On the spatio-temporal representativeness of observations

Schutgens, Nick; Tsyro, Svetlana; Gryspeerdt, Edward; Goto, Daisuke; Weigum, Natalie; Schulz, Mıchael; Stier, Philip

doi:10.5194/acp-17-9761-2017

Cited by 102 publications

(119 citation statements)

References 36 publications

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“…have also previously noted that the ! for MARC is generally lower than that retrieved from the MODerate Resolution Imaging Spectroradiometer (MODIS; Collection 5.1); but it should be noted that differences in spatial-temporal sampling (Schutgens et al, 2017(Schutgens et al, , 2016 have not been accounted for.…”

Section: Aerosol Optical Depthmentioning

confidence: 94%

Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG

et al. 2018

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We quantify the effective radiative forcing (ERF) of anthropogenic aerosols modelled by the aerosol-climate model CAM5.3-MARC-ARG. CAM5.3-MARC-ARG is a new configuration of the Community Atmosphere Model version 5.3 (CAM5.3) in which the default aerosol module has been replaced by the two-Moment, Multi-Modal, Mixing-stateresolving Aerosol model for Research of Climate (MARC). CAM5.3-MARC-ARG uses the default ARG aerosol activationscheme, consistent with the default configuration of CAM5.3. We compute differences between simulations using year-1850 aerosol emissions and simulations using year-2000 aerosol emissions in order to assess the radiative effects of anthropogenic aerosols. We compare the aerosol column burdens, cloud properties, and radiative effects produced by CAM5.3-MARC-ARG with those produced by the default configuration of CAM5.3, which uses the modal aerosol module with three lognormal modes (MAM3). Compared with MAM3, we find that MARC produces stronger cooling via the direct radiative effect, stronger cooling via the surface albedo radiative effect, and stronger warming via the cloud longwave radiative effect.The global mean cloud shortwave radiative effect is similar between MARC and MAM3, although the regional distributions differ. Overall, MARC produces a global mean net ERF of W m -2 , which is stronger than the global mean net ERF of W m -2 produced by MAM3. The regional distribution of ERF also differs between MARC and MAM3, largely due to differences in the regional distribution of the cloud shortwave radiative effect. We conclude that the specific representation of aerosols in global climate models, including aerosol mixing state, has important implications for climate modelling. IntroductionAerosol particles influence the earth's climate system by perturbing its radiation budget. There are three primary mechanisms by which aerosols interact with radiation. First, aerosols interact directly with radiation by scattering and absorbing solar and thermal infrared radiation (Haywood and Boucher, 2000). Second, aerosols interact indirectly with radiation by perturbing clouds, by acting as the cloud condensation nuclei on which cloud droplets form and the ice nuclei that facilitate freezing of cloud droplets (Fan et al., 2016;Rosenfeld et al., 2014): for example, an aerosol-induced increase in cloud cover would lead to increased scattering of "shortwave" solar radiation and increased absorption of "longwave" thermal infrared radiation. Third, aerosols can influence the albedo of the earth's surface (Ghan, 2013): for example, deposition of absorbing aerosol on snow reduces the albedo of the snow, causing more solar radiation to be absorbed at the earth's surface.The "effective radiative forcing" (ERF) of anthropogenic aerosols, defined as the top-of-atmosphere radiative effect caused by anthropogenic emissions of aerosols and aerosol precursors, is often used to quantify the radiative effects of aerosols (Boucher et al., 2013). The anthropogenic aerosol ERF is approximately equivalent to "the rad...

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Section: Aerosol Optical Depthmentioning

confidence: 94%

Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG

et al. 2018

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“…Large aerosol radiative forcing uncertainty has persisted through all Intergovernmental Panel on Climate Change assessment reports since 1996 despite substantial developments in climate model complexity (Flato et al, 2013, Sect. 9.1.3), numerous intercomparison projects Tsigaridis et al, 2014;Kim et al, 2014;Mann et al, 2014;Pan et al, 2015;Lacagnina et al, 2015;Kipling et al, 2016;Ghan et al, 2016;Koffi et al, 2016) and enormous investments in observing systems (Khain et al, 2000;Lacagnina et al, 2015;Seinfeld et al, 2016;Reddington et al, 2017). Reducing aerosol forcing uncertainty has therefore proven to be one of the most challenging and persistent problems in atmospheric science.…”

Section: Introductionmentioning

confidence: 99%

Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

et al. 2018

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Changes in aerosols cause a change in net topof-the-atmosphere (ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds, water vapour and temperature; and an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosolclimate model variants that span the model uncertainty due to 27 parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters account for around 80 % of the uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations), and these parameters also account for around 60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 to −2.37 W m −2 . This suggests the strongest forcings (below around −2.4 W m −2 ) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is 2 orders of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

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“…During this preprocessing, we aggregate the original Himawari‐8 gridded AOTs to a model horizontal resolution of 2° × 2° using the mean value of all the data points in the aggregation cell and assign a missing value for the grid where the number of effective Himawari‐8 observations is less than 320, which represents 20% of the maximum possible number of 0.05° × 0.05° Himawari‐8 observations in a 2° × 2° grid. The threshold value of 20% is applied to avoid representing coarse grid values with limited observations filled only within a portion of the coarse grid (Kikuchi et al, ; Schutgens et al, ). The gridded total observation error variances (

σ_{o}^{2}

) are estimated as the sum of the instrumental error variance (

σ_{i}^{2}

) and sample error variance (

σ_{s}^{2}

), or the so‐called spatial representative error variance (Zhang et al, ).…”

Section: Observational Data and Operatormentioning

confidence: 99%

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

Dai

Cheng

Suzuki

et al. 2019

J Adv Model Earth Syst

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The next‐generation geostationary satellite Himawari‐8 has a much higher observation frequency of the aerosol field than polar‐orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one‐monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari‐8‐retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non‐hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM‐SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four‐dimensional LETKF (4D‐LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari‐8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)‐ and AErosol RObotic NETwork (AERONET)‐retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root‐mean‐square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari‐8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D‐LETKF experiments are generally not so different, but the computational load of the 4D‐LETKF is much lighter than that of the LETKF.

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On the spatio-temporal representativeness of observations

Cited by 102 publications

References 36 publications

Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG

Effective radiative forcing in the aerosol–climate model CAM5.3-MARC-ARG

Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter

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