Publications by Nick Schutgens


On the limits of CALIOP for constraining modelled free‐tropospheric aerosol

Geophysical Research Letters American Geophysical Union 45 (2018) 9260-9266

D Watson-Parris, N Schutgens, D Winker, S Burton, R Ferrare, P Stier

The space‐borne Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument provides valuable information on the vertical distribution of global aerosol, and is often used to evaluate vertical aerosol distributions in General Circulation Models (GCMs). Here we show, however, that the detection limit of the CALIOP retrievals mean background aerosol is not detected, leading to substantially skewed statistics that moreover differ significantly by product. In the CALIOP Level 2 product this missing low‐backscatter aerosol results in the retrieved aerosol distribution significantly over‐representing aerosol backscatter and extinction in the mid‐ and upper‐troposphere if taken to be representative of the undetected aerosol. The CALIOP Level 3 product assumes no aerosol where none is detected which then leads to an under‐estimation in the aerosol extinction profile in the upper troposphere. Using the ECHAM‐HAM GCM, we estimate that CALIOP nighttime (daytime) retrievals miss 41% (44%) of aerosol mass in the atmosphere.


The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 1: Aerosol evaluation

Geoscientific Model Development Discussions Copernicus Publications 12 (2018) 1643-1677

I Tegen, D Neubauer, S Ferrachat, C Siegenthaler-Le Drian, I Bey, N Schutgens, P Stier, D Watson-Parris, T Stanelle, H Schmidt, S Rast, H Kokkola, M Schultz, S Schroeder, N Daskalakis, S Barthel, B Heinold, U Lohmann

We introduce and evaluate aerosol simulations with the global aerosol–climate model ECHAM6.3–HAM2.3, which is the aerosol component of the fully coupled aerosol–chemistry–climate model ECHAM–HAMMOZ. Both the host atmospheric climate model ECHAM6.3 and the aerosol model HAM2.3 were updated from previous versions. The updated version of the HAM aerosol model contains improved parameterizations of aerosol processes such as cloud activation, as well as updated emission fields for anthropogenic aerosol species and modifications in the online computation of sea salt and mineral dust aerosol emissions. Aerosol results from nudged and free-running simulations for the 10-year period 2003 to 2012 are compared to various measurements of aerosol properties. While there are regional deviations between the model and observations, the model performs well overall in terms of aerosol optical thickness, but may underestimate coarse-mode aerosol concentrations to some extent so that the modeled particles are smaller than indicated by the observations. Sulfate aerosol measurements in the US and Europe are reproduced well by the model, while carbonaceous aerosol species are biased low. Both mineral dust and sea salt aerosol concentrations are improved compared to previous versions of ECHAM–HAM. The evaluation of the simulated aerosol distributions serves as a basis for the suitability of the model for simulating aerosol–climate interactions in a changing climate.


The Global Aerosol Synthesis and Science Project (GASSP): measurements and modelling to reduce uncertainty

Bulletin of the American Meteorological Society American Meteorological Society September (2017) 1857-1878

CL Reddington, KS Carslaw, N Schutgens, H Coe, D Liu, J Allan, J Browse, KJ Pringle, LA Lee, JS Johnson, DV Spracklen, M Hermann, S Henning, H Wex, TB Kristensen, WR Leaitch, U Pöschl, D Rose, MO Andreae, B Anderson, GC Roberts, C Leck, X Chi, A Ding, Q Zhang

<p>Novel methodologies to quantify model uncertainty are combined with an extensive new database of in-situ aerosol microphysical and chemical measurements to reduce uncertainty in aerosol effects on climate.</p> <p>The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in-situ measurements of the particle size distribution, number concentration and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, an extensive global dataset of aerosol in-situ microphysical and chemical measurements, and new ways to assess the uncertainty associated with comparing sparse point measurements with low resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modellers and non-specialist users. Available measurements are extensive, but they biased to polluted regions of the northern hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model-data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.</p>


Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0

GEOSCIENTIFIC MODEL DEVELOPMENT 10 (2017) 1107-1129

E Di Tomaso, NAJ Schutgens, O Jorba, CP Garcia-Pando


On the spatio-temporal representativeness of observations

Atmospheric Chemistry and Physics Discussions European Geosciences Union (EGU) (2017)

NJ Schutgens, S Tsyro, E Gryspeerdt, D Goto, N Weigum, M Schulz, P Stier

The discontinuous spatio-temporal sampling of observations has an impact when using them to construct climatologies or evaluate models. Here we provide estimates of this so-called representation error for a range of time and length-scales (semi-annually down to sub-daily, 300 to 50 km) and show that even after substantial averaging of data significant representation errors may remain, larger than typical measurement errors. Our study considers a variety of observations: ground-site remote sensing or in-situ (PM2.5, black carbon mass or number concentrations), satellite remote sensing with imagers or LIDARs (extinction). We show that observational coverage (a measure of how dense the spatio-temporal sampling of the observations is) is not an effective metric to limit representation errors. Different strategies to construct monthly satellite L3 data are assessed and temporal averaging of spatially aggregated observations (super-observations) is found to be the best, although it still allows for significant representation errors. Temporal collocation of data (only possible in the context of evaluating model data with observations) can be very effective at reducing representation errors even when spatial sampling issues remain (e.g. when using ground-sites). We also show that ground-based and wide-swath imager satellite remote sensing data give rise to similar representation errors although their observational sampling is different. Finally, emission sources and orography can lead to representation errors that are very hard to reduce even with substantial temporal averaging.


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

Atmos. Chem. Phys. 16 (2016) 6335-6353

NAJ Schutgens, E Gryspeerdt, N Weigum, S Tsyro, D Goto, M Schulz, P Stier


Effect of aerosol sub-grid variability on aerosol optical depth and cloud condensation nuclei: Implications for global aerosol modelling

Atmospheric Chemistry and Physics Discussions Copernicus Publications 16 (2016) 13619-13639

N Weigum, N Schutgens, P Stier

A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid-boxes, which can lead to discrepancies in simulated aerosol climate effects between high and low resolution models. This study investigates the impact of neglecting sub-grid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. <br/><br/> We compare WRF-Chem runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. Processes most affected by neglecting aerosol sub-grid variability are gas-phase chemistry and aerosol uptake of water through aerosol/gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol parameters when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of sub-grid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.


Community Intercomparison Suite (CIS) v1.3.2: A tool for intercomparing models and observations

Geoscientific Model Development Discussions European Geosciences Union (2016)

D Watson-Parris, N Schutgens, N Cook, Z Kipling, P Kershaw, E Gryspeerdt, B Lawrence, P Stier

The Community Intercomparison Suite (CIS) is an easy-to-use command-line tool which has been developed to allow the straightforward intercomparison of remote sensing, in-situ and model data. While there are a number of tools available for working with climate model data, the large diversity of sources (and formats) of remote sensing and in-situ measurements necessitated a novel software solution. Developed by a professional software company, CIS supports a large number of gridded and ungridded data sources "out-of-the-box", including climate model output in NetCDF or the UK Met Office pp file format, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), MODIS (MODerate resolution Imaging Spectroradiometer), Cloud and Aerosol CCI (Climate Change Initiative) level 2 satellite data, and a number of in-situ aircraft and ground station datasets. The open-source architecture also supports user defined "plugins" to allow many other sources to be easily added. Many of the key operations required when comparing heterogenous datasets are provided by CIS, including subsetting, aggregating, collocating and plotting the data. Output data is written to CF-compliant NetCDF files to ensure interoperability with other tools and systems. The latest documentation, including a user manual and installation instructions can be found on our website (http://cistools.net). Here we describe the need which this tool fulfils, followed by descriptions of its main functionality (as at version 1.3.2) and plugin architecture which make it unique in the field.


