ALBERT

Description: We use simultaneous observations of tropospheric ozone and outgoing longwave radiation (OLR) sensitivity to tropospheric ozone from the Tropospheric Emission Spectrometer (TES) to evaluate model tropospheric ozone and its effect on OLR simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES ozone for 2005–2010. These ozone biases have a significant impact on the OLR. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean tropospheric ozone low bias leads up to 120 mW m−2 OLR high bias locally but zonally compensating errors reduce the global OLR high bias to 39 ± 41 m Wm−2 relative to TES data. We show that there is a correlation (R2 = 0.59) between the magnitude of the ACCMIP OLR bias and the deviation of the ACCMIP preindustrial to present day (1750–2010) ozone radiative forcing (RF) from the ensemble ozone RF mean. However, this correlation is driven primarily by models whose absolute OLR bias from tropospheric ozone exceeds 100 m Wm−2. Removing these models leads to a mean ozone radiative forcing of 394 ± 42 m Wm−2. The mean is about the same and the standard deviation is about 30% lower than an ensemble ozone RF of 384 ± 60 m Wm−2 derived from 14 of the 16 ACCMIP models reported in a companion ACCMIP study. These results point towards a profitable direction of combining satellite observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

Description: We investigate the influence of African biomass burning, biogenic, lightning and anthropogenic emissions on the tropospheric ozone over Africa and globally using a coupled global chemistry climate model. Our model studies indicate that surface ozone concentration may rise by up to 50 ppbv in the burning region during the biomass burning seasons. Biogenic emissions yield between 5–30 ppbv increase in the near surface ozone concentration over tropical Africa. The impact of lightning on surface ozone is negligible, while anthropogenic emissions yield a maximum of 7 ppbv increase in the annual-mean surface ozone concentration over Nigeria, South Africa and Egypt. Our results show that biogenic emissions are the most important African emission source affecting total tropospheric ozone. The influence of each of the African emissions on the global tropospheric ozone burden (TOB) of 384 Tg yields about 9.5 Tg, 19.6 Tg, 9.0 Tg and 4.7 Tg for biomass burning, biogenic, lightning and anthropogenic emissions emitted in Africa respectively. The impact of each of these emission categories on African TOB of 33 Tg is 2.5 Tg, 4.1 Tg, 1.75 Tg and 0.89 Tg respectively, which together represents about 28% of the total TOB calculated over Africa. Our model calculations also suggest that more than 70% of the tropospheric ozone produced by each of the African emissions is found outside the continent, thus exerting a noticeable influence on a large part of the tropical troposphere. Apart from the Atlantic and Indian Ocean, Latin America experiences the largest impact of African emissions, followed by Oceania, the Middle East, Southeast and south-central Asia, northern North America (i.e. the United States and Canada), Europe and north-central Asia, for all the emission categories.

Description: Atmospheric transport of traces gases and aerosols plays an important role in the distribution of air pollutants and radiatively active compounds. For model simulations of chemistry-climate interactions it is important to know how the transport of tracers depends on the geographical resolution of the general circulation model. However, this aspect has been scarcely investigated until now. Here, we analyse tracer transport in the ECHAM5 general circulation model using 6 independent idealized tracers with constant lifetimes, which are released in two different altitudes at the surface and in the stratosphere, respectively. Model resolutions from T21L19 to T106L31 were tested by performing multi-annual simulations with prescribed sea surface temperatures and sea ice fields of the 1990s. The impacts of the tracer lifetime were investigated by varying the globally uniform exponential decay time between 0.5 and 50 months. We also tested the influence of using prescribed meteorological fields (ERA40) instead of climatological sea surface temperature and sea ice fields. Meridional transport of surface tracers decreases in the coarse resolution model due to enhanced vertical mixing, with the exception of the advection into the tropical region, which shows an inconsistent trend between the resolutions. Whereas, the meridional transport of tracers released in the stratosphere was enhanced with higher model resolutions, except in the transport from tropical stratosphere to the Southern Hemisphere, which exhibits an increase trend with increasing model resolution. The idealized tracers exhibit a seasonal cycle, which is modulated by the tracer lifetime. In comparison to the run with prescribed sea surface temperature and sea ice fields, the simulation with prescribed meteorological fields did not exhibit significant change in the meridional transport, except in the exchange of stratospheric tracers between both hemispheres, where it causes about 100% increase. The import of the surface tracers into the stratosphere is increased by up to a factor of 2.5, and the export from the stratosphere into the troposphere was increased by up to 60% when prescribed meteorological fields is used. The ERA40 simulation also showed larger interannual variability (up to 24% compared to 12% in the standard simulations). Using our surface tracers released in either the northern or Southern Hemisphere, respectively, we calculate inter-hemispheric transport times between 11 and 17 months, consistent with values reported in the literature. While this study cannot be used to relate differences in model results to specific changes in transport processes, it nevertheless provides some insight into the characteristics of tracer transport in the widely used ECHAM5 general circulation model.

