The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Overview and description of models, simulations and climate diagnostics

The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) consists of a series of time slice experiments targeting the long-term changes in atmospheric composition between 1850 and 2100, with the goal of documenting composition changes and the associated radiative forcing. In this overview paper, we introduce the ACCMIP activity, the various simulations performed (with a requested set of 14) and the associated model output. The 16 ACCMIP models have a wide range of horizontal and vertical resolutions, vertical extent, chemistry schemes and interaction with radiation and clouds. While anthropogenic and biomass burning emissions were specified for all time slices in the ACCMIP protocol, it is found that the natural emissions are responsible for a significant range across models, mostly in the case of ozone precursors. The analysis of selected present-day climate diagnostics (precipitation, temperature, specific humidity and zonal wind) reveals biases consistent with state-of-the-art climate models. The model-to-model comparison of changes in temperature, specific humidity and zonal wind between 1850 and 2000 and between 2000 and 2100 indicates mostly consistent results. However, models that are clear outliers are different enough from the other models to significantly affect their simulation of atmospheric chemistry

Attribution of chemistry-climate model initiative (CCMI) ozone radiative flux bias from satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6 µm ozone band is a fundamental quantity for understanding chemistry–climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present-day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6 µm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. The principal culprits are tropical middle and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mW m−2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mW m−2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mW m−2). We found that the atmospheric component (AM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model and Canadian Middle Atmosphere Model (CMAM) have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to different processes. Overall, the multi-model ensemble mean bias is −133±98  mW m−2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation

Cluster-based ensemble means for climate model intercomparison

Clustering – the automated grouping of similar data – can provide powerful and unique insight into large and complex data sets, in a fast and computationally efficient manner. While clustering has been used in a variety of fields (from medical image processing to economics), its application within atmospheric science has been fairly limited to date, and the potential benefits of the application of advanced clustering techniques to climate data (both model output and observations) has yet to be fully realised. In this paper, we explore the specific application of clustering to a multi-model climate ensemble. We hypothesise that clustering techniques can provide (a) a flexible, data-driven method of testing model–observation agreement and (b) a mechanism with which to identify model development priorities. We focus our analysis on chemistry–climate model (CCM) output of tropospheric ozone – an important greenhouse gas – from the recent Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Tropospheric column ozone from the ACCMIP ensemble was clustered using the Data Density based Clustering (DDC) algorithm. We find that a multi-model mean (MMM) calculated using members of the most-populous cluster identified at each location offers a reduction of up to  ∼  20 % in the global absolute mean bias between the MMM and an observed satellite-based tropospheric ozone climatology, with respect to a simple, all-model MMM. On a spatial basis, the bias is reduced at  ∼  62 % of all locations, with the largest bias reductions occurring in the Northern Hemisphere – where ozone concentrations are relatively large. However, the bias is unchanged at 9 % of all locations and increases at 29 %, particularly in the Southern Hemisphere. The latter demonstrates that although cluster-based subsampling acts to remove outlier model data, such data may in fact be closer to observed values in some locations. We further demonstrate that clustering can provide a viable and useful framework in which to assess and visualise model spread, offering insight into geographical areas of agreement among models and a measure of diversity across an ensemble. Finally, we discuss caveats of the clustering techniques and note that while we have focused on tropospheric ozone, the principles underlying the cluster-based MMMs are applicable to other prognostic variables from climate models

Stratospheric ozone change and related climate impacts over 1850-2100 as modelled by the ACCMIP ensemble

Stratospheric ozone and associated climate impacts in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) simulations are evaluated in the recent past (1980–2000), and examined in the long-term (1850–2100) using the Representative Concentration Pathways (RCPs) low- and high-emission scenarios (RCP2.6 and RCP8.5, respectively) for the period 2000–2100. ACCMIP multi-model mean total column ozone (TCO) trends compare favourably, within uncertainty estimates, against observations. Particularly good agreement is seen in the Antarctic austral spring (−11.9 % dec−1 compared to observed  ∼  −13.9 ± 10.4 % dec−1), although larger deviations are found in the Arctic's boreal spring (−2.1 % dec−1 compared to observed  ∼  −5.3 ± 3.3 % dec−1). The simulated ozone hole has cooled the lower stratosphere during austral spring in the last few decades (−2.2 K dec−1). This cooling results in Southern Hemisphere summertime tropospheric circulation changes captured by an increase in the Southern Annular Mode (SAM) index (1.3 hPa dec−1). In the future, the interplay between the ozone hole recovery and greenhouse gases (GHGs) concentrations may result in the SAM index returning to pre-ozone hole levels or even with a more positive phase from around the second half of the century (−0.4 and 0.3 hPa dec−1 for the RCP2.6 and RCP8.5, respectively). By 2100, stratospheric ozone sensitivity to GHG concentrations is greatest in the Arctic and Northern Hemisphere midlatitudes (37.7 and 16.1 DU difference between the RCP2.6 and RCP8.5, respectively), and smallest over the tropics and Antarctica continent (2.5 and 8.1 DU respectively). Future TCO changes in the tropics are mainly determined by the upper stratospheric ozone sensitivity to GHG concentrations, due to a large compensation between tropospheric and lower stratospheric column ozone changes in the two RCP scenarios. These results demonstrate how changes in stratospheric ozone are tightly linked to climate and show the benefit of including the processes interactively in climate models

