A new version of the CNRM Chemistry-Climate Model, CNRM-CCM: description and improvements from the CCMVal-2 simulations

This paper presents a new version of the Météo-France CNRM Chemistry-Climate Model, so-called CNRM-CCM. It includes some fundamental changes from the previous version (CNRM-ACM) which was extensively evaluated in the context of the CCMVal-2 validation activity. The most notable changes concern the radiative code of the GCM, and the inclusion of the detailed stratospheric chemistry of our Chemistry-Transport model MOCAGE on-line within the GCM. A 47-yr transient simulation (1960–2006) is the basis of our analysis. CNRM-CCM generates satisfactory dynamical and chemical fields in the stratosphere. Several shortcomings of CNRM-ACM simulations for CCMVal-2 that resulted from an erroneous representation of the impact of volcanic aerosols as well as from transport deficiencies have been eliminated. <br><br> Remaining problems concern the upper stratosphere (5 to 1 hPa) where temperatures are too high, and where there are biases in the NO<sub>2</sub>, N<sub>2</sub>O<sub>5</sub> and O<sub>3</sub> mixing ratios. In contrast, temperatures at the tropical tropopause are too cold. These issues are addressed through the implementation of a more accurate radiation scheme at short wavelengths. Despite these problems we show that this new CNRM CCM is a useful tool to study chemistry-climate applications

A new chemistry-climate tropospheric and stratospheric model MOCAGE-Climat: evaluation of the present-day climatology and sensitivity to surface processes

International audienceWe present the chemistry-climate configuration of the Météo-France Chemistry and Transport Model, MOCAGE-Climat. MOCAGE-Climat is a state-of-the-art model that simulates the global distribution of ozone and its precursors (82 chemical species) both in the troposphere and the stratosphere, up to the mid-mesosphere (~70 km). Surface processes (emissions, dry deposition), convection, and scavenging are explicitly described in the model that has been driven by the ECMWF operational analyses of the period 2000–2005, on T21 and T42 horizontal grids and 60 hybrid vertical levels, with and without a procedure that reduces calculations in the boundary layer, and with on-line or climatological deposition velocities. Model outputs have been compared to available observations, both from satellites (TOMS, HALOE, SMR, SCIAMACHY, MOPITT) and in-situ instrument measurements (ozone sondes, MOZAIC and aircraft campaigns) at climatological timescales. The distribution of long-lived species is in fair agreement with observations in the stratosphere putting apart shortcomings linked to the large-scale circulation. The variability of the ozone column, both spatially and temporarily, is satisfactory. However, the too fast Brewer-Dobson circulation accumulates too much ozone in the lower to mid-stratosphere at the end of winter. Ozone in the UTLS region does not show any systematic bias. In the troposphere better agreement with ozone sonde measurements is obtained at mid and high latitudes than in the tropics and differences with observations are the lowest in summer. Simulations using a simplified boundary layer lead to ozone differences between the model and the observations up to the mid-troposphere. NOx in the lowest troposphere is in general overestimated, especially in the winter months over the northern hemisphere, which might result from a positive bias in OH. Dry deposition fluxes of O3 and nitrogen species are within the range of values reported by recent inter-comparison model exercises. The use of climatological deposition velocities versus deposition velocities calculated on-line had greatest impact on HNO3 and NO2 in the troposphere

Evaluation of the MOCAGE Chemistry Transport Model during the ICARTT/ITOP Experiment

We evaluate the Meteo-France global chemistry transport 3D model MOCAGE (MOdele de Chimie Atmospherique a Grande Echelle) using the important set of aircraft measurements collected during the ICARRT/ITOP experiment. This experiment took place between US and Europe during summer 2004 (July 15-August 15). Four aircraft were involved in this experiment providing a wealth of chemical data in a large area including the North East of US and western Europe. The model outputs are compared to the following species of which concentration is measured by the aircraft: OH, H2O2, CO, NO, NO2, PAN, HNO3, isoprene, ethane, HCHO and O3. Moreover, to complete this evaluation at larger scale, we used also satellite data such as SCIAMACHY NO2 and MOPITT CO. Interestingly, the comprehensive dataset allowed us to evaluate separately the model representation of emissions, transport and chemical processes. Using a daily emission source of biomass burning, we obtain a very good agreement for CO while the evaluation of NO2 points out incertainties resulting from inaccurate ratio of emission factors of NOx/CO. Moreover, the chemical behavior of O3 is satisfactory as discussed in the paper

Clinical effect of obesity on N-terminal pro-B-type natriuretic peptide cut-off concentrations for the diagnosis of acute heart failure

