Chemistry-Climate Model Initiative Research Articles

Weakening of springtime Arctic ozone depletion with climate change

Abstract. In the Arctic stratosphere, the combination of chemical ozone depletion by halogenated ozone-depleting substances (hODSs) and dynamic fluctuations can lead to severe ozone minima. These Arctic ozone minima are of great societal concern due to their health and climate impacts. Owing to the success of the Montreal Protocol, hODSs in the stratosphere are gradually declining, resulting in a recovery of the ozone layer. On the other hand, continued greenhouse gas (GHG) emissions cool the stratosphere, possibly enhancing the formation of polar stratospheric clouds (PSCs) and, thus, enabling more efficient chemical ozone destruction. Other processes, such as the acceleration of the Brewer–Dobson circulation, also affect stratospheric temperatures, further complicating the picture. Therefore, it is currently unclear whether major Arctic ozone minima will still occur at the end of the 21st century despite decreasing hODSs. We have examined this question for different emission pathways using simulations conducted within the Chemistry-Climate Model Initiative (CCMI-1 and CCMI-2022) and found large differences in the models' ability to simulate the magnitude of ozone minima in the present-day climate. Models with a generally too-cold polar stratosphere (cold bias) produce pronounced ozone minima under present-day climate conditions because they simulate more PSCs and, thus, high concentrations of active chlorine species (ClOx). These models predict the largest decrease in ozone minima in the future. Conversely, models with a warm polar stratosphere (warm bias) have the smallest sensitivity of ozone minima to future changes in hODS and GHG concentrations. As a result, the scatter among models in terms of the magnitude of Arctic spring ozone minima will decrease in the future. Overall, these results suggest that Arctic ozone minima will become weaker over the next decades, largely due to the decline in hODS abundances. We note that none of the models analysed here project a notable increase of ozone minima in the future. Stratospheric cooling caused by increasing GHG concentrations is expected to play a secondary role as its effect in the Arctic stratosphere is weakened by opposing radiative and dynamical mechanisms.

Changes in Stratosphere‐Troposphere Exchange of Air Mass and Ozone Concentration in CCMI Models From 1960 to 2099

AbstractThis study investigates changes in stratosphere‐troposphere exchange (STE) of air masses and ozone concentrations from 1960 to 2099 using multiple model simulations from Chemistry Climate Model Initiative (CCMI) under climate change scenario RCP6.0. We employ a lowermost stratosphere mass budget approach with dynamic isentropic surfaces fitted to the tropical tropopause as the upper boundary of lowermost stratosphere. The multi‐model mean (MMM) trends of air mass STEs are all small over all regions, which are within 0.3 (0.1) % decade−1 for 1960–2000 (2000–2099). The MMM trends of ozone STE for 1960–2000 are 0.3%, −2.7%, 3.4%, −0.9%, and −2.7% decade−1 over the Northern hemisphere (NH) extratropics, Southern hemisphere (SH) extratropics, tropics, extratropics, and globe, respectively. The corresponding ozone STE trends for 2000–2099 are 3.0%, 4.3%, 0.8%, 3.5%, and 4.7% decade−1. Changes in ozone STEs are dominated by ozone concentration changes, driven by climate‐induced changes and ozone‐depleting substance (ODS) changes. For 1960–2000, small changes in ozone STEs in the NH extratropics are due to a cancellation between effects of climate‐induced changes and ODS increases, while the ODS effect dominates in the SH extratropics, leading to a large ozone STE magnitude decrease. Increased ozone transport from tropical troposphere to stratosphere for 1960–2000 is due to increased tropospheric ozone. A decreased global ozone STE magnitude for 1960–2000 was largely caused by ODS‐induced ozone loss that is partly compensated by climate‐induced ozone changes. For 2000–2099, about two‐thirds of global ozone STE magnitude increases are caused by ozone increases in the extratropical lower stratosphere due to climate‐induced changes. The remaining one‐third is caused by ozone recovery due to the phaseout of ODS.

