ORCID Profile
0000-0001-8087-3976
Current Organisation
Environment and Climate Change Canada
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Publisher: Springer Science and Business Media LLC
Date: 03-07-2023
DOI: 10.1038/S41467-023-39559-2
Abstract: Water vapor plays an important role in many aspects of the climate system, by affecting radiation, cloud formation, atmospheric chemistry and dynamics. Even the low stratospheric water vapor content provides an important climate feedback, but current climate models show a substantial moist bias in the lowermost stratosphere. Here we report crucial sensitivity of the atmospheric circulation in the stratosphere and troposphere to the abundance of water vapor in the lowermost stratosphere. We show from a mechanistic climate model experiment and inter-model variability that lowermost stratospheric water vapor decreases local temperatures, and thereby causes an upward and poleward shift of subtropical jets, a strengthening of the stratospheric circulation, a poleward shift of the tropospheric eddy-driven jet and regional climate impacts. The mechanistic model experiment in combination with atmospheric observations further shows that the prevailing moist bias in current models is likely caused by the transport scheme, and can be alleviated by employing a less diffusive Lagrangian scheme. The related effects on atmospheric circulation are of similar magnitude as climate change effects. Hence, lowermost stratospheric water vapor exerts a first order effect on atmospheric circulation and improving its representation in models offers promising prospects for future research.
Publisher: Copernicus GmbH
Date: 26-04-2019
Abstract: Abstract. Transport from the Northern Hemisphere (NH) midlatitudes to the Arctic plays a crucial role in determining the abundance of trace gases and aerosols that are important to Arctic climate via impacts on radiation and chemistry. Here we examine this transport using an idealized tracer with a fixed lifetime and predominantly midlatitude land-based sources in models participating in the Chemistry Climate Model Initiative (CCMI). We show that there is a 25 %–45 % difference in the Arctic concentrations of this tracer among the models. This spread is correlated with the spread in the location of the Pacific jet, as well as the spread in the location of the Hadley Cell (HC) edge, which varies consistently with jet latitude. Our results suggest that it is likely that the HC-related zonal-mean meridional transport rather than the jet-related eddy mixing is the major contributor to the inter-model spread in the transport of land-based tracers into the Arctic. Specifically, in models with a more northern jet, the HC generally extends further north and the tracer source region is mostly covered by surface southward flow associated with the lower branch of the HC, resulting in less efficient transport poleward to the Arctic. During boreal summer, there are poleward biases in jet location in free-running models, and these models likely underestimate the rate of transport into the Arctic. Models using specified dynamics do not have biases in the jet location, but do have biases in the surface meridional flow, which may result in differences in transport into the Arctic. In addition to the land-based tracer, the midlatitude-to-Arctic transport is further examined by another idealized tracer with zonally uniform sources. With equal sources from both land and ocean, the inter-model spread of this zonally uniform tracer is more related to variations in parameterized convection over oceans rather than variations in HC extent, particularly during boreal winter. This suggests that transport of land-based and oceanic tracers or aerosols towards the Arctic differs in pathways and therefore their corresponding inter-model variabilities result from different physical processes.
Publisher: Copernicus GmbH
Date: 13-02-2017
Abstract: Abstract. We present an overview of state-of-the-art chemistry–climate and chemistry transport models that are used within phase 1 of the Chemistry–Climate Model Initiative (CCMI-1). The CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models' projections of the stratospheric ozone layer, tropospheric composition, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI-1 recommendations for simulations have been followed is necessary to understand model responses to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI-1 simulations with the aim of informing CCMI data users.
Publisher: Copernicus GmbH
Date: 13-11-2019
DOI: 10.5194/ACP-19-13701-2019
Abstract: Abstract. 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 8.7×105 and 12.8×105 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 0.1–0.3×105 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 state-of-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.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-2768
Abstract: & & One of the key questions in the air quality and climate sciences is how will tropospheric ozone concentrations change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers implemented in the models to examine changes in transport. We find that the strengthening of the shallow branch of the Brewer-Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere lead to enhanced STT in the subtropics. The acceleration of the deep branch of the BDC in the NH and changes in eddy transport contribute to increase STT at high latitudes. In the SH, the deep branch does not accelerate due to the dynamical effects of the ozone hole recovery.& &
Publisher: Copernicus GmbH
Date: 20-12-2017
Abstract: Abstract. Stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry-climate models (CCMs). Compared to observational estimates simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both, mean transport along the residual circulation and two-way mixing we quantify the effects of these processes using data from the CCM inter-comparison projects CCMVal-2 and CCMI-1. Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread in AoA between the models is primarily caused by differences in the effects of mixing, and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase of AoA by mixing. The mixing efficiency varies strongly between the models from 0.21 to 0.99. We show that the mixing efficiency is not only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parametrized wave forcing do not appear to be related to differences in mixing efficiency or AoA.
