Search

Future projections of east Amazonian precipitation indicate drying, but they are uncertain and poorly understood. In this study we analyze the Amazonian precipitation response to individual atmospheric forcings using a number of global climate models. Black carbon is found to drive reduced precipitation over the Amazon due to temperature-driven circulation changes, but the magnitude is uncertain. CO2 drives reductions in precipitation concentrated in the east, mainly due to a robustly negative, but highly variable in magnitude, fast response. We find that the physiological effect of CO2 on plant stomata is the dominant driver of the fast response due to reduced latent heating and also contributes to the large model spread. Using a simple model, we show that CO2 physiological effects dominate future multimodel mean precipitation projections over the Amazon. However, in individual models temperature-driven changes can be large, but due to little agreement, they largely cancel out in the model mean. ©2018. The Authors.

We investigate the climate response to increased concentrations of black carbon (BC), as part of the Precipitation Driver Response Model Intercomparison Project (PDRMIP). A tenfold increase in BC is simulated by 9 global coupled-climate models, producing a model-median effective radiative forcing (ERF) of 0.82 (ranging from 0.41 to 2.91) Wm(−2), and a warming of 0.67 (0.16 to 1.66) K globally and 1.24 (0.26 to 4.31) K in the Arctic. A strong positive instantaneous radiative forcing (median of 2.10 Wm(−2) based on five of the models) is countered by negative rapid adjustments (−0.64 Wm(−2) for the same five models), which dampen the total surface temperature signal. Unlike other drivers of climate change, the response of temperature and cloud profiles to the BC forcing is dominated by rapid adjustments. Low-level cloud amounts increase for all models, while higher-level clouds are diminished. The rapid temperature response is particularly strong above 400 hPa, where increased atmospheric stabilization and reduced cloud cover contrast the response pattern of the other drivers. In conclusion, we find that this substantial increase in BC concentrations does have considerable impacts on important aspects of the climate system. However, some of these effects tend to offset one another, leaving a relatively small global warming of 0.47 K per Wm(−2) – about 20 % lower than the response to a doubling of CO(2). Translating the tenfold increase in BC to the present-day impact of anthropogenic BC (given the emissions used in this work) would leave a warming of merely 0.07 K.

Precipitation is expected to respond differently to various drivers of anthropogenic climate change. We present the first results from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where nine global climate models have perturbed CO2, CH4, black carbon, sulfate, and solar insolation. We divide the resulting changes to global mean and regional precipitation into fast responses that scale with changes in atmospheric absorption and slow responses scaling with surface temperature change. While the overall features are broadly similar between models, we find significant regional intermodel variability, especially over land. Black carbon stands out as a component that may cause significant model diversity in predicted precipitation change. Processes linked to atmospheric absorption are less consistently modeled than those linked to top-of-atmosphere radiative forcing. We identify a number of land regions where the model ensemble consistently predicts that fast precipitation responses to climate perturbations dominate over the slow, temperature-driven responses.

International audience; The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of two years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry-transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry-climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC/rBC and sulfate concentrations quite well, compared to past comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January-March underestimated by 59 and 37% for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44% for July–September), but with over- as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is three times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modelling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

The goal of the Chemistry‐Climate Model Validation (CCMVal) activity is to improve understanding of chemistry‐climate models (CCMs) through process‐oriented evaluation and to provide reliable projections of stratospheric ozone and its impact on climate. An appreciation of the details of model formulations is essential for understanding how models respond to the changing external forcings of greenhouse gases and ozonedepleting substances, and hence for understanding the ozone and climate forecasts produced by the models participating in this activity. Here we introduce and review the models used for the second round (CCMVal‐2) of this intercomparison, regarding the implementation of chemical, transport, radiative, and dynamical processes in these models. In particular, we review the advantages and problems associated with approaches used to model processes of relevance to stratospheric dynamics and chemistry. Furthermore, we state the definitions of the reference simulations performed, and describe the forcing data used in these simulations. We identify some developments in chemistry‐climate modeling that make models more physically based or more comprehensive, including the introduction of an interactive ocean, online photolysis, troposphere‐stratosphere chemistry, and non‐orographic gravity‐wave deposition as linked to tropospheric convection. The\ud relatively new developments indicate that stratospheric CCM modeling is becoming more consistent with our physically based understanding of the atmosphere.