Exploring the link between austral stratospheric polar vortex anomalies and surface climate in chemistry-climate models

Abstract. Extreme events in the stratospheric polar vortex can lead to changes in the tropospheric circulation and impact the surface climate on a wide range of timescales. The austral stratospheric vortex shows its largest variability in spring, and a weakened polar vortex is associated with changes in the spring to summer surface climate, including hot and dry extremes in Australia. However, the robustness and extent of the connection between polar vortex strength and surface climate on interannual timescales remain unclear. We assess this relationship by using reanalysis data and time-slice simulations from two chemistry-climate models (CCMs), building on previous work that is mainly based on observations. The CCMs show a similar downward propagation of anomalies in the polar vortex strength to the reanalysis data: a weak polar vortex is on average followed by a negative tropospheric Southern Annular Mode (SAM) in spring to summer, while a strong polar vortex is on average followed by a positive SAM. The signature in the surface climate following polar vortex weakenings is characterized by high surface pressure and warm temperature anomalies over Antarctica, the region where surface signals are most robust across all model and observational datasets. However, the tropospheric SAM response in the two CCMs considered is inconsistent with observations. In one CCM, the SAM is more negative compared to the reanalysis after weak polar vortex events, whereas in the other CCM, it is less negative. In addition, neither model reproduces all the regional changes in midlatitudes, such as the warm and dry anomalies over Australia. We find that these inconsistencies are linked to model biases in the basic state, such as the latitude of the eddy-driven jet and the persistence of the SAM. These results are largely corroborated by models that participated in the Chemistry-Climate Model Initiative (CCMI). Furthermore, bootstrapping of the data reveals sizable uncertainty in the magnitude of the surface signals in both models and observations due to internal variability. Our results demonstrate that anomalies of the austral stratospheric vortex have significant impacts on surface climate, although the ability of models to capture regional effects across the Southern Hemisphere is limited by biases in their representation of the stratospheric and tropospheric circulation.

Surface ozone interannual variability, trends, and extremes in CCMI models

Tropospheric ozone exhibits variations at interannual to decadal timescales tied closely to climate variability. The fidelity of current-generation global chemistry-climate models (CCMs) in representing observed variations and extremes of troposphere ozone is an important aspect of model abilities to predict ozone but has not been comprehensively assessed. Here we evaluate multiple state-of-the-art CCMs participating in the Chemistry-Climate Model Initiative phase 1 (CCMI-1) in simulating the interannual variability (IAV), decadal trends, and extremes of surface ozone during 1990–2010 at northern mid-latitudes. We find that current CCMs tend to underestimate observed ozone IAV over the U.S., Europe, and East Asia in both spring and summer and fail to capture the springtime ozone trends in the eastern U.S. (EUS) and Europe. Specified-dynamics simulations constrained by reanalysis data outperform the free-running simulations with CCMs in terms of IAV and decadal trends of surface ozone. Observations show large surface ozone IAV in summer over the EUS, Europe, and East Asia (standard deviation = 3−6 ppbv or relative standard deviation = 8−15%) while all models substantially underestimate observed IAV by a factor of 2–3. Analysis in these populous/polluted regions unveils that the models significantly underestimate ozone extreme anomalies during heatwaves/droughts by 2–9 ppbv (17–55%) over the EUS and by 7–10 ppbv (42–59%) over Europe during the past two decades. These underestimates in summertime extreme anomalies contribute to the model biases in surface ozone IAV. Future model developments are needed to improve the ability of current CCMs to predict ozone extremes during heatwaves and droughts.

CCMI-1 모형에서의 동아시아 전량오존 및 대류권오존 평가

We assess Total Column Ozone (TCO) and Tropospheric Ozone (TRO) reproduced by the Chemistry Climate Model Initiative phase 1 (CCMI-1) project using ground-based observations and reanalysis datasets from 2003 to 2010 for East Asia. Five models, CESM1-WACCM, CESM1-CAM4Chem, HadGEM3-ES, MRI-CGCM3, and GEOSCCM, are evaluated. The multi-model mean of the five models reproduces a spatial distribution and seasonal cycle of TCO similar to the observation and reanalysis results. CCMI-1 models present a relative bias of TCO in the range of -4.22 to 0.47% against the observation data. Contrary to TCO, TRO shows a comparatively large inter-model spread in spatial pattern and seasonality. Indeed, the relative bias of TRO from the models is from -8.37 to 13.71% compared to the observation data. While CESM1-CAM4Chem and MRI-ESM1r1 produce reasonable spatial and temporal variability of TCO and TRO, HadGEM3-ES and GEOSCCM exhibit a bias with substantially lower and higher TRO in most seasons, respectively. Long-term trends in TCO and TRO are not significant in either reanalysis or CCMI models. These results suggest that TCO and TRO reproduction from CCMI-1 models requires caution and further evaluation, particularly for regional analysis.