Publisher: Copernicus GmbH
Date: 07-2020
Publisher: Copernicus GmbH
Date: 06-02-2018
Publisher: Copernicus GmbH
Date: 27-06-2019
DOI: 10.5194/ACP-2019-581
Abstract: Abstract. One of the key questions in the air quality and climate sciences is how will tropospheric ozone concentrations change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers to isolate changes in transport: stratospheric ozone (O3S), which is exactly like ozone but has no chemical sources in the troposphere, and st80, a passive tracer with fixed volume mixing ratio in the stratosphere. We find a robust increase in the tropospheric columns of these two tracers across the models. In particular, stratospheric ozone in the troposphere is projected to increase 10–16 % by the end of the 21st century in the RCP6.0 scenario. Future STT is enhanced in the subtropics due to the strengthening of the shallow branch of the Brewer-Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere. The acceleration of the deep branch of the BDC and changes in eddy transport contribute to increase STT at high latitudes. The idealized tracer st80 shows that these STT changes are dominated by greenhouse gas (GHG) increases, while phasing out of ozone depleting substances (ODS) does not lead to robust STT changes. Nevertheless, the increase of O3S concentrations in the troposphere is attributed to GHG only in the subtropics. At middle and high latitudes it is due to stratospheric ozone recovery linked to ODS decline. A higher emission scenario (RCP8.5) produces qualitatively similar but stronger STT trends, with changes in tropospheric column O3S more than three times larger than those in the RCP6.0 scenario by the end of the 21st century.
Publisher: Copernicus GmbH
Date: 29-01-2018
Abstract: Abstract. Ozone fields simulated for the first phase of the Chemistry-Climate Model Initiative (CCMI-1) will be used as forcing data in the 6th Coupled Model Intercomparison Project. Here we assess, using reference and sensitivity simulations produced for CCMI-1, the suitability of CCMI-1 model results for this process, investigating the degree of consistency amongst models regarding their responses to variations in in idual forcings. We consider the influences of methane, nitrous oxide, a combination of chlorinated or brominated ozone-depleting substances, and a combination of carbon dioxide and other greenhouse gases. We find varying degrees of consistency in the models' responses in ozone to these in idual forcings, including some considerable disagreement. In particular, the response of total-column ozone to these forcings is less consistent across the multi-model ensemble than profile comparisons. We analyse how stratospheric age of air, a commonly used diagnostic of stratospheric transport, responds to the forcings. For this diagnostic we find some salient differences in model behaviour, which may explain some of the findings for ozone. The findings imply that the ozone fields derived from CCMI-1 are subject to considerable uncertainties regarding the impacts of these anthropogenic forcings. We offer some thoughts on how to best approach the problem of generating a consensus ozone database from a multi-model ensemble such as CCMI-1.
Publisher: Copernicus GmbH
Date: 24-01-2019
Abstract: Abstract. Climate models consistently predict an acceleration of the Brewer–Dobson circulation (BDC) due to climate change in the 21st century. However, the strength of this acceleration varies considerably among in idual models, which constitutes a notable source of uncertainty for future climate projections. To shed more light upon the magnitude of this uncertainty and on its causes, we analyse the stratospheric mean age of air (AoA) of 10 climate projection simulations from the Chemistry-Climate Model Initiative phase 1 (CCMI-I), covering the period between 1960 and 2100. In agreement with previous multi-model studies, we find a large model spread in the magnitude of the AoA trend over the simulation period. Differences between future and past AoA are found to be predominantly due to differences in mixing (reduced aging by mixing and recirculation) rather than differences in residual mean transport. We furthermore analyse the mixing efficiency, a measure of the relative strength of mixing for given residual mean transport, which was previously hypothesised to be a model constant. Here, the mixing efficiency is found to vary not only across models, but also over time in all models. Changes in mixing efficiency are shown to be closely related to changes in AoA and quantified to roughly contribute 10 % to the long-term AoA decrease over the 21st century. Additionally, mixing efficiency variations are shown to considerably enhance model spread in AoA changes. To understand these mixing efficiency variations, we also present a consistent dynamical framework based on diffusive closure, which highlights the role of basic state potential vorticity gradients in controlling mixing efficiency and therefore aging by mixing.
