The importance of temporal collocation for the evaluation of aerosol models with observations

Schutgens, Nick; Partridge, Derek; Stier, Philip

doi:10.5194/acp-16-1065-2016

Cited by 76 publications

(76 citation statements)

References 54 publications

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“…Although statistical profile results (e.g., Turner et al, 2001;Yu et al, 2010;Ma and Yu, 2014) suggest little contribution from high-altitude aerosol layers in the region of these two sites, Schutgens et al (2016) demonstrate the importance of considering the specifics rather than the statistical. We used the Raman lidar best-estimate data product of extinction profiles at SGP to evaluate the presence of aerosol above the highest flight level at the site.…”

Section: E Andrews Et Al: Comparison Of Aod Aaod and Column Singlementioning

confidence: 99%

“…The low number of flights for which there are comparisons available (∼ 10 % of total number of flights) indicates both the effects of AERONET's stringent cloud screening routine and the constraints imposed by the almucantar retrievals. In addition to limiting the number of comparisons available for this study, this limited data availability also has implications for modellers utilizing AERONET data -for example, Schutgens et al (2016) showed the importance of temporal collocation in measurement-model comparisons. Figure 4 also contains red points -the red data points represent all directsun AERONET Level 2 AOD measurements during the ±3 h window around the end of each profile.…”

Section: Bnd and Sgp: In Situ Vs Aeronet -Direct Comparisonsmentioning

confidence: 99%

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Comparison of AOD, AAOD and column single scattering albedo from AERONET retrievals and in situ profiling measurements

et al. 2017

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Abstract. Here we present new results comparing aerosol optical depth (AOD), aerosol absorption optical depth (AAOD) and column single scattering albedo (SSA) obtained from in situ vertical profile measurements with AERONET ground-based remote sensing from two rural, continental sites in the US. The profiles are closely matched in time (within ±3 h) and space (within 15 km) with the AERONET retrievals. We have used Level 1.5 inversion retrievals when there was a valid Level 2 almucantar retrieval in order to be able to compare AAOD and column SSA below AERONET's recommended loading constraint (AOD > 0.4 at 440 nm). While there is reasonable agreement for the AOD comparisons, the direct comparisons of in situderived to AERONET-retrieved AAOD (or SSA) reveal that AERONET retrievals yield higher aerosol absorption than obtained from the in situ profiles for the low aerosol optical depth conditions prevalent at the two study sites. However, it should be noted that the majority of SSA comparisons for AOD 440 > 0.2 are, nonetheless, within the reported SSA uncertainty bounds. The observation that, relative to in situ measurements, AERONET inversions exhibit increased absorption potential at low AOD values is generally consistent with other published AERONET-in situ comparisons across a range of locations, atmospheric conditions and AOD values. This systematic difference in the comparisons suggests a bias in one or both of the methods, but we cannot assess whether the AERONET retrievals are biased towards high absorption or the in situ measurements are biased low. Based on the discrepancy between the AERONET and in situ values, we conclude that scaling modeled black carbon concentrations upwards to match AERONET retrievals of AAOD should be approached with caution as it may lead to aerosol absorption overestimates in regions of low AOD. Both AERONET retrievals and in situ measurements suggest there is a systematic relationship between SSA and aerosol amount (AOD or aerosol light scattering) -specifically that SSA decreases at lower aerosol loading. This implies that the fairly common assumption that AERONET SSA values retrieved at high-AOD conditions can be used to obtain AAOD at low-AOD conditions may not be valid.

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Section: E Andrews Et Al: Comparison Of Aod Aaod and Column Singlementioning

confidence: 99%

Section: Bnd and Sgp: In Situ Vs Aeronet -Direct Comparisonsmentioning

confidence: 99%

Comparison of AOD, AAOD and column single scattering albedo from AERONET retrievals and in situ profiling measurements

et al. 2017

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“…A strong caveat in these comparisons is that the modelled AOD has not been sampled with the spatial and temporal incidence of the MODIS data. Schutgens et al (2016) showed that this can result in considerable regional biases between modelled and observed monthly and annual mean AOD. In particular, the comparison may be of limited value at high latitudes (beyond 60 • N or S) where retrievals are not possible for several months of the year (due to the solar zenith angle being too high, or due to a lack of solar illumination altogether).…”