Interpol-IAGOS: a new method for assessing long-term chemistry–climate simulations in the UTLS based on IAGOS data, and its application to the MOCAGE CCMI REF-C1SD simulation

Abstract. A wide variety of observation data sets are used to assess long-term simulations provided by chemistry–climate models (CCMs) and chemistry-transport models (CTMs). However, the upper troposphere–lower stratosphere (UTLS) has hardly been assessed in these modelling exercises yet. Observations performed in the framework of IAGOS (In-service Aircraft for a Global Observing System) combine the advantages of in situ airborne measurements in the UTLS with an almost-global-scale sampling, a ∼20-year monitoring period and a high frequency. Even though a few model assessments have been made using the IAGOS database, none of them took advantage of the dense and high-resolution cruise data in their whole ensemble yet. The present study proposes a method to compare this large IAGOS data set to long-term simulations used for chemistry–climate studies. As a first application, the REF-C1SD reference simulation generated by the MOCAGE (MOdèle de Chimie Atmosphérique à Grande Echelle) CTM in the framework of Chemistry-Climate Model Initiative (CCMI) phase I has been evaluated during the 1994–2013 period for ozone (O3) and the 2002–2013 period for carbon monoxide (CO). The concept of the new comparison software proposed here (so-called Interpol-IAGOS) is to project all IAGOS data onto the 3-D grid of the model with a monthly resolution, since generally the 3-D outputs provided by chemistry–climate models for multi-model comparisons on multi-decadal timescales are archived as monthly means. This provides a new IAGOS data set (IAGOS-DM) mapped onto the model's grid and time resolution. To get a model data set consistent with IAGOS-DM for the comparison, a subset of the model's outputs is created (MOCAGE-M) by applying a mask that retains only the model data at the available IAGOS-DM grid points. Climatologies are derived from the IAGOS-DM product, and good correlations are reported between with the MOCAGE-M spatial distributions. As an attempt to analyse MOCAGE-M behaviour in the upper troposphere (UT) and the lower stratosphere (LS) separately, UT and LS data in IAGOS-DM were sorted according to potential vorticity. From this, we derived O3 and CO seasonal cycles in eight regions well sampled by IAGOS flights in the northern midlatitudes. They are remarkably well reproduced by the model for lower-stratospheric O3 and also good for upper-tropospheric CO. Along this model evaluation, we also assess the differences caused by the use of a weighting function in the method when projecting the IAGOS data onto the model grid compared to the scores derived in a simplified way. We conclude that the data projection onto the model's grid allows us to filter out biases arising from either spatial or temporal resolution, and the use of a weighting function yields different results, here by enhancing the assessment scores. Beyond the MOCAGE REF-C1SD evaluation presented in this paper, the method could be used by CCMI models for individual assessments in the UTLS and for model intercomparisons with respect to the IAGOS data set.

Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers

Abstract. The hydroxyl radical (OH) is the primary atmospheric oxidant responsible for removing many important trace gases, including methane, from the atmosphere. Although robust relationships between OH drivers and modes of climate variability have been shown, the underlying mechanisms between OH and these climate modes, such as the El Niño–Southern Oscillation (ENSO), have not been thoroughly investigated. Here, we use a chemical transport model to perform a 38 year simulation of atmospheric chemistry, in conjunction with satellite observations, to understand the relationship between tropospheric OH and ENSO, Northern Hemispheric modes of variability, the Indian Ocean Dipole, and monsoons. Empirical orthogonal function (EOF) and regression analyses show that ENSO is the dominant mode of global OH variability in the tropospheric column and upper troposphere, responsible for approximately 30 % of the total variance in boreal winter. Reductions in OH due to El Niño are centered over the tropical Pacific and Australia and can be as high as 10 %–15 % in the tropospheric column. The relationship between ENSO and OH is driven by changes in nitrogen oxides in the upper troposphere and changes in water vapor and O1D in the lower troposphere. While the correlations between monsoons or other modes of variability and OH span smaller spatial scales than for ENSO, regional changes in OH can be significantly larger than those caused by ENSO. Similar relationships occur in multiple models that participated in the Chemistry–Climate Model Initiative (CCMI), suggesting that the dependence of OH interannual variability on these well-known modes of climate variability is robust. Finally, the spatial pattern and r2 values of correlation between ENSO and modeled OH drivers – such as carbon monoxide, water vapor, lightning, and, to a lesser extent, NO2 – closely agree with satellite observations. The ability of satellite products to capture the relationship between OH drivers and ENSO provides an avenue to an indirect OH observation strategy and new constraints on OH variability.

Influence of the El Niño–Southern Oscillation on entry stratospheric water vapor in coupled chemistry–ocean CCMI and CMIP6 models

Abstract. The connection between the dominant mode of interannual variability in the tropical troposphere, the El Niño–Southern Oscillation (ENSO), and the entry of stratospheric water vapor is analyzed in a set of model simulations archived for the Chemistry-Climate Model Initiative (CCMI) project and for Phase 6 of the Coupled Model Intercomparison Project. While the models agree on the temperature response to ENSO in the tropical troposphere and lower stratosphere, and all models and observations also agree on the zonal structure of the temperature response in the tropical tropopause layer, the only aspect of the entry water vapor response with consensus in both models and observations is that La Niña leads to moistening in winter relative to neutral ENSO. For El Niño and for other seasons, there are significant differences among the models. For example, some models find that the enhanced water vapor for La Niña in the winter of the event reverses in spring and summer, some models find that this moistening persists, and some show a nonlinear response, with both El Niño and La Niña leading to enhanced water vapor in both winter, spring, and summer. A moistening in the spring following El Niño events, the signal focused on in much previous work, is simulated by only half of the models. Focusing on Central Pacific ENSO vs. East Pacific ENSO, or temperatures in the mid-troposphere compared with temperatures near the surface, does not narrow the inter-model discrepancies. Despite this diversity in response, the temperature response near the cold point can explain the response of water vapor when each model is considered separately. While the observational record is too short to fully constrain the response to ENSO, it is clear that most models suffer from biases in the magnitude of the interannual variability of entry water vapor. This bias could be due to biased cold-point temperatures in some models, but others appear to be missing forcing processes that contribute to observed variability near the cold point.

The response of mesospheric H<sub>2</sub>O and CO to solar irradiance variability in models and observations

Abstract. Water vapor (H2O) is the source of reactive hydrogen radicals in the middle atmosphere, whereas carbon monoxide (CO), being formed by CO2 photolysis, is suitable as a dynamical tracer. In the mesosphere, both H2O and CO are sensitive to solar irradiance (SI) variability because of their destruction/production by solar radiation. This enables us to analyze the solar signal in both models and observed data. Here, we evaluate the mesospheric H2O and CO response to solar irradiance variability using the Chemistry-Climate Model Initiative (CCMI-1) simulations and satellite observations. We analyzed the results of four CCMI models (CMAM, EMAC-L90MA, SOCOLv3, and CESM1-WACCM 3.5) operated in CCMI reference simulation REF-C1SD in specified dynamics mode, covering the period from 1984–2017. Multiple linear regression analyses show a pronounced and statistically robust response of H2O and CO to solar irradiance variability and to the annual and semiannual cycles. For periods with available satellite data, we compared the simulated solar signal against satellite observations, namely the GOZCARDS composite for 1992–2017 for H2O and Aura/MLS measurements for 2005–2017 for CO. The model results generally agree with observations and reproduce an expected negative and positive correlation for H2O and CO, respectively, with solar irradiance. However, the magnitude of the response and patterns of the solar signal varies among the considered models, indicating differences in the applied chemical reaction and dynamical schemes, including the representation of photolyzes. We suggest that there is no dominating thermospheric influence of solar irradiance in CO, as reported in previous studies, because the response to solar variability is comparable with observations in both low-top and high-top models. We stress the importance of this work for improving our understanding of the current ability and limitations of state-of-the-art models to simulate a solar signal in the chemistry and dynamics of the middle atmosphere.

