Validation and empirical correction of MODIS AOT and AE over ocean

Schutgens, Nick; Nakata, Makiko; Nakajima, Teruyuki

doi:10.5194/amt-6-2455-2013

Cited by 32 publications

(30 citation statements)

References 29 publications

(58 reference statements)

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“…The evolution of aerosol across a global model grid box may differ quite a bit due to differences in sources, flow and deposition (especially wet). Nevertheless, as is well known from observations, aerosol exhibits correlations over several tens of kilometres (Anderson et al, 2003;Kovacs, 2006;Santese et al, 2007;Shinozuka and Redemann, 2011;Schutgens et al, 2013) and our highresolution simulations are no different. Hence, an observation at the centre of a grid box will correlate strongly with a large part of that grid box, while an observation in the upperright corner will only correlate strongly with (part of) the upper-right quadrant of that grid box but less so with the lower-left quadrant.…”

Section: Observations Offset From the Grid Pointmentioning

confidence: 69%

“…Ground stations from AErosol RObotic NETwork (AERONET) can be estimated to sample no more than 5 km horizontally away from the site. In situ measurements cover even less of the atmosphere surrounding them; yet, observed aerosol fields are known to exhibit variations over relatively short distances of 10 to 100 km (Anderson et al, 2003;Kovacs, 2006;Santese et al, 2007;Shinozuka and Redemann, 2011;Schutgens et al, 2013). Note that the spatial resolution of global models also impacts global model data due to the non-linear nature of many physical and chemical processes (Qian et al, 2010;Gustafson et al, 2011;Stroud et al, 2011;Weigum et al, 2016); but that is not the topic of this paper.…”

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confidence: 88%

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Will a perfect model agree with perfect observations? The impact of spatial sampling

et al. 2016

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Abstract. The spatial resolution of global climate models with interactive aerosol and the observations used to evaluate them is very different. Current models use grid spacings of ∼ 200 km, while satellite observations of aerosol use so-called pixels of ∼ 10 km. Ground site or airborne observations relate to even smaller spatial scales. We study the errors incurred due to different resolutions by aggregating high-resolution simulations (10 km grid spacing) over either the large areas of global model grid boxes ("perfect" model data) or small areas corresponding to the pixels of satellite measurements or the field of view of ground sites ("perfect" observations). Our analysis suggests that instantaneous rootmean-square (RMS) differences of perfect observations from perfect global models can easily amount to 30-160 %, for a range of observables like AOT (aerosol optical thickness), extinction, black carbon mass concentrations, PM 2.5 , number densities and CCN (cloud condensation nuclei). These differences, due entirely to different spatial sampling of models and observations, are often larger than measurement errors in real observations. Temporal averaging over a month of data reduces these differences more strongly for some observables (e.g. a threefold reduction for AOT), than for others (e.g. a twofold reduction for surface black carbon concentrations), but significant RMS differences remain (10-75 %). Note that this study ignores the issue of temporal sampling of real observations, which is likely to affect our present monthly error estimates. We examine several other strategies (e.g. spatial aggregation of observations, interpolation of model data) for reducing these differences and show their effectiveness. Finally, we examine consequences for the use of flight campaign data in global model evaluation and show that significant biases may be introduced depending on the flight strategy used.

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Section: Observations Offset From the Grid Pointmentioning

confidence: 69%

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confidence: 88%

Will a perfect model agree with perfect observations? The impact of spatial sampling

et al. 2016

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“…If one does not properly collocate AE model data with observations, this would create an artificial contrast up to ∼ 0.6 in AE between the continents and the remote oceans. The reason is that AE observations require a minimum AOT to be successful (see Schutgens et al, 2013). In areas with polluted outflows, low AOT correspond to the sea-salt background with low AE and observations will mostly sample the outflow with high AE, leading to negative sampling errors.…”

Section: Spatial Patterns In Temporal Sampling Errorsmentioning

confidence: 99%

“…We also use the MODIS Aqua L3 AORI (Atmosphere and Ocean Research Institute) data (Schutgens et al, 2013) for 2007. These are based on MODIS Aqua L2 data that have been subjected to further quality checks, empirical corrections and spatial aggregation.…”

