Publications by Philip Stier


tobac v1.0: towards a flexible framework for tracking and analysis of clouds in diverse datasets

Geoscientific Model Development Discussions Copernicus GmbH (2019) 1-31

M Heikenfeld, PJ Marinescu, M Christensen, D Watson-Parris, F Senf, SC van den Heever, P Stier

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. tobac provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data.&lt;/p&gt; </jats:p>


Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation

Atmospheric Chemistry and Physics Copernicus GmbH 19 (2019) 8591-8617

GS Fanourgakis, M Kanakidou, A Nenes, SE Bauer, T Bergman, KS Carslaw, A Grini, DS Hamilton, JS Johnson, VA Karydis, A Kirkevåg, JK Kodros, U Lohmann, G Luo, R Makkonen, H Matsui, D Neubauer, JR Pierce, J Schmale, P Stier, K Tsigaridis, T van Noije, H Wang, D Watson-Parris, DM Westervelt, Y Yang, M Yoshioka, N Daskalakis, S Decesari, M Gysel-Beer, N Kalivitis, X Liu, NM Mahowald, S Myriokefalitakis, R Schrödner, M Sfakianaki, AP Tsimpidi, M Wu, F Yu

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties.&lt;/p&gt; &lt;p&gt;There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of &lt;span class="inline-formula"&gt;−24&lt;/span&gt;&amp;amp;thinsp;% and &lt;span class="inline-formula"&gt;−35&lt;/span&gt;&amp;amp;thinsp;% for particles with dry diameters &lt;span class="inline-formula"&gt;&amp;amp;gt;50&lt;/span&gt; and &lt;span class="inline-formula"&gt;&amp;amp;gt;120&lt;/span&gt;&amp;amp;thinsp;nm, as well as &lt;span class="inline-formula"&gt;−36&lt;/span&gt;&amp;amp;thinsp;% and &lt;span class="inline-formula"&gt;−34&lt;/span&gt;&amp;amp;thinsp;% for CCN at supersaturations of 0.2&amp;amp;thinsp;% and 1.0&amp;amp;thinsp;%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (&lt;span class="inline-formula"&gt;&amp;amp;lt;0.1&lt;/span&gt;&amp;amp;thinsp;%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated &lt;span class="inline-formula"&gt;N&lt;sub&gt;3&lt;/sub&gt;&lt;/span&gt; (number concentration of particles with dry diameters larger than 3&amp;amp;thinsp;nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of &lt;span class="inline-formula"&gt;0.2&lt;/span&gt;&amp;amp;thinsp;% (CCN&lt;span class="inline-formula"&gt;&lt;sub&gt;0.2&lt;/sub&gt;&lt;/span&gt;) compared to that for &lt;span class="inline-formula"&gt;N&lt;sub&gt;3&lt;/sub&gt;&lt;/span&gt;, maximizing over regions where new particle formation is important.&lt;/p&gt; &lt;p&gt;An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter.&lt;/p&gt; &lt;p&gt;Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120&amp;amp;thinsp;nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40&amp;amp;thinsp;% during winter and 20&amp;amp;thinsp;% in summer.&lt;/p&gt; &lt;p&gt;In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB &lt;span class="inline-formula"&gt;−13&lt;/span&gt;&amp;amp;thinsp;% and &lt;span class="inline-formula"&gt;−22&lt;/span&gt;&amp;amp;thinsp;% for updraft velocities 0.3 and 0.6&amp;amp;thinsp;m&amp;amp;thinsp;s&lt;span class="inline-formula"&gt;&lt;sup&gt;−1&lt;/sup&gt;&lt;/span&gt;, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (&lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="48pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="9faa6b9bc700a00532091cfd69cae419"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00001.svg" width="48pt" height="14pt" src="acp-19-8591-2019-ie00001.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;) and to updraft velocity (&lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M16" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="9a0c289e263af38b17f8d2715a056c8f"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00002.svg" width="43pt" height="14pt" src="acp-19-8591-2019-ie00002.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities &lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="48pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="a47c1357bf9f8959859c5d28931197ed"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00003.svg" width="48pt" height="14pt" src="acp-19-8591-2019-ie00003.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt; and &lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="2e76a96aacff9b259f027c6bf554be27"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00004.svg" width="43pt" height="14pt" src="acp-19-8591-2019-ie00004.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.&lt;/p&gt; </jats:p>


Surprising similarities in model and observational aerosol radiative forcing estimates

Atmospheric Chemistry and Physics Discussions Copernicus GmbH (2019) 1-18

E Gryspeerdt, J Mülmenstädt, A Gettelman, FF Malavelle, H Morrison, D Neubauer, DG Partridge, P Stier, T Takemura, H Wang, M Wang, K Zhang

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the IPCC AR5 report. With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model-observational difference is thus vital to reduce uncertainty in the impact of aerosols on the climate.&lt;/p&gt; &lt;p&gt;These reported discrepancies arise from the different decompositions of the aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties. Cloud adjustments remain a significant source of uncertainty, particularly for ice clouds. However, they are consistent with the uncertainty from observation-based methods, with the liquid water path adjustment usually enhancing the Twomey effect by less than 50&amp;amp;thinsp;%. Depending on different sets of assumptions, this work suggests that model and observation-based estimates could be more equally weighted in future synthesis studies.&lt;/p&gt; </jats:p>


Detecting anthropogenic cloud perturbations with deep learning

Proceedings of the 36th International Conference on Machine Learning (2019)