Description: Ensemble climate model simulations used for the Intergovernmental Panel on Climate Change (IPCC) assessments have become important tools for exploring the response of the Earth System to changes in anthropogenic and natural forcings. The systematic evaluation of these models through global satellite observations is a critical step in assessing the uncertainty of climate change projections. This paper presents the technical steps required for using nadir sun-synchronous infrared satellite observations for multi-model evaluation and the uncertainties associated with each step. This is motivated by need to use satellite observations to evaluate climate models. We quantified the implications of the effect of satellite orbit and spatial coverage, the effect of variations in vertical sensitivity as quantified by the observation operator and the impact of averaging the operators for use with monthly-mean model output. We calculated these biases in ozone, carbon monoxide, atmospheric temperature and water vapour by using the output from two global chemistry climate models (ECHAM5-MOZ and GISS-PUCCINI) and the observations from the Tropospheric Emission Spectrometer (TES) instrument on board the NASA-Aura satellite from January 2005 to December 2008. The results show that sampling and monthly averaging of the observation operators produce zonal-mean biases of less than ±3 % for ozone and carbon monoxide throughout the entire troposphere in both models. Water vapour sampling zonal-mean biases were also within the insignificant range of ±3 % (that is ±0.14 g kg−1) in both models. Sampling led to a temperature zonal-mean bias of ±0.3 K over the tropical and mid-latitudes in both models, and up to −1.4 K over the boundary layer in the higher latitudes. Using the monthly average of temperature and water vapour operators lead to large biases over the boundary layer in the southern-hemispheric higher latitudes and in the upper troposphere, respectively. Up to 8 % bias was calculated in the upper troposphere water vapour due to monthly-mean operators, which may impact the detection of water vapour feedback in response to global warming. Our results reveal the importance of using the averaging kernel and the a priori profiles to account for the limited vertical resolution and clouds of a nadir observation during model application. Neglecting the observation operators resulted in large biases, which are more than 60 % for ozone, ±30 % for carbon monoxide, and range between −1.5 K and 5 K for atmospheric temperature, and between −60 % and 100 % for water vapour.

Description: The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used in future model evaluations. The highest positive correlations (around 0.8) are found in the northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and may reflect a mixture of different processes such as transport.

Description: The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used as benchmarks in future studies. The highest positive correlations (around 0.8) are found in the Northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and reflects a mixture of different processes such as transport.

Description: We investigate the relative importance of African biomass burning, biogenic volatile organic compounds (VOC), lightning and anthropogenic emissions to the tropospheric ozone budget over Africa and globally using a coupled global chemistry climate model. Our model studies indicate that the photochemical surface ozone concentration may rise by up to 50 ppbv in the burning region during the biomass burning seasons. Biogenic VOCs contribute between 5–20 ppbv to the near surface ozone concentration over the tropical African region. The impact of lightning on surface ozone is negligible, while anthropogenic emissions contribute a maximum of 10 ppbv to the surface ozone over Nigeria, South-Africa and Egypt. The annual average of the surface and column ozone over Africa shows that biomass burning is the single most important emission source affecting the African region, while biogenic emissions have the highest contribution during the rainy seasons. The contributions of African emissions to global tropospheric ozone burden (TOB) are about 9 Tg, 13 Tg, 8 Tg and 4 Tg for African biomass burning, biogenic VOC, lightning and anthropogenic emissions respectively. These correspond to 2.4%, 3.4%, 2.1% and 1% of the global tropospheric ozone budget respectively. Over Africa itself, the contribution of each of these emission types is only 2.4 Tg, 2.2 Tg, 1.4 Tg and 0.8 Tg respectively. Outside the continent, African biogenic VOC emissions yield the highest contribution to the TOB. Our model calculations suggest that about 70% of the tropospheric ozone produced from emissions in Africa is found outside the continent, thus exerting a noticeable influence on a large part of the tropical troposphere. Latin America experiences the highest impact of African emissions, followed by southeast and south-central Asia, Oceania, and the Middle East for all the emission categories; while Canada, the United States, Russia, Mongolia, China and Europe experience the least impact of African emissions.

Description: Ensemble climate model simulations used for the Intergovernmental Panel on Climate Change (IPCC) assessments have become important tools for exploring the response of the Earth System to changes in anthropogenic and natural forcings. The systematic evaluation of these models through global satellite observations is a critical step in assessing the uncertainty of climate change projections. This paper presents the technical steps required for using nadir sun-synchronous infrared satellite observations for multi-model evaluation and the uncertainties associated with each step. This is motivated by need to use satellite observations to evaluate climate models. We quantified the implications of the effect of satellite orbit and spatial coverage, the effect of variations in vertical sensitivity as quantified by the observation operator and the impact of averaging the operators for use with monthly-mean model output. We calculated these biases in ozone, carbon monoxide, atmospheric temperature and water vapour by using the output from two global chemistry climate models (ECHAM5-MOZ and GISS-PUCCINI) and the observations from the Tropospheric Emission Spectrometer (TES) satellite from January 2005 to December 2008. The results show that sampling and monthly averaging of the observation operators produce biases of less than ±3% for ozone and carbon monoxide throughout the entire troposphere in both models. Water vapour sampling biases were also within the insignificant range of ±3% (that is ±0.14 g kg−1) in both models. Sampling led to a temperature bias of ±0.3 K over the tropical and mid-latitudes in both models, and up to −1.4 K over the boundary layer in the higher latitudes. Using the monthly average of temperature and water vapour operators lead to large biases over the boundary layer in the southern-hemispheric higher latitudes and in the upper troposphere, respectively. Up to 8% bias was calculated in the upper troposphere water vapour due to monthly-mean operators, which may impact the detection of water vapour feedback in response to global warming. Our results reveal the importance of using the averaging kernel and the a priori profiles to account for the limited vertical resolution of a nadir observation during model application. Neglecting the observation operators resulted in large biases, which are more than 60% for ozone, ±30% for carbon monoxide, and range between −1.5 K and 5 K for atmospheric temperature, and between −60% and 100% for water vapour.