Section: Observationsmentioning

confidence: 99%

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

2016

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Abstract. It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show that they are of similar magnitude as (but smaller than) actual model errors (20-60 %).Using temporal sampling from remote-sensing data sets, the satellite imager MODIS (MODerate resolution Imaging Spectroradiometer) and the ground-based sun photometer network AERONET (AErosol Robotic NETwork), and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100 % in AOT (aerosol optical thickness), 0.4 in AE (Ångström Exponent) and 0.05 in SSA (single scattering albedo) are possible. Even in daily averages, sampling errors can be significant. Moreover these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made.We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation.Although this study is framed in the context of model evaluation, it has a clear and direct relevance to climatologies derived from observational data sets.

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“…MODIS AOT values are calculated by retrieval algorithms based on lookup tables for different particles which depend on scattering geometries (Tanre et al, 1997). Error estimations of MODIS AOT retrievals have been investigated by comparison with ground-based AERONET (e.g., Remer et al, 2005;Bréon et al, 2011) and MAN observations (e.g., Adames et al, 2011;Smirnov et al, 2011;Schutgens et al, 2013). Here we use the Naval Research Laboratory (NRL) L3 Shi et al, 2011) data that are derived from MODIS L2 observations through additional quality checks and empirical correction formulae.…”

Section: Observational Data Sets For Model Evaluationmentioning

confidence: 99%

Fire emission heights in the climate system – Part 2: Impact on transport, black carbon concentrations and radiation

et al. 2015

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Abstract. Wildfires represent a major source for aerosols impacting atmospheric radiation, atmospheric chemistry and cloud micro-physical properties. Previous case studies indicated that the height of the aerosol-radiation interaction may crucially affect atmospheric radiation, but the sensitivity to emission heights has been examined with only a few models and is still uncertain. In this study we use the general circulation model ECHAM6 extended by the aerosol module HAM2 to investigate the impact of wildfire emission heights on atmospheric long-range transport, black carbon (BC) concentrations and atmospheric radiation. We simulate the wildfire aerosol release using either various versions of a semiempirical plume height parametrization or prescribed standard emission heights in ECHAM6-HAM2. Extreme scenarios of near-surface or free-tropospheric-only injections provide lower and upper constraints on the emission height climate impact. We find relative changes in mean global atmospheric BC burden of up to 7.9 ± 4.4 % caused by average changes in emission heights of 1.5-3.5 km. Regionally, changes in BC burden exceed 30-40 % in the major biomass burning regions. The model evaluation of aerosol optical thickness (AOT) against Moderate Resolution Imaging Spectroradiometer (MODIS), AErosol RObotic NETwork (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations indicates that the implementation of a plume height parametrization slightly reduces the ECHAM6-HAM2 biases regionally, but on the global scale these improvements in model performance are small. For prescribed emission release at the surface, wildfire emissions entail a total sky top-of-atmosphere (TOA) radiative forcing (RF) of −0.16 ± 0.06 W m −2 . The application of a plume height parametrization which agrees reasonably well with observations introduces a slightly stronger negative TOA RF of −0.20 ± 0.07 W m −2 . The standard ECHAM6-HAM2 model in which 25 % of the wildfire emissions are injected into the free troposphere (FT) and 75 % into the planetary boundary layer (PBL), leads to a TOA RF of −0.24 ± 0.06 W m −2 . Overall, we conclude that simple plume height parametrizations provide sufficient representations of emission heights for global climate modeling. Significant improvements in aerosol wildfire modeling likely depend on better emission inventories and aerosol process modeling rather than on improved emission height parametrizations.

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Validation and empirical correction of MODIS AOT and AE over ocean

Cited by 32 publications

References 29 publications

Will a perfect model agree with perfect observations? The impact of spatial sampling

Will a perfect model agree with perfect observations? The impact of spatial sampling

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

Fire emission heights in the climate system – Part 2: Impact on transport, black carbon concentrations and radiation

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