when calculating warming patterns as well as spatially-based patterns for the pattern scaling approach. This relationship between the datasets, which may escape users when they are provided with a single radiative forcing pathway, needs to be stressed by climate information providers.     

An evaluation of the surface radiation budget over North America for a suite of regional climate models against surface station observations

Components of the surface radiation budget (SRB) [incoming shortwave radiation (ISR) and downwelling longwave radiation (DLR)] and cloud cover are assessed for three regional climate models (RCM) forced by analysed boundary conditions, over North America. We present a comparison of the mean seasonal and diurnal cycles of surface radiation between the three RCMs, and surface observations. This aids in identifying in what type of sky situation simulated surface radiation budget errors arise. We present results for total-sky conditions as well as overcast and clear-sky conditions separately. Through the analysis of normalised frequency distributions we show the impact of varying cloud cover on the simulated and observed surface radiation budget, from which we derive observed and model estimates of surface cloud radiative forcing. Surface observations are from the NOAA SURFRAD network. For all models DLR all-sky biases are significantly influenced by cloud-free radiation, cloud emissivity and cloud cover errors. Simulated cloud-free DLR exhibits a systematic negative bias during cold, dry conditions, probably due to a combination of omission of trace gas contributions to the DLR and a poor treatment of the water vapor continuum at low water vapor concentrations. Overall, models overestimate ISR all-sky in summer, which is primarily linked to an underestimate of cloud cover. Cloud-free ISR is relatively well simulated by all RCMs. We show that cloud cover and cloud-free ISR biases can often compensate to result in an accurate total-sky ISR, emphasizing the need to evaluate the individual components making up the total simulated SRB.