AIMS Obese patients have lower natriuretic peptide concentrations. We hypothesized that adjusting the concentration of N-terminal pro-B-type natriuretic peptide (NT-proBNP) for obesity could further increase its clinical utility in the early diagnosis of acute heart failure (AHF). METHODS AND RESULTS This hypothesis was tested in a prospective diagnostic study enrolling unselected patients presenting to the emergency department with acute dyspnoea. Two independent cardiologists/internists centrally adjudicated the final diagnosis using all individual patient information including cardiac imaging. NT-proBNP plasma concentrations were applied: first, using currently recommended cut-offs; second, using cut-offs lowered by 33% with body mass index (BMI) of 30-34.9 kg/m$^{2}$ and by 50% with BMI ≥ 35 kg/m$^{2}$ . Among 2038 patients, 509 (25%) were obese, of which 271 (53%) had AHF. The diagnostic accuracy of NT-proBNP as quantified by the area under the receiver-operating characteristic curve was lower in obese versus non-obese patients (0.890 vs. 0.938). For rapid AHF rule-out in obese patients, the currently recommended cut-off of 300 pg/ml achieved a sensitivity of 96.7% (95% confidence interval [CI] 93.8-98.2%), ruling out 29% of patients and missing 9 AHF patients. For rapid AHF rule-in, the age-dependent cut-off concentrations (age 75 years: 1800 pg/ml) achieved a specificity of 84.9% (95% CI 79.8-88.9%). Proportionally lowering the currently recommended cut-offs by BMI increased sensitivity to 98.2% (95% CI 95.8-99.2%), missing 5 AHF patients; reduced the proportion of AHF patients remaining in the 'gray zone' (48% vs. 26%; p = 0.002), achieving a specificity of 76.5% (95% CI 70.7-81.4%). CONCLUSIONS Adjusting NT-proBNP concentrations for obesity seems to further increase its clinical utility in the early diagnosis of AHF

Stuttered swallowing: Electric stimulation of the right insula interferes with water swallowing. A case report

<p>Abstract</p> <p>Background</p> <p>Various functional resonance imaging, magnetoencephalographic and lesion studies suggest the involvement of the insular cortex in the control of swallowing. However, the exact location of insular activation during swallowing and its functional significance remain unclear.</p> <p>Case presentation</p> <p>Invasive electroencephalographic monitoring was performed in a 24-year-old man with medically intractable stereotyped nocturnal hypermotor seizures due to a ganglioglioma. During stimulation of the right inferior posterior insular cortex with depth electrodes the patient spontaneously reported a perception of a "stutter in swallowing". Stimulation of the inferior posterior insular cortex at highest intensity (4 mA) was also associated with irregular and delayed swallows. Swallowing was not impaired during stimulation of the superior posterior insular cortex, regardless of stimulation intensity.</p> <p>Conclusions</p> <p>These results indicate that the right inferior posterior insular cortex is involved in the neural circuitry underlying the control of swallowing.</p

Air quality evaluation of London Paddington train station

Enclosed railway stations hosting diesel trains are at risk of reduced air quality as a result of exhaust emissions that may endanger passengers and workers. Air quality measurements were conducted inside London Paddington Station, a semi-enclosed railway station where 70% of trains are powered by diesel engines. Particulate matter (PM2.5) mass was measured at five station locations. PM size, PM number, oxides of nitrogen (NOx), and sulfur dioxide (SO2) were measured at two station locations. Paddington Station’s hourly mean PM2.5 mass concentrations averaged 16 μg/m3 [min 2, max 68]. Paddington Station’s hourly mean NO2 concentrations averaged 73 ppb [49, 120] and SO2 concentrations averaged 25 ppb [15, 37]. While UK train stations are not required to comply with air quality standards, there were five instances where the hourly mean NO2 concentrations exceeded the EU hourly mean limits (106 ppb) for outdoor air quality. PM2.5, SO2, and NO2 concentrations were compared against Marylebone, a busy London roadside 1.5 km from the station. The comparisons indicated that train station air quality was more polluted than the nearby roadside. PM2.5 for at least one measurement location within Paddington Station was shown to be statistically higher (P-value < 0.05) than Marylebone on 3 out of 4 days. Measured NO2 within Paddington Station was statistically higher than Marylebone on 4 out of 5 days. Measured SO2 within Paddington Station was statistically higher than Marylebone on all 3 days.We thank the Engineering and Physical Sciences Research Council (EP/F034350/1) for funding the Energy Efficient Cities Initiative and the Schiff Foundation for doctoral studentship funding.This is the final version of the article. It first appeared from IOP via http://dx.doi.org/10.1088/1748-9326/10/9/09401