D WATSON-PARRIS, M CHRISTENSEN, A Caterini, D SEJDINOVIC, P STIER


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

GEOSCIENTIFIC MODEL DEVELOPMENT 12 (2019) 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


Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation

Atmospheric Chemistry and Physics Discussions European Geosciences Union (2019)

GS Fanourgakis, M Kanakidou, A Nenes, S Bauer, T Bergman, KS Carslaw, A Grini, DS Hamilton, JS Johnson, VA Karydis, A Kirkevag, JK Kodros, U Lohmann, G Luo, R Makkonen, H Matsui, D Neubauer, JR Pierce, J Schmale, PHILIP STIER, K Tsigaridis, T van Noije, H Wang, D WATSON-PARRIS, DM Westervelt, Y Yang, M Yoshioka, N Daskalakis, S Decesari, M Gysel Beer, N Kalivitis, X Liu, NM Mahowald, S Myriokefalitakis, R Schrödner, M Sfakianaki, AP Tsimpidi, M Wu, F Yu


Increased water vapour lifetime due to global warming

Atmospheric Chemistry and Physics Discussions Copernicus GmbH (2019) 1-17

Ø Hodnebrog, G Myhre, BH Samset, K Alterskjær, T Andrews, O Boucher, G Faluvegi, D Fläschner, PM Forster, M Kasoar, A Kirkevåg, J-F Lamarque, D Olivié, TB Richardson, D Shawki, D Shindell, KP Shine, P Stier, T Takemura, A Voulgarakis, D Watson-Parris

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The relationship between changes in integrated water vapour (IWV) and precipitation can be characterized by quantifying changes in atmospheric water vapour lifetime. Precipitation isotope ratios correlate with this lifetime, a relationship that helps understand dynamical processes and may lead to improved climate projections. We investigate how water vapour and its lifetime respond to different drivers of climate change, such as greenhouse gases and aerosols. Results from 11 global climate models have been used, based on simulations where CO&lt;sub&gt;2&lt;/sub&gt;, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. A lifetime increase from 8 to 10&amp;amp;thinsp;days is projected between 1986&amp;amp;ndash;2005 and 2081&amp;amp;ndash;2100, under a business-as-usual pathway. By disentangling contributions from individual climate drivers, we present a physical understanding of how global warming slows down the hydrological cycle, due to longer lifetime, but still amplifies the cycle due to stronger precipitation/evaporation fluxes. The feedback response of IWV to surface temperature change differs somewhat between drivers. Fast responses amplify these differences and lead to net changes in IWV per degree surface warming ranging from 6.4&amp;amp;plusmn;0.9&amp;amp;thinsp;%/K for sulphate to 9.8&amp;amp;plusmn;2&amp;amp;thinsp;%/K for BC. While BC is the driver with the strongest increase in IWV per degree surface warming, it is also the only driver with a reduction in precipitation per degree surface warming. Consequently, increases in BC aerosol concentrations yield the strongest slowdown of the hydrological cycle among the climate drivers studied, with a change in water vapour lifetime per degree surface warming of 1.1&amp;amp;plusmn;0.4&amp;amp;thinsp;days/K, compared to less than 0.5&amp;amp;thinsp;days/K for the other climate drivers (CO&lt;sub&gt;2&lt;/sub&gt;, methane, solar irradiance, sulphate).&lt;/p&gt; </jats:p>


Contrasting response of precipitation to aerosol perturbation in the tropics and extra-tropics explained by energy budget considerations

Geophysical Research Letters American Geophysical Union (2019)

G DAGAN, P STIER, D WATSON-PARRIS


Aerosol effects on deep convection: The propagation of aerosol perturbations through convective cloud microphysics

Atmospheric Chemistry and Physics European Geosciences Union (2019)

M HEIKENFELD, B White, L Labbouz, P STIER


In-situ constraints on the vertical distribution of global aerosol

Atmospheric Chemistry and Physics Discussions European Geosciences Union (2019)

D WATSON-PARRIS, N Schutgens, C Reddington, K Pringle, D Liu, JA Allan, H Coe, K Carslaw, P STIER


The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity

Geoscientific Model Development Discussions Copernicus GmbH (2019) 1-52

D Neubauer, S Ferrachat, C Siegenthaler-Le Drian, P Stier, DG Partridge, I Tegen, I Bey, T Stanelle, H Kokkola, U Lohmann

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The global aerosol-climate model ECHAM6.3-HAM2.3 (E63H23) and the previous model versions ECHAM5.5-HAM2.0 (E55H20) and ECHAM6.1-HAM2.2 (E61H22) are evaluated using global observational datasets for clouds and precipitation. In E63H23 low cloud amount, liquid and ice water path and cloud radiative effects are more realistic than in previous model versions. E63H23 has a more physically based aerosol activation scheme, improvements in the cloud cover scheme, changes in detrainment of convective clouds, changes in the sticking efficiency for accretion of ice crystals by snow, consistent ice crystal shapes throughout the model, changes in mixed phase freezing and an inconsistency in ice crystal number concentration (ICNC) in cirrus clouds was removed. Biases that were identified in E63H23 (and in previous model versions) are a too low cloud amount in stratocumulus regions, deep convective clouds in the Atlantic and Pacific oceans form too close to the continents and there are indications that ICNCs are overestimated.&lt;/p&gt; &lt;p&gt;Since clouds are important for effective radiative forcing due to aerosol-radiation and aerosol-cloud interactions (ERF&lt;sub&