Future global mortality from changes in air pollution attributable to climate change

Ground-level ozone and fine particulate matter (PM2.5) are associated with premature human mortality1-4; their future concentrations depend on changes in emissions, which dominate the near-term5, and on climate change6,7. Previous global studies of the air quality-related health effects of future climate change8,9 used single atmospheric models. However, in related studies, mortality results differ among models10-12. Here we use an ensemble of global chemistry-climate models13 to show that premature mortality from changes in air pollution attributable to climate change, under the high greenhouse gas scenario RCP8.514, is likely positive. We estimate 3,340 (-30,300 to 47,100) ozone-related deaths in 2030, relative to 2000 climate, and 43,600 (-195,000 to 237,000) in 2100 (14% of the increase in global ozone-related mortality). For PM2.5, we estimate 55,600 (-34,300 to 164,000) deaths in 2030 and 215,000 (-76,100 to 595,000) in 2100 (countering by 16% the global decrease in PM2.5-related mortality). Premature mortality attributable to climate change is estimated to be positive in all regions except Africa, and is greatest in India and East Asia. Most individual models yield increased mortality from climate change, but some yield decreases, suggesting caution in interpreting results from a single model. Climate change mitigation will likely reduce air pollution-related mortality

Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period

The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolving discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields was analyzed and aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz (Laboratoire de Meteorologie Dynamique) with a standard CH4 emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8:7*10^5 and 12:8*10^5 molec cm-3. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and volatile organic compound (VOC) chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0:3*10^5 molec cm-3 in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in the CH4 mixing ratio in 2010, which represents 7%–20% of the model-simulated CH4 increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH4 mixing ratio uncertainty of > 30 ppbv. Over the full 2000–2016 time period, using a common stateof- the-art but nonoptimized emission scenario, the impact of [OH] changes tested here can explain up to 54% of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH4 forward simulations and emission optimizations performed by atmospheric inversions

Analysis of present day and future OH and methane lifetime in the ACCMIP simulations

Results from simulations performed for the Atmospheric Chemistry and Climate Modeling Intercomparison Project (ACCMIP) are analysed to examine how OH and methane lifetime may change from present day to the future, under different climate and emissions scenarios. Present day (2000) mean tropospheric chemical lifetime derived from the ACCMIP multi-model mean is 9.8 ± 1.6 yr (9.3 ± 0.9 yr when only including selected models), lower than a recent observationally-based estimate, but with a similar range to previous multi-model estimates. Future model projections are based on the four Representative Concentration Pathways (RCPs), and the results also exhibit a large range. Decreases in global methane lifetime of 4.5 ± 9.1% are simulated for the scenario with lowest radiative forcing by 2100 (RCP 2.6), while increases of 8.5 ± 10.4% are simulated for the scenario with highest radiative forcing (RCP 8.5). In this scenario, the key driver of the evolution of OH and methane lifetime is methane itself, since its concentration more than doubles by 2100 and it consumes much of the OH that exists in the troposphere. Stratospheric ozone recovery, which drives tropospheric OH decreases through photolysis modifications, also plays a partial role. In the other scenarios, where methane changes are less drastic, the interplay between various competing drivers leads to smaller and more diverse OH and methane lifetime responses, which are difficult to attribute. For all scenarios, regional OH changes are even more variable, with the most robust feature being the large decreases over the remote oceans in RCP8.5. Through a regression analysis, we suggest that differences in emissions of non-methane volatile organic compounds and in the simulation of photolysis rates may be the main factors causing the differences in simulated present day OH and methane lifetime. Diversity in predicted changes between present day and future OH was found to be associated more strongly with differences in modelled temperature and stratospheric ozone changes. Finally, through perturbation experiments we calculated an OH feedback factor (<i>F</i>) of 1.24 from present day conditions (1.50 from 2100 RCP8.5 conditions) and a climate feedback on methane lifetime of 0.33 ± 0.13 yr K⁻¹, on average. Models that did not include interactive stratospheric ozone effects on photolysis showed a stronger sensitivity to climate, as they did not account for negative effects of climate-driven stratospheric ozone recovery on tropospheric OH, which would have partly offset the overall OH/methane lifetime response to climate change