Publisher: Copernicus GmbH
Date: 04-2019
DOI: 10.5194/ACP-2019-281
Abstract: Abstract. The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolve discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields were analysed and were aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz 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 × 105 and 12.8 × 105 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 VOC chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0.3 × 105 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 CH4 mixing ratio in 2010, which represent 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 state-of-the-art but non-optimized 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.
Publisher: Copernicus GmbH
Date: 30-08-2018
DOI: 10.5194/ACP-2018-841
Abstract: Abstract. Transport from the Northern Hemisphere (NH) midlatitudes to the Arctic plays a crucial role in determining the abundance of trace gases and aerosols that are important to Arctic climate via impacts on radiation and chemistry. Here we examine this transport using an idealized tracer with fixed lifetime and predominantly midlatitude land-based sources in models participating in the Chemistry Climate Model Initiative (CCMI). We show that there is a 20 %–40 % difference in the Arctic concentrations of this tracer among the models. This spread is found to be generally related to the spread in location of the Pacific jet, with lower Arctic tracer concentrations occurring in models with a more northern jet, during both winter and summer. However, the underlying mechanism for this relationship does not involve the jet directly, but instead involves differences in the surface meridional flow over the tracer source region, that vary with jet latitude. Specifically, in models with a more northern jet, the Hadley Cell (HC) generally extends further north and the tracer source region is mostly covered by surface southward flow associated with the lower branch of the HC, resulting in less efficient transport poleward to the Arctic. During boreal summer, there are poleward biases in jet location in free-running models, and these models likely underestimate the rate of transport into the Arctic. Models using specified dynamics do not have biases in the jet location, but do have biases in the surface meridional flow, which results in differences in the transport into the Arctic. In addition to the land-based tracer, the midlatitude-to-Arctic transport is further examined by another idealized tracer with zonally uniform sources. With equal sources from lands and oceans, the intermodel spread of this zonally uniform tracer is more related to variations of parameterized convection over oceans than variations of HC extent particularly during boreal summer. This suggests that transport of land-based and oceanic tracers or aerosols towards the Arctic differ in pathways and therefore their corresponding intermodel variabilities result from different physical processes.
Publisher: Copernicus GmbH
Date: 14-05-2018
Abstract: Abstract. The stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry–climate models (CCMs). Compared to observational estimates, simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both mean transport by the residual circulation and two-way mixing we quantify the effects of these processes using data from the CCM inter-comparison projects CCMVal-2 (Chemistry–Climate Model Validation Activity 2) and CCMI-1 (Chemistry–Climate Model Initiative, phase 1). Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread in AoA between the models is primarily caused by differences in the effects of mixing and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase in AoA by mixing. The mixing efficiency varies strongly between the models from 0.24 to 1.02. We show that the mixing efficiency is not only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid-scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parameterized wave forcing do not appear to be related to differences in mixing efficiency or AoA.
Publisher: Copernicus GmbH
Date: 04-2019
Publisher: Copernicus GmbH
Date: 26-03-2018
DOI: 10.5194/ACP-2018-296
Abstract: Abstract. Major mid-winter stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Because SSWs are able to cause significant surface weather anomalies on intra-seasonal time scales, several previous studies have focused on their potential future change, as might be induced by anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to an actual decrease. Several factors might explain these contradictory results, notably the use of different metrics for the identification of SSWs, and the impact of large climatological biases in single-model studies. To bring some clarity, we here revisit the question of future SSWs changes, using an identical set of metrics applied consistently across 12 different models participating in the Chemistry Climate Model Initiative. Our analysis reveals that no statistically significant change in the frequency of SSWs will occur over the 21st century, irrespective of the metric used for the identification of the event. Changes in other SSWs characteristics, such as their duration and the tropospheric forcing, are also assessed: again, we find no evidence of future changes over the 21st century.