Predicting Major Adverse Events in Patients With Acute Myocardial Infarction

Early and accurate detection of short-term major adverse cardiac events (MACE) in patients with suspected acute myocardial infarction (AMI) is an unmet clinical need.; The goal of this study was to test the hypothesis that adding clinical judgment and electrocardiogram findings to the European Society of Cardiology (ESC) high-sensitivity cardiac troponin (hs-cTn) measurement at presentation and after 1 h (ESC hs-cTn 0/1 h algorithm) would further improve its performance to predict MACE.; Patients presenting to an emergency department with suspected AMI were enrolled in a prospective, multicenter diagnostic study. The primary endpoint was MACE, including all-cause death, cardiac arrest, AMI, cardiogenic shock, sustained ventricular arrhythmia, and high-grade atrioventricular block within 30 days including index events. The secondary endpoint was MACE + unstable angina (UA) receiving early (≤24 h) revascularization.; Among 3,123 patients, the ESC hs-cTnT 0/1 h algorithm triaged significantly more patients toward rule-out compared with the extended algorithm (60%; 95% CI: 59% to 62% vs. 45%; 95% CI: 43% to 46%; p < 0.001), while maintaining similar 30-day MACE rates (0.6%; 95% CI: 0.3% to 1.1% vs. 0.4%; 95% CI: 0.1% to 0.9%; p = 0.429), resulting in a similar negative predictive value (99.4%; 95% CI: 98.9% to 99.6% vs. 99.6%; 95% CI: 99.2% to 99.8%; p = 0.097). The ESC hs-cTnT 0/1 h algorithm ruled-in fewer patients (16%; 95% CI: 14.9% to 17.5% vs. 26%; 95% CI: 24.2% to 27.2%; p < 0.001) compared with the extended algorithm, albeit with a higher positive predictive value (76.6%; 95% CI: 72.8% to 80.1% vs. 59%; 95% CI: 55.5% to 62.3%; p < 0.001). For 30-day MACE + UA, the ESC hs-cTnT 0/1 h algorithm had a higher positive predictive value for rule-in, whereas the extended algorithm had a higher negative predictive value for the rule-out. Similar findings emerged when using hs-cTnI.; The ESC hs-cTn 0/1 h algorithm better balanced efficacy and safety in the prediction of MACE, whereas the extended algorithm is the preferred option for the rule-out of 30-day MACE + UA. (Advantageous Predictors of Acute Coronary Syndromes Evaluation [APACE]; NCT00470587)

Revisiting the Mystery of Recent Stratospheric Temperature Trends

Simulated stratospheric temperatures over the period 1979–2016 in models from the Chemistry-Climate Model Initiative are compared with recently updated and extended satellite data sets. The multimodel mean global temperature trends over 1979–2005 are -0.88 ± 0.23, -0.70 ± 0.16, and -0.50 ± 0.12 K/decade for the Stratospheric Sounding Unit (SSU) channels 3 (~40–50 km), 2 (~35–45 km), and 1 (~25–35 km), respectively (with 95% confidence intervals). These are within the uncertainty bounds of the observed temperature trends from two reprocessed SSU data sets. In the lower stratosphere, the multimodel mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13–22 km) is -0.25 ± 0.12 K/decade over 1979–2005, consistent with observed estimates from three versions of this satellite record. The models and an extended satellite data set comprised of SSU with the Advanced Microwave Sounding Unit-A show weaker global stratospheric cooling over 1998–2016 compared to the period of intensive ozone depletion (1979–1997). This is due to the reduction in ozone-induced cooling from the slowdown of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite-observed stratospheric temperature trends than was reported by Thompson et al. (2012, https://doi.org/10.1038/nature11579) for the previous versions of the SSU record and chemistry-climate models. The improved agreement mainly comes from updates to the satellite records; the range of stratospheric temperature trends over 1979–2005 simulated in Chemistry-Climate Model Initiative models is comparable to the previous generation of chemistry-climate models

Estimates of ozone return dates from Chemistry-Climate Model Initiative simulations