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

Atmospheric Chemistry and Physics European Geosciences Union 16 (2016) 1065-1079

NAJ Schutgens, DG Partridge, P Stier

<p>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 %).</p> <p>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.</p> <p>We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation.</p> <p>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.</p>


Effect of aerosol subgrid variability on aerosol optical depth and cloud condensation nuclei: implications for global aerosol modelling

Atmospheric Chemistry and Physics Copernicus Publications (2016)

N Weigum, N Schutgens, P Stier

A fundamental limitation of grid-based models is their inability to resolve variability on scales smaller than a grid box. Past research has shown that significant aerosol variability exists on scales smaller than these grid boxes, which can lead to discrepancies in simulated aerosol climate effects between high- and low-resolution models. This study investigates the impact of neglecting subgrid variability in present-day global microphysical aerosol models on aerosol optical depth (AOD) and cloud condensation nuclei (CCN). We introduce a novel technique to isolate the effect of aerosol variability from other sources of model variability by varying the resolution of aerosol and trace gas fields while maintaining a constant resolution in the rest of the model. <br/><br/> We compare WRF-Chem (Weather and Research Forecast model) runs in which aerosol and gases are simulated at 80 km and again at 10 km resolutions; in both simulations the other model components, such as meteorology and dynamics, are kept at the 10 km baseline resolution. We find that AOD is underestimated by 13 % and CCN is overestimated by 27 % when aerosol and gases are simulated at 80 km resolution compared to 10 km. The processes most affected by neglecting aerosol subgrid variability are gas-phase chemistry and aerosol uptake of water through aerosol–gas equilibrium reactions. The inherent non-linearities in these processes result in large changes in aerosol properties when aerosol and gaseous species are artificially mixed over large spatial scales. These changes in aerosol and gas concentrations are exaggerated by convective transport, which transports these altered concentrations to altitudes where their effect is more pronounced. These results demonstrate that aerosol variability can have a large impact on simulating aerosol climate effects, even when meteorology and dynamics are held constant. Future aerosol model development should focus on accounting for the effect of subgrid variability on these processes at global scales in order to improve model predictions of the aerosol effect on climate.


Jury is still out on the radiative forcing by black carbon

Proceedings of the National Academy of Sciences of USA National Academy of Sciences (2016)

O Boucher, Y Balkanski, Ø Hodnebrog, C Lund Myhre, G Myhre, J Quaas, BH Samset, N Schutgens, P Stier, R Wang

The jury is still out on the question of the net climate impact of BC and how much climate cobenefit will result from the necessary mitigation of BC emissions.


Effects of data assimilation on the global aerosol key optical properties simulations

ATMOSPHERIC RESEARCH 178 (2016) 175-186

X Yin, T Dai, NAJ Schutgens, D Goto, T Nakajima, G Shi


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

Atmospheric Chemistry and Physics Discussions 2016 (2016)

NAJ Schutgens, E Gryspeerdt, N Weigum, S Tsyro, D Goto, M Schulz, P Stier

© Author(s) 2016. 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 air-borne observations concern 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 RMS differences between these perfect observations and perfect global models can easily amount to 30-160%, for a range of observables like AOT (Aerosol Optical Thickness), extinction, black carbon mass concentrations, PM2.5, 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 three-fold reduction i.c. AOT), than for others (e.g. a two-fold 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.


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

ATMOSPHERIC CHEMISTRY AND PHYSICS 15 (2015) 7173-7193

A Veira, S Kloster, NAJ Schutgens, JW Kaiser


Development of Seamless Chemical Assimilation System and Its Application for Atmospheric Environmental Materials

シミュレーション 小宮山印刷工業 34 (2015) 104-114

T Nakajima, R Imasu, A Takami, D Goto, H Tsuruta, J Uchida, T Dai, S Misawa, K Ueda, CFS Ng, C Watanabe, S Konishi, Y Sato, A Higuchi, Y Masutomi, A Murakami, K Tsuchiya, H Kondo, Y Niwa, K Yoshimura, T Ohara, Y Morino, N Schutgens, K Sudo, T Takemura, T Inoue, Y Arai, R Murata, R Yonemoto, TTN Trieu, M Uematsu, M Satoh, H Tomita, H Yashiro, M Hara


Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model.