Publisher: Copernicus GmbH
Date: 13-08-2018
DOI: 10.5194/ACP-18-11277-2018
Abstract: Abstract. Major mid-winter stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Because SSWs are able to cause significant surface weather anomalies on intra-seasonal timescales, several previous studies have focused on their potential future change, as might be induced by anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to an actual decrease. Several factors might explain these contradictory results, notably the use of different metrics for the identification of SSWs and the impact of large climatological biases in single-model studies. To bring some clarity, we here revisit the question of future SSW changes, using an identical set of metrics applied consistently across 12 different models participating in the Chemistry–Climate Model Initiative. Our analysis reveals that no statistically significant change in the frequency of SSWs will occur over the 21st century, irrespective of the metric used for the identification of the event. Changes in other SSW characteristics – such as their duration, deceleration of the polar night jet, and the tropospheric forcing – are also assessed: again, we find no evidence of future changes over the 21st century.
Publisher: Copernicus GmbH
Date: 25-05-2018
Abstract: Abstract. Understanding and modeling the large-scale transport of trace gases and aerosols is important for interpreting past (and projecting future) changes in atmospheric composition. Here we show that there are large differences in the global-scale atmospheric transport properties among the models participating in the IGAC SPARC Chemistry–Climate Model Initiative (CCMI). Specifically, we find up to 40 % differences in the transport timescales connecting the Northern Hemisphere (NH) midlatitude surface to the Arctic and to Southern Hemisphere high latitudes, where the mean age ranges between 1.7 and 2.6 years. We show that these differences are related to large differences in vertical transport among the simulations, in particular to differences in parameterized convection over the oceans. While stronger convection over NH midlatitudes is associated with slower transport to the Arctic, stronger convection in the tropics and subtropics is associated with faster interhemispheric transport. We also show that the differences among simulations constrained with fields derived from the same reanalysis products are as large as (and in some cases larger than) the differences among free-running simulations, most likely due to larger differences in parameterized convection. Our results indicate that care must be taken when using simulations constrained with analyzed winds to interpret the influence of meteorology on tropospheric composition.
Publisher: Copernicus GmbH
Date: 06-02-2018
DOI: 10.5194/ACP-2018-87
Abstract: Abstract. 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 (±20 DU 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 2047 (with a 1-σ uncertainty of 2042–2052). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2046 (2042–2050), and at Northern Hemisphere mid-latitudes in 2034 (2024–2044). In the polar regions, the return dates are 2062 (2055–2066) in the Antarctic in October and 2035 (2025–2040) in the Arctic in March. The earlier return dates in the NH reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–15 years, depending on the region. In the tropics only around half the models predict a return to 1980 values, at around 2040, while the other half do not reach this 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, which is the main driver 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, 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, the effect in the simulations analysed here is small and at the limit of detectability from 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 changes ozone return by ~ 15 years, again mainly through its impact in the tropics. 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 is more important to have multi-member (at least 3) ensembles for each scenario from each established participating model, rather than a large number of in idual models.
Publisher: Copernicus GmbH
Date: 30-08-2018
Publisher: Copernicus GmbH
Date: 14-09-2016
DOI: 10.5194/GMD-2016-199
Abstract: Abstract. We present an overview of state-of-the-art chemistry-climate and -transport models that are used within the Chemistry-Climate Model Initiative (CCMI). CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models' projections of the stratospheric ozone layer, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI recommendations for simulations have been followed is necessary to understand model response to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI simulations with the aim to inform CCMI data users.