Observation- and Model-Based Estimates of Particulate Dry Nitrogen Deposition to the Oceans

© Author(s) 2017. This is an Open Access article distributed under the Creative Commons Attribution License CC BY 3.0 ( https://creativecommons.org/licenses/by/3.0/ ). Published by Copernicus Publications on behalf of the European Geosciences Union.Anthropogenic nitrogen (N) emissions to the atmosphere have increased significantly the deposition of nitrate (NO3−) and ammonium (NH4+) to the surface waters of the open ocean, with potential impacts on marine productivity and the global carbon cycle. Global-scale understanding of the impacts of N deposition to the oceans is reliant on our ability to produce and validate models of nitrogen emission, atmospheric chemistry, transport and deposition. In this work,  ∼  2900 observations of aerosol NO3− and NH4+ concentrations, acquired from sampling aboard ships in the period 1995–2012, are used to assess the performance of modelled N concentration and deposition fields over the remote ocean. Three ocean regions (the eastern tropical North Atlantic, the northern Indian Ocean and northwest Pacific) were selected, in which the density and distribution of observational data were considered sufficient to provide effective comparison to model products. All of these study regions are affected by transport and deposition of mineral dust, which alters the deposition of N, due to uptake of nitrogen oxides (NOx) on mineral surfaces. Assessment of the impacts of atmospheric N deposition on the ocean requires atmospheric chemical transport models to report deposition fluxes; however, these fluxes cannot be measured over the ocean. Modelling studies such as the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), which only report deposition flux, are therefore very difficult to validate for dry deposition. Here, the available observational data were averaged over a 5° × 5° grid and compared to ACCMIP dry deposition fluxes (ModDep) of oxidised N (NOy) and reduced N (NHx) and to the following parameters from the Tracer Model 4 of the Environmental Chemical Processes Laboratory (TM4): ModDep for NOy, NHx and particulate NO3− and NH4+, and surface-level particulate NO3− and NH4+ concentrations. As a model ensemble, ACCMIP can be expected to be more robust than TM4, while TM4 gives access to speciated parameters (NO3− and NH4+) that are more relevant to the observed parameters and which are not available in ACCMIP. Dry deposition fluxes (CalDep) were calculated from the observed concentrations using estimates of dry deposition velocities. Model–observation ratios (RA, n), weighted by grid-cell area and number of observations, were used to assess the performance of the models. Comparison in the three study regions suggests that TM4 overestimates NO3− concentrations (RA, n =  1.4–2.9) and underestimates NH4+ concentrations (RA, n =  0.5–0.7), with spatial distributions in the tropical Atlantic and northern Indian Ocean not being reproduced by the model. In the case of NH4+ in the Indian Ocean, this discrepancy was probably due to seasonal biases in the sampling. Similar patterns were observed in the various comparisons of CalDep to ModDep (RA, n =  0.6–2.6 for NO3−, 0.6–3.1 for NH4+). Values of RA, n for NHx CalDep–ModDep comparisons were approximately double the corresponding values for NH4+ CalDep–ModDep comparisons due to the significant fraction of gas-phase NH3 deposition incorporated in the TM4 and ACCMIP NHx model products. All of the comparisons suffered due to the scarcity of observational data and the large uncertainty in dry deposition velocities used to derive deposition fluxes from concentrations. These uncertainties have been a major limitation on estimates of the flux of material to the oceans for several decades. Recommendations are made for improvements in N deposition estimation through changes in observations, modelling and model–observation comparison procedures. Validation of modelled dry deposition requires effective comparisons to observable aerosol-phase species' concentrations, and this cannot be achieved if model products only report dry deposition flux over the ocean.Peer reviewedFinal Published versio

Contrasting fast precipitation responses to tropospheric and stratospheric ozone forcing

The precipitation response to radiative forcing (RF) can be decomposed into a fast precipitation response (FPR), which depends on the atmospheric component of RF, and a slow response, which depends on surface temperature change. We present the first detailed climate model study of the FPR due to tropospheric and stratospheric ozone changes. The FPR depends strongly on the altitude of ozone change. Increases below about 3 km cause a positive FPR; increases above cause a negative FPR. The FPR due to stratospheric ozone change is, per unit RF, about 3 times larger than that due to tropospheric ozone. As historical ozone trends in the troposphere and stratosphere are opposite in sign, so too are the FPRs. Simple climate model calculations of the time-dependent total (fast and slow) precipitation change, indicate that ozone's contribution to precipitation change in 2011, compared to 1765, could exceed 50% of that due to CO2 change