Comparison of the CMAM30 data set with ACE-FTS and OSIRIS: polar regions

Pendlebury, Diane; Plummer, David A.; Scinocca, John; Sheese, Patrick E.; Strong, Kimberly; Walker, Kaley A.; Degenstein, D. A.

doi:10.5194/acp-15-12465-2015

Cited by 16 publications

(9 citation statements)

References 44 publications

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“…This study uses hindcast specified-dynamics reference simulations (RefC1SD) (Plummer et al, 2014) conducted for the Chemistry Climate Model Initiative (CCMI-1) (Hegglin and Lamarque, 2015;Morgenstern et al, 2017), of both ozone (O 3 ) and stratosphere-tagged tracer ozone (O 3 S) for the period 1980-2010 inclusive from two state-of-theart CCMs: European Centre for Medium-Range Weather Forecasts -Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC;Jöckel et al, 2016) and the Canadian Middle Atmosphere Model (CMAM; Scinocca et al, 2008). These two models were primarily selected due to the close similarity in the O 3 S tracer definition (detailed below in Sect.…”

Section: Ccm Simulationsmentioning

confidence: 99%

Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes

et al. 2019

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Abstract. The stratospheric contribution to tropospheric ozone (O3) has been a subject of much debate in recent decades but is known to have an important influence. Recent improvements in diagnostic and modelling tools provide new evidence that the stratosphere has a much larger influence than previously thought. This study aims to characterise the seasonal and geographical distribution of tropospheric ozone, its variability, and its changes and provide quantification of the stratospheric influence on these measures. To this end, we evaluate hindcast specified-dynamics chemistry–climate model (CCM) simulations from the European Centre for Medium-Range Weather Forecasts – Hamburg (ECHAM)/Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model and the Canadian Middle Atmosphere Model (CMAM), as contributed to the International Global Atmospheric Chemistry – Stratosphere-troposphere Processes And their Role in Climate (IGAC-SPARC) (IGAC–SPARC) Chemistry Climate Model Initiative (CCMI) activity, together with satellite observations from the Ozone Monitoring Instrument (OMI) and ozone-sonde profile measurements from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) over a period of concurrent data availability (2005–2010). An overall positive, seasonally dependent bias in 1000–450 hPa (∼0–5.5 km) sub-column ozone is found for EMAC, ranging from 2 to 8 Dobson units (DU), whereas CMAM is found to be in closer agreement with the observations, although with substantial seasonal and regional variation in the sign and magnitude of the bias (∼±4 DU). Although the application of OMI averaging kernels (AKs) improves agreement with model estimates from both EMAC and CMAM as expected, comparisons with ozone-sondes indicate a positive ozone bias in the lower stratosphere in CMAM, together with a negative bias in the troposphere resulting from a likely underestimation of photochemical ozone production. This has ramifications for diagnosing the level of model–measurement agreement. Model variability is found to be more similar in magnitude to that implied from ozone-sondes in comparison with OMI, which has significantly larger variability. Noting the overall consistency of the CCMs, the influence of the model chemistry schemes and internal dynamics is discussed in relation to the inter-model differences found. In particular, it is inferred that CMAM simulates a faster and shallower Brewer–Dobson circulation (BDC) compared to both EMAC and observational estimates, which has implications for the distribution and magnitude of the downward flux of stratospheric ozone over the most recent climatological period (1980–2010). Nonetheless, it is shown that the stratospheric influence on tropospheric ozone is significant and is estimated to exceed 50 % in the wintertime extratropics, even in the lower troposphere. Finally, long-term changes in the CCM ozone tracers are calculated for different seasons. An overall statistically significant increase in tropospheric ozone is found across much of the world but particularly in the Northern Hemisphere and in the middle to upper troposphere, where the increase is on the order of 4–6 ppbv (5 %–10 %) between 1980–1989 and 2001–2010. Our model study implies that attribution from stratosphere–troposphere exchange (STE) to such ozone changes ranges from 25 % to 30 % at the surface to as much as 50 %–80 % in the upper troposphere–lower stratosphere (UTLS) across some regions of the world, including western Eurasia, eastern North America, the South Pacific and the southern Indian Ocean. These findings highlight the importance of a well-resolved stratosphere in simulations of tropospheric ozone and its implications for the radiative forcing, air quality and oxidation capacity of the troposphere.

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Section: Ccm Simulationsmentioning

confidence: 99%

Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes

et al. 2019

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“…Their quality has been extensively evaluated in numerous data characterization and validation papers (i.e. Pumphrey et al, 2007;Read et al, 2007;Santee et al, 2007;Schwartz et al, 2008;Stiller et al, 2012;Hegglin et al, 2013;Raspollini et al, 2013;Neu et al, 2014;Livesey et al, 2017;Sheese et al, 2017). Furthermore, their longterm stability has also been studied (i.e.…”

Section: Millán Et Al: Sampling and Screening Biasesmentioning

confidence: 99%

“…Extensive validation (de Grandpré et al, 2000;Hegglin and Shepherd, 2007;Melo et al, 2008;Jin et al, 2005Jin et al, , 2009 has shown that the free-running version of this model performs well against observations relevant to dynamics, transport, and chemistry. Comparisons against ACE-FTS and the Odin Optical Spectrograph and Infrared Imaging System (OSIRIS) have shown that CMAM30-SD has a good representation of stratospheric temperature, H 2 O, O 3 , and CH 4 in polar regions (Pendlebury et al, 2015). Further, CMAM30-SD has been used to construct a long-term H 2 O record, acting as a transfer function between satellite observations (Hegglin et al, 2014), and it reproduces halogen-induced mid-latitude O 3 depletion sufficiently well to be used in longterm O 3 trend studies (Shepherd et al, 2014).…”

Section: Millán Et Al: Sampling and Screening Biasesmentioning

confidence: 99%

Characterizing sampling and quality screening biases in infrared and microwave limb sounding

et al. 2018

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Abstract. This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected. The results show that upper-tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper-tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although its penetration is limited to the upper troposphere, while MIPAS may cover well into the mid-troposphere in cloud-free scenarios. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality.

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“…Extensive validation (de Grandpré 5 et al, 2000;Hegglin and Shepherd, 2007;Melo et al, 2008;Jin et al, 2005Jin et al, , 2009 has shown that the free-running version of this model performs well against observations relevant to dynamics, transport, and chemistry. Comparisons against ACE-FTS and the Odin Optical Spectrograph and Infrared Imaging System (OSIRIS) have shown that CMAM30-SD has a good representation of stratospheric temperature, H 2 O, O 3 , and CH 4 in polar regions (Pendlebury et al, 2015). Further, CMAM30-SD has been used to construct a long-term H 2 O record, acting as a transfer function between satellite observations (Hegglin 10 et al, 2014), and it reproduces halogen-induced midlatitude O 3 depletion sufficiently well to be used in long-term O 3 trend studies .…”

Section: Model Fieldsmentioning

confidence: 99%

Characterizing Sampling and Quality Screening Biases in Infrared and Microwave Limb Sounding

Millán¹,

Livesey²,

Santee³

et al. 2017

Preprint

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Abstract. This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS).MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders.These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense 5 uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected.The results show that upper tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10% 10 to 30%, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30% to 100% for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points 15 that no additional bias is introduced, although the vertical range of reliable measurements is slightly reduced. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality.

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Comparison of the CMAM30 data set with ACE-FTS and OSIRIS: polar regions

Cited by 16 publications

References 44 publications

Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes

Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes

Characterizing sampling and quality screening biases in infrared and microwave limb sounding

Characterizing Sampling and Quality Screening Biases in Infrared and Microwave Limb Sounding

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