On the role of trend and variability in the hydroxyl radical (OH) in the global methane budget

Abstract. Decadal trends and interannual variations in the hydroxyl radical (OH), while poorly constrained at present, are critical for understanding the observed evolution of atmospheric methane (CH4). Through analyzing the OH fields simulated by the model ensemble of the Chemistry–Climate Model Initiative (CCMI), we find (1) the negative OH anomalies during the El Niño years mainly corresponding to the enhanced carbon monoxide (CO) emissions from biomass burning and (2) a positive OH trend during 1980–2010 dominated by the elevated primary production and the reduced loss of OH due to decreasing CO after 2000. Both two-box model inversions and variational 4D inversions suggest that ignoring the negative anomaly of OH during the El Niño years leads to a large overestimation of the increase in global CH4 emissions by up to 10 ± 3 Tg yr−1 to match the observed CH4 increase over these years. Not accounting for the increasing OH trends given by the CCMI models leads to an underestimation of the CH4 emission increase by 23 ± 9 Tg yr−1 from 1986 to 2010. The variational-inversion-estimated CH4 emissions show that the tropical regions contribute most to the uncertainties related to OH. This study highlights the significant impact of climate and chemical feedbacks related to OH on the top-down estimates of the global CH4 budget.

The Global Space-based Stratospheric Aerosol Climatology (version 2.0): 1979–2018

Abstract. A robust stratospheric aerosol climate data record enables the depiction of the radiative forcing of this highly variable component of climate. In addition to the radiative forcing, stratospheric aerosol also plays a key role in the chemical processes leading to ozone depletion. Therefore, stratospheric aerosol is one of the crucial parameters in understanding climate change in the past and potential changes in the future. As a part of Stratospheric-tropospheric Processes and their Role in Climate (SPARC) Stratospheric Sulfur and its Role in Climate (SSiRC) activity, the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) was created (Thomason et al., 2018) to support the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al., 2016). This data set is a follow-on to one created as a part of SPARC's Assessment of Stratospheric Aerosol Properties (ASAP) activity (SPARC, 2006) and a data created for the Chemistry-Climate Model Initiative (CCMI) in 2012 (Eyring and Lamarque, 2012). Herein, we discuss changes to the original release version including those as a part of v1.1 that was released in September 2018 that primarily corrects an error in the conversion of Cryogenic Limb Array Etalon Spectrometer (CLAES) data to Stratospheric Aerosol and Gas Experiment (SAGE) II wavelengths, as well as the new release, v2.0. Version 2.0 is focused on improving the post-SAGE II era (after 2005) with the goal of mitigating elevated aerosol extinction in the lower stratosphere at mid- and high latitudes noted in v1.0 as noted in Thomason et al. (2018). Changes include the use of version 7.0 of the Optical Spectrograph and InfraRed Imaging System (OSIRIS), the recently released Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar Level 3 stratospheric aerosol profile monthly product and the new addition of SAGE III/ISS. Here, we use an observed relationship between (i) OSIRIS extinction at 750 nm and (ii) SAGE II and SAGE III/ISS extinction at 525 nm to derive an altitude–latitude-based monthly climatology of Ångström exponent to compute OSIRIS extinction at 525 nm, resulting in a better agreement between OSIRIS and SAGE measurements. We employ a similar approach to convert OSIRIS 750 nm extinction to 1020 nm extinction for the post-SAGE II period. Additionally, we incorporate the recently released standard CALIPSO stratospheric aerosol profile monthly product into GloSSAC with an improved conversion technique of the 532 nm backscatter coefficient to extinction using an observed relationship between OSIRIS 525 nm extinction and CALIPSO 532 nm backscatter. SAGE III/ISS data are also incorporated in GloSSAC to extend the climatology to the present and to test the approach used to correct OSIRIS/CALIPSO data. The GloSSAC v2.0 netCDF file is accessible at https://doi.org/10.5067/glossac-l3-v2.0 (Thomason, 2020).

Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence

Abstract. Calculating a multi-model mean, a commonly used method for ensemble averaging, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted-model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry–Climate Model Initiative (CCMI) ensemble to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052–2060), 4 years earlier than the most recent study. Perfect-model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi-model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry–climate modelling communities.

Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets

Abstract. The hydroxyl radical (OH), which is the dominant sink of methane (CH4), plays a key role in closing the global methane budget. Current top-down estimates of the global and regional CH4 budget using 3D models usually apply prescribed OH fields and attribute model–observation mismatches almost exclusively to CH4 emissions, leaving the uncertainties due to prescribed OH fields less quantified. Here, using a variational Bayesian inversion framework and the 3D chemical transport model LMDz, combined with 10 different OH fields derived from chemistry–climate models (Chemistry–Climate Model Initiative, or CCMI, experiment), we evaluate the influence of OH burden, spatial distribution, and temporal variations on the global and regional CH4 budget. The global tropospheric mean CH4-reaction-weighted [OH] ([OH]GM-CH4) ranges 10.3–16.3×105 molec cm−3 across 10 OH fields during the early 2000s, resulting in inversion-based global CH4 emissions between 518 and 757 Tg yr−1. The uncertainties in CH4 inversions induced by the different OH fields are similar to the CH4 emission range estimated by previous bottom-up syntheses and larger than the range reported by the top-down studies. The uncertainties in emissions induced by OH are largest over South America, corresponding to large inter-model differences of [OH] in this region. From the early to the late 2000s, the optimized CH4 emissions increased by 22±6 Tg yr−1 (17–30 Tg yr−1), of which ∼25 % (on average) offsets the 0.7 % (on average) increase in OH burden. If the CCMI models represent the OH trend properly over the 2000s, our results show that a higher increasing trend of CH4 emissions is needed to match the CH4 observations compared to the CH4 emission trend derived using constant OH. This study strengthens the importance of reaching a better representation of OH burden and of OH spatial and temporal distributions to reduce the uncertainties in the global and regional CH4 budgets.

Future trends in stratosphere-to-troposphere transport in CCMI models

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.

Description and Evaluation of the specified-dynamics experiment in the Chemistry-Climate Model Initiative

Abstract. We provide an overview of the REF-C1SD specified-dynamics experiment that was conducted as part of phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted of mainly nudged general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and are evaluated in terms of how well they represent different measures of the dynamical and transport circulations. In the troposphere there are large (∼40 %) differences in the climatological mean distributions, seasonal cycle amplitude, and trends of the meridional and vertical winds. In the stratosphere there are similarly large (∼50 %) differences in the magnitude, trends and seasonal cycle amplitude of the transformed Eulerian mean circulation and among various chemical and idealized tracers. At the same time, interannual variations in nearly all quantities are very well represented, compared to the underlying reanalyses. We show that the differences in magnitude, trends and seasonal cycle are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. In most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models, which are also shown to be more dynamically consistent. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition.

Near-Surface Ozone Variations in East Asia during Boreal Summer

This study examined the variability of near-surface (850 hPa) ozone during summer in East Asia using simulations from 12 models participating in the Chemistry–Climate Model Initiative (CCMI). The empirical orthogonal function (EOF) analysis of non-detrended ozone shows that the first (second) EOF mode is characterized by a monopole (dipole) structure that describe 83.3% (7.1%) of total variance. The corresponding the first principle component (PC1) time series exhibits a gradually increasing trend due to the rising anthropogenic emission, whereas PC2 shows interannual variation. To understand the drivers of this interannual variability, the detrended ozone is also analyzed. The two leading EOF patterns of detrended ozone, EOF1 and EOF2, explain 37.0% and 29.2% of the total variance, respectively. The regression results indicate that the positive ozone anomaly in East Asia associated with EOF1 is caused by the combination of net ozone production and transport from the upper atmosphere. In contrast, EOF2 is associated with the weakened western Pacific subtropical high during the La Niña decaying summer, which tends to decrease monsoon precipitation, thus increasing ozone concentration in China. Our results suggest that the El Niño-Southern Oscillation (ENSO) plays a key role in driving interannual variability in tropospheric ozone in East Asia.