Section: Averaging Methodsmentioning

confidence: 99%

“…Whilst these results give clues as to where BB aerosol emissions may be overestimated or underestimated, the differences between modelled and observed AOD may be affected by various other sources of uncertainty in the models and measurements. In particular, temporal sampling biases may affect the results (Schutgens et al, 2016), as we have not sampled the model data to match AERONET retrieval times. The approach we have taken is to average over 10 years of data to gain more confidence in the long-term monthly means.…”

Section: Aod Comparison With Aeronetmentioning

confidence: 99%

Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign

et al. 2016

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Abstract. We present observations of biomass burning aerosol from the South American Biomass Burning Analysis (SAMBBA) and other measurement campaigns, and use these to evaluate the representation of biomass burning aerosol properties and processes in a state-of-the-art climate model. The evaluation includes detailed comparisons with aircraft and ground data, along with remote sensing observations from MODIS and AERONET. We demonstrate several improvements to aerosol properties following the implementation of the Global Model for Aerosol Processes (GLOMAP-mode) modal aerosol scheme in the HadGEM3 climate model. This predicts the particle size distribution, composition, and optical properties, giving increased accuracy in the representation of aerosol properties and physical-chemical processes over the Coupled Largescale Aerosol Scheme for Simulations in Climate Models (CLASSIC) bulk aerosol scheme previously used in HadGEM2. Although both models give similar regional distributions of carbonaceous aerosol mass and aerosol optical depth (AOD), GLOMAP-mode is better able to capture the observed size distribution, single scattering albedo, and Ångström exponent across different tropical biomass burning source regions. Both aerosol schemes overestimate the uptake of water compared to recent observations, CLAS-SIC more so than GLOMAP-mode, leading to a likely overestimation of aerosol scattering, AOD, and single scattering albedo at high relative humidity. Observed aerosol vertical distributions were well captured when biomass burning aerosol emissions were injected uniformly from the surface to 3 km. Finally, good agreement between observed and modelled AOD was gained only after scaling up GFED3 emissions by a factor of 1.6 for CLASSIC and 2.0 for GLOMAPmode. We attribute this difference in scaling factor mainly to different assumptions for the water uptake and growth of aerosol mass during ageing via oxidation and condensation of organics. We also note that similar agreement with observed AOD could have been achieved with lower scaling factors if the ratio of organic carbon to primary organic matter was increased in the models toward the upper range of observed values. Improved knowledge from measurements is required to reduce uncertainties in emission ratios for black carbon and organic carbon, and the ratio of organic carbon to primary organic matter for primary emissions from biomass burning.

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“…While the data we use from CALIOP are spatially collocated with the AERONET stations and model data, it is not temporally collocated. A recent study has shown that temporal collocation can be significant, and sampling errors are introduced when it is not considered (Schutgens et al, 2016).…”

Section: Caliopmentioning

confidence: 99%

Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements

Ocko

Ginoux

2017

Atmos. Chem. Phys.

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Abstract. Anthropogenic aerosols are a key factor governing Earth's climate and play a central role in humancaused climate change. However, because of aerosols' complex physical, optical, and dynamical properties, aerosols are one of the most uncertain aspects of climate modeling. Fortunately, aerosol measurement networks over the past few decades have led to the establishment of long-term observations for numerous locations worldwide. Further, the availability of datasets from several different measurement techniques (such as ground-based and satellite instruments) can help scientists increasingly improve modeling efforts. This study explores the value of evaluating several modelsimulated aerosol properties with data from spatially collocated instruments. We compare aerosol optical depth (AOD; total, scattering, and absorption), single-scattering albedo (SSA), Ångström exponent (α), and extinction vertical profiles in two prominent global climate models (Geophysical Fluid Dynamics Laboratory, GFDL, CM2.1 and CM3) to seasonal observations from collocated instruments (AErosol RObotic NETwork, AERONET, and Cloud-Aerosol Lidar with Orthogonal Polarization, CALIOP) at seven polluted and biomass burning regions worldwide. We find that a multi-parameter evaluation provides key insights on model biases, data from collocated instruments can reveal underlying aerosol-governing physics, column properties wash out important vertical distinctions, and "improved" models does not mean all aspects are improved. We conclude that it is important to make use of all available data (parameters and instruments) when evaluating aerosol properties derived by models.

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The importance of temporal collocation for the evaluation of aerosol models with observations

Cited by 76 publications

References 54 publications

Comparison of AOD, AAOD and column single scattering albedo from AERONET retrievals and in situ profiling measurements

Comparison of AOD, AAOD and column single scattering albedo from AERONET retrievals and in situ profiling measurements

Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign

Comparing multiple model-derived aerosol optical properties to spatially collocated ground-based and satellite measurements

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