Publisher: Copernicus GmbH
Date: 30-11-2017
Publisher: Copernicus GmbH
Date: 30-11-2017
Abstract: Abstract. Understanding and modeling the large-scale transport of trace gases and aerosols is important for interpreting past (and projecting future) changes in atmospheric composition. Here we show that there are large differences in the global-scale atmospheric transport properties among models participating in the IGAC SPARC Chemistry-Climate Model Initiative (CCMI). Specifically, we find up to 40 % differences in the transport timescales connecting the Northern Hemisphere (NH) midlatitude surface to the Arctic and to Southern Hemisphere high latitudes, where the mean age ranges between 1.7 years and 2.6 years. We show that these differences are related to large differences in vertical transport among the simulations and, in particular, to differences in parameterized convection over the oceans. While stronger convection over NH midlatitudes is associated with slower transport to the Arctic, stronger convection in the tropics and subtropics is associated with faster interhemispheric transport. We also show that the differences among simulations constrained with fields derived from the same reanalysis products are as large as (and, in some cases, larger than) the differences among free-running simulations, due to larger differences in parameterized convection. Our results indicate that care must be taken when using simulations constrained with analyzed winds to interpret the influence of meteorology on tropospheric composition.
Publisher: Copernicus GmbH
Date: 11-06-2020
Abstract: Abstract. One of the key questions in the air quality and climate sciences is how tropospheric ozone concentrations will change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers to isolate changes in transport: stratospheric ozone (O3S), which is exactly like ozone but has no chemical sources in the troposphere, and st80, a passive tracer with fixed volume mixing ratio in the stratosphere. We find a robust increase in the tropospheric columns of these two tracers across the models. In particular, stratospheric ozone in the troposphere is projected to increase 10 %–16 % by the end of the 21st century in the RCP6.0 scenario. Future STT is enhanced in the subtropics due to the strengthening of the shallow branch of the Brewer–Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere. The acceleration of the deep branch of the BDC in the Northern Hemisphere (NH) and changes in eddy transport contribute to increased STT at high latitudes. These STT trends are caused by greenhouse gas (GHG) increases, while phasing out of ozone-depleting substances (ODS) does not lead to robust transport changes. Nevertheless, the decline of ODS increases the reservoir of ozone in the lower stratosphere, which results in enhanced STT of O3S at middle and high latitudes. A higher emission scenario (RCP8.5) produces stronger STT trends, with increases in tropospheric column O3S more than 3 times larger than those in the RCP6.0 scenario by the end of the 21st century.
Publisher: Copernicus GmbH
Date: 14-09-2016
Publisher: American Geophysical Union (AGU)
Date: 02-07-2019
DOI: 10.1029/2018JD029516
Publisher: Copernicus GmbH
Date: 26-06-2018
Publisher: Copernicus GmbH
Date: 15-06-2018
Abstract: Abstract. 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 (±20 DU 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 in idual models.
Publisher: Copernicus GmbH
Date: 26-06-2018
DOI: 10.5194/ACP-2018-615
Abstract: Abstract. Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40–50 % in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~ 30 % in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU – approximately 33 % larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, SOCOLv3.1, which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx, carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90 % of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20 % more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.
Publisher: Copernicus GmbH
Date: 20-08-2013
Abstract: Abstract. We present multi-model global datasets of nitrogen and sulfate deposition covering time periods from 1850 to 2100, calculated within the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The computed deposition fluxes are compared to surface wet deposition and ice core measurements. We use a new dataset of wet deposition for 2000–2002 based on critical assessment of the quality of existing regional network data. We show that for present day (year 2000 ACCMIP time slice), the ACCMIP results perform similarly to previously published multi-model assessments. For this time slice, we find a multi-model mean deposition of approximately 50 Tg(N) yr−1 from nitrogen oxide emissions, 60 Tg(N) yr−1 from ammonia emissions, and 83 Tg(S) yr−1 from sulfur emissions. The analysis of changes between 1980 and 2000 indicates significant differences between model and measurements over the United States but less so over Europe. This difference points towards a potential misrepresentation of 1980 NH3 emissions over North America. Based on ice core records, the 1850 deposition fluxes agree well with Greenland ice cores, but the change between 1850 and 2000 seems to be overestimated in the Northern Hemisphere for both nitrogen and sulfur species. Using the Representative Concentration Pathways (RCPs) to define the projected climate and atmospheric chemistry related emissions and concentrations, we find large regional nitrogen deposition increases in 2100 in Latin America, Africa and parts of Asia under some of the scenarios considered. Increases in South Asia are especially large, and are seen in all scenarios, with 2100 values more than double their 2000 counterpart in some scenarios and reaching 1300 mg(N) m−2 yr−1 averaged over regional to continental-scale regions in RCP 2.6 and 8.5, ~ 30–50% larger than the values in any region currently (circa 2000). However, sulfur deposition rates in 2100 are in all regions lower than in 2000 in all the RCPs. The new ACCMIP multi-model deposition dataset provides state-of-the-science, consistent and evaluated time slice (spanning 1850–2100) global gridded deposition fields for use in a wide range of climate and ecological studies.