A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1

Abstract. The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D), the mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx≡NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in τCH4, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone column, and temperature account for little to no model variation in τCH4. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapour on OH and τCH4, imparting an increasing and decreasing trend of about 0.5 % decade−1, respectively. The responses due to NOx, ozone column, and temperature are also in reasonably good agreement between the two studies.

Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites

Abstract. 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.

A Global/Regional Integrated Model System‐Chemistry Climate Model: 1. Simulation Characteristics

AbstractThis study describes the simulation characteristics of a newly developed Global/Regional Integrated Model system‐Chemistry Climate Model (GRIMs‐CCM), which is listed in Chemistry Climate Model Initiative as a participating model. The GRIMs‐CCM was run using a standard set of forcings, and historical sea surface temperatures and sea ice concentration from the Hadley Centre were prescribed. The simulation results of GRIMs‐CCM were compared to the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set 1. The GRIMs‐CCM satisfactorily simulated the climatological (1960–2010) atmospheric features and atmospheric teleconnections resulting from tropical sea surface temperature forcing. However, the GRIMs‐CCM also had some regional biases; for instance, particularly, the temperature bias over the Antarctic was noticeable. We further analyzed physical processes that caused such biases and the influence of coupled chemistry‐climate processes in the GRIMs‐CCM, which may provide further guidance to improve an earlier version of the GRIMs‐CCM and other climate‐chemistry models participating in the Chemistry Climate Model Initiative.

The effect of atmospheric nudging on the stratospheric residual circulation in chemistry–climate models

Abstract. We perform the first multi-model intercomparison of the impact of nudged meteorology on the stratospheric residual circulation using hindcast simulations from the Chemistry–Climate Model Initiative (CCMI). We examine simulations over the period 1980–2009 from seven models in which the meteorological fields are nudged towards a reanalysis dataset and compare these with their equivalent free-running simulations and the reanalyses themselves. We show that for the current implementations, nudging meteorology does not constrain the mean strength of the stratospheric residual circulation and that the inter-model spread is similar, or even larger, than in the free-running simulations. The nudged models generally show slightly stronger upwelling in the tropical lower stratosphere compared to the free-running versions and exhibit marked differences compared to the directly estimated residual circulation from the reanalysis dataset they are nudged towards. Downward control calculations applied to the nudged simulations reveal substantial differences between the climatological lower-stratospheric tropical upward mass flux (TUMF) computed from the modelled wave forcing and that calculated directly from the residual circulation. This explicitly shows that nudging decouples the wave forcing and the residual circulation so that the divergence of the angular momentum flux due to the mean motion is not balanced by eddy motions, as would typically be expected in the time mean. Overall, nudging meteorological fields leads to increased inter-model spread for most of the measures of the mean climatological stratospheric residual circulation assessed in this study. In contrast, the nudged simulations show a high degree of consistency in the inter-annual variability in the TUMF in the lower stratosphere, which is primarily related to the contribution to variability from the resolved wave forcing. The more consistent inter-annual variability in TUMF in the nudged models also compares more closely with the variability found in the reanalyses, particularly in boreal winter. We apply a multiple linear regression (MLR) model to separate the drivers of inter-annual and long-term variations in the simulated TUMF; this explains up to ∼75 % of the variance in TUMF in the nudged simulations. The MLR model reveals a statistically significant positive trend in TUMF for most models over the period 1980–2009. The TUMF trend magnitude is generally larger in the nudged models compared to their free-running counterparts, but the intermodel range of trends doubles from around a factor of 2 to a factor of 4 due to nudging. Furthermore, the nudged models generally do not match the TUMF trends in the reanalysis they are nudged towards for trends over different periods in the interval 1980–2009. Hence, we conclude that nudging does not strongly constrain long-term trends simulated by the chemistry–climate model (CCM) in the residual circulation. Our findings show that while nudged simulations may, by construction, produce accurate temperatures and realistic representations of fast horizontal transport, this is not typically the case for the slower zonal mean vertical transport in the stratosphere. Consequently, caution is required when using nudged simulations to interpret the behaviour of stratospheric tracers that are affected by the residual circulation.