We analyse simulations performed for the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion caused by anthropogenic stratospheric chlorine and bromine. We consider a total of 155 simulations from 20 models, including a range of sensitivity studies which examine the impact of climate change on ozone recovery. For the control simulations (unconstrained by nudging towards analysed meteorology) there is a large spread (±20DU in the global average) in the predictions of the absolute ozone column. Therefore, the model results need to be adjusted for biases against historical data. Also, the interannual variability in the model results need to be smoothed in order to provide a reasonably narrow estimate of the range of ozone return dates. Consistent with previous studies, but here for a Representative Concentration Pathway (RCP) of 6.0, these new CCMI simulations project that global total column ozone will return to 1980 values in 2049 (with a 1σ uncertainty of 2043–2055). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2045 (2039–2050), and at Northern Hemisphere mid-latitudes in 2032 (2020–2044). In the polar regions, the return dates are 2060 (2055–2066) in the Antarctic in October and 2034 (2025–2043) in the Arctic in March. The earlier return dates in the Northern Hemisphere reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–17 years, depending on the region, with the previous best estimates often falling outside of our uncertainty range. In the tropics only around half the models predict a return of ozone to 1980 values, around 2040, while the other half do not reach the 1980 value. All models show a negative trend in tropical total column ozone towards the end of the 21st century. The CCMI models generally agree in their simulation of the time evolution of stratospheric chlorine and bromine, which are the main drivers of ozone loss and recovery. However, there are a few outliers which show that the multi-model mean results for ozone recovery are not as tightly constrained as possible. Throughout the stratosphere the spread of ozone return dates to 1980 values between models tends to correlate with the spread of the return of inorganic chlorine to 1980 values. In the upper stratosphere, greenhouse gas-induced cooling speeds up the return by about 10–20 years. In the lower stratosphere, and for the column, there is a more direct link in the timing of the return dates of ozone and chlorine, especially for the large Antarctic depletion. Comparisons of total column ozone between the models is affected by different predictions of the evolution of tropospheric ozone within the same scenario, presumably due to differing treatment of tropospheric chemistry. Therefore, for many scenarios, clear conclusions can only be drawn for stratospheric ozone columns rather than the total column. As noted by previous studies, the timing of ozone recovery is affected by the evolution of N2O and CH4. However, quantifying the effect in the simulations analysed here is limited by the few realisations available for these experiments compared to internal model variability. The large increase in N2O given in RCP 6.0 extends the ozone return globally by ∼15 years relative to N2O fixed at 1960 abundances, mainly because it allows tropical column ozone to be depleted. The effect in extratropical latitudes is much smaller. The large increase in CH4 given in the RCP 8.5 scenario compared to RCP 6.0 also lengthens ozone return by ∼15 years, again mainly through its impact in the tropics. Overall, our estimates of ozone return dates are uncertain due to both uncertainties in future scenarios, in particular those of greenhouse gases, and uncertainties in models. The scenario uncertainty is small in the short term but increases with time, and becomes large by the end of the century. There are still some model–model differences related to well-known processes which affect ozone recovery. Efforts need to continue to ensure that models used for assessment purposes accurately represent stratospheric chemistry and the prescribed scenarios of ozone-depleting substances, and only those models are used to calculate return dates. For future assessments of single forcing or combined effects of CO2, CH4, and N2O on the stratospheric column ozone return dates, this work suggests that it is more important to have multi-member (at least three) ensembles for each scenario from every established participating model, rather than a large number of individual models

Revisiting the mystery of recent stratospheric temperature trends

Simulated stratospheric temperatures over the period 1979‐2016 in models from the Chemistry‐Climate Model Initiative (CCMI) are compared with recently updated and extended satellite observations. The multi‐model mean global temperature trends over 1979‐2005 are ‐0.88 ± 0.23, ‐0.70 ± 0.16, and ‐0.50 ± 0.12 K decade⁻¹ for the Stratospheric Sounding Unit (SSU) channels 3 (~40‐50 km), 2 (~35‐45 km), and 1 (~25‐35 km), respectively. These are within the uncertainty bounds of the observed temperature trends from two reprocessed satellite datasets. In the lower stratosphere, the multi‐model mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13‐22 km) is ‐0.25 ± 0.12 K decade⁻¹ over 1979‐2005, consistent with estimates from three versions of this satellite record. The simulated stratospheric temperature trends in CCMI models over 1979‐2005 agree with the previous generation of chemistry‐climate models. The models and an extended satellite dataset of SSU with the Advanced Microwave Sounding Unit‐A show weaker global stratospheric cooling over 1998‐2016 compared to the period of intensive ozone depletion (1979‐1997). This is due to the reduction in ozone‐induced cooling from the slow‐down of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite observed stratospheric temperature trends than was reported by Thompson et al. (2012) for the previous versions of the SSU record and chemistry‐climate models. The improved agreement mainly comes from updates to the satellite records; the range of simulated trends is comparable to the previous generation of models