Publisher: Copernicus GmbH
Date: 25-10-2018
Publisher: Copernicus GmbH
Date: 25-10-2018
Abstract: Abstract. Climate models consistently predict an acceleration of the Brewer-Dobson circulation (BDC) due to climate change in the 21st century. However, the strength of this acceleration varies considerably among in idual models, which constitutes a notable source of uncertainty for future climate projections. To shed more light upon the magnitude of this uncertainty and on its causes, we analyze the stratospheric mean age of air (AoA) of 10 climate projection simulations from the Chemistry Climate Model Initiative phase 1 (CCMI-I), covering the period between 1960 and 2100. In agreement with previous multi-model studies, we find a large model spread in the magnitude of the AoA trend over the simulation period. Differences between future and past AoA are found to be predominantly due to differences in mixing (reduced aging by mixing and recirculation) rather than differences in residual mean transport. We furthermore analyze the mixing efficiency, a measure of the relative strength of mixing for given residual mean transport, which was previously hypothesized to be a model constant. Here, the mixing efficiency is found to vary not only across models, but also over time in all models. Changes in mixing efficiency are shown to be closely related to changes in AoA and quantified to roughly contribute 10 % to the long-term AoA decrease over the 21st century. Additionally, mixing efficiency variations are shown to considerably enhance model spread in AoA changes. To understand these mixing efficiency variations, we also present a consistent dynamical framework based on diffusive closure, which highlights the role of basic state potential vorticity gradients in controlling mixing efficiency and therefore aging by mixing.
Publisher: Copernicus GmbH
Date: 26-03-2018
Publisher: Copernicus GmbH
Date: 07-2020
DOI: 10.5194/ACP-2020-525
Abstract: Abstract. Ozone profile measurements collected at L'Aquila (Italy, 42.4° N) during seventeen years of radio-sounding (2000–2016) are presented here, with an analysis of derived trends. Model results from the SPARC-CCMI exercise are used in parallel to highlight the physical and chemical mechanisms regulating mid-latitude ozone trends. The statistically significant trends highlighted in time series at L'Aquila are those in the mid-upper stratosphere (+5.9 ± 4.2), mid troposphere (+5.9 ± 2.4) and upper troposphere (+2.5 ± 0.9), all in percent/decade. The upper stratospheric positive trend was already well documented in recent WMO assessments and attributed to the starting decline of stratospheric Cly and Bry and to the stratospheric cooling induced by increasing well mixed greenhouse gases, thus slowing down gas-phase reactions that destroy ozone in the upper stratosphere. The ozone increase in the mid-upper troposphere is largely regulated by the increasing strength of the Brewer-Dobson circulation, which moves more ozone from the tropics to the extratropics and enhances the tropospheric influx from the lowermost stratosphere. This climate feedback mechanism on tropospheric ozone is only partially compensated by the increasing chemical ozone loss associated to higher H2O values in response to the tropospheric warming. We also note that ozone trends obtained in the lower stratosphere are negative (−2.2 percent/decade), but do not result to be statistically significant in our analyses.
Publisher: American Geophysical Union (AGU)
Date: 27-05-2018
DOI: 10.1029/2017JD027978
Publisher: Copernicus GmbH
Date: 13-11-2018
DOI: 10.5194/ACP-18-16155-2018
Abstract: Abstract. Previous multi-model intercomparisons have shown that chemistry–climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC–IGAC (Stratosphere–troposphere Processes And their Role in Climate–International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40 %–50 % in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ∼30 % in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU – approximately 33 % larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, “SOCOLv3.1”, which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx), carbon monoxide, methane and other volatile organic compounds, VOCs) are responsible for more than 90 % of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20 % more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry–climate models.
No related grants have been discovered for David Plummer.