Environmental pollution (Barking, Essex : 1987) 195 (2014) 319-329

T Dai, NAJ Schutgens, D Goto, G Shi, T Nakajima

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only.


C459 大気環境物質のためのシームレス同化システム構築とその応用(都市における気候変動適応研究の最先端,専門分科会)

大会講演予講集 日本気象学会 105 (2014)

映 中島, 良 今須, 光 植松, 昭 高見, 大 五藤, 純 打田, 豊 井上, 治 鶴田, 翔 三澤, 諒 村田, T Dai, N Schutgens, 佳 上田, C-F-S Ng, 正 佐藤, 陽 佐藤, 暁 村上, 篤 樋口, SALSAプロジェクトチーム


A pathway analysis of global aerosol processes

Atmospheric Chemistry and Physics Copernicus GmbH 14 (2014) 11657-11686

N Schutgens, P Stier

We present a detailed budget of the changes in atmospheric aerosol mass and numbers due to various processes: emission (including instant condensation of soluble biogenic emissions), nucleation, coagulation, H2SO4 condensation and in-cloud production, aging and deposition. The budget is created from monthly averaged tracer tendencies calculated by the global aerosol model ECHAM5.5-HAM2 and allows us to investigate process contributions at various length-scales and timescales. As a result, we show in unprecedented detail what processes drive the evolution of aerosol. In particular, we show that the processes that affect aerosol masses are quite different from those that affect aerosol numbers. Condensation of H2SO4 gas onto pre-existing particles is an important process, dominating the growth of small particles in the nucleation mode to the Aitken mode and the aging of hydrophobic matter. Together with in-cloud production of H2SO4, it significantly contributes to (and often dominates) the mass burden (and hence composition) of the hydrophilic Aitken and accumulation mode particles. Particle growth itself is the leading source of number densities in the hydrophilic Aitken and accumulation modes, with their hydrophobic counterparts contributing (even locally) relatively little. As expected, the coarse mode is dominated by primary emissions and mostly decoupled from the smaller modes. Our analysis also suggests that coagulation serves mainly as a loss process for number densities and that, relative to other processes, it is a rather unimportant contributor to composition changes of aerosol. The analysis is extended with sensitivity studies where the impact of a lower model resolution or pre-industrial emissions is shown to be small. We discuss the use of the current budget for model simplification, prioritization of model improvements, identification of potential structural model errors and model evaluation against observations.


Simulated aerosol key optical properties over global scale using an aerosol transport model coupled with a new type of dynamic core

ATMOSPHERIC ENVIRONMENT 82 (2014) 71-82

T Dai, D Goto, NAJ Schutgens, X Dong, G Shia, T Nakajima


Improvement of the retrieval algorithm for GOSAT SWIR XCO<inf>2</inf>and XCH<inf>4</inf>and their validation using TCCON data

Atmospheric Measurement Techniques 6 (2013) 1533-1547

Y Yoshida, N Kikuchi, I Morino, O Uchino, S Oshchepkov, A Bril, T Saeki, N Schutgens, GC Toon, D Wunch, CM Roehl, PO Wennberg, DWT Griffith, NM Deutscher, T Warneke, J Notholt, J Robinson, V Sherlock, B Connor, M Rettinger, R Sussmann, P Ahonen, P Heikkinen, E Kyrö, J Mendonca, K Strong, F Hase, S Dohe, T Yokota

The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (-8.85 and 4.75 ppm for XCO2 and -20.4 and 18.9 ppb for XCH 4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (-1.48 and 2.09 ppm for XCO2 and -5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas. © Author(s) 2013.

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