International audience; We 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.

International audience; Boundary layer turbulence has a profound influence on the distribution of tracers with sources or sinks at the surface. The 40-year ERA-40 meteorological data set of the European Centre for Medium-range Weather Forecasts contains archived vertical diffusion coefficients. We evaluated the use of these archived diffusion coefficients versus off-line diagnosed coefficients based on other meteorological parameters archived during ERA-40 by examining the influence on the distribution of the radionuclide 222Rn in the chemistry transport model TM3. In total four different sets of vertical diffusion coefficients are compared: (i) 3-hourly vertical diffusion coefficients archived during the ERA-40 project, (ii) 3-hourly off-line diagnosed coefficients from a non-local scheme based on Holtslag and Boville (1993), Vogelezang and Holtslag (1996), and Beljaars and Viterbo (1999), (iii) 6-hourly coefficients archived during the ERA-40 project, and (iv) 6-hourly off-line diagnosed coefficients based on a local scheme described in Louis (1979) and Louis et al. (1982). The diffusion scheme to diagnose the coefficients off-line in (ii) is similar to the diffusion scheme used during the ERA-40 project (i and iii).

Boundary layer turbulence has a profound influence on the distribution of tracers with sources or sinks at the surface. The 40-year ERA-40 meteorological data set of the European Centre for Medium-range Weather Forecasts contains archived vertical diffusion coefficients. We evaluated the use of these archived diffusion coefficients instead of off-line diagnosed coefficients based on other meteorological parameters archived during ERA-40 by investigation of the effect on the distribution of the radioactive tracer 222Rn in the chemistry transport model TM3. In total four different sets of vertical diffusion coefficients are compared: (i) 3-hourly vertical diffusion coefficients archived during the ERA-40 project, (ii) 3-hourly off-line diagnosed coefficients from a non-local scheme based on Holtslag and Boville (1993), Vogelezang and Holtslag (1996), and Beljaars and Viterbo (1999), (iii) 6-hourly coefficients archived during the ERA-40 project, and (iv) 6-hourly off-line diagnosed coefficients based on a local scheme described in Louis (1979) and Louis et al. (1982). The diffusion scheme to diagnose the coefficients off-line in (ii) is similar to the diffusion scheme used during the ERA-40 project (i and iii). The archived diffusion coefficients from the ERA-40 project which are time-averaged cause stronger mixing than the instantaneous off-line diagnosed diffusion coefficients. This can be partially attributed to the effect of instantaneous versus time-averaged coefficients, as well as to differences in the diffusion schemes. The 3-hourly off-line diagnosis of diffusion coefficients can reproduce quite well the 3-hourly archived diffusion coefficients. Boundary layer heights are also available for the sets (ii) and (iii). Both were found to be in reasonable agreement with observations of the boundary layer height from Cabauw in the Netherlands and from the FIFE-campaign in the United States. Simulations of 222Rn with the TM3 model using these four sets of vertical diffusion coefficients are compared to surface measurements of 222Rn in Freiburg, Schauinsland, Cincinnati and Socorro in order to evaluate the effect of these different sets of diffusion coefficients on the tracer transport. It is found that the daily cycle of the 222Rn concentration is well represented using 3-hourly diffusion coefficients. Comparison with observations of 222Rn data with the station in Schauinsland which is situated on a hill shows that all considered schemes underestimate the amplitude of the daily cycle of the 222Rn concentration in the upper part of the atmospheric boundary layer. We conclude that the 3-hourly archived diffusion coefficients from ERA-40 are well suited for use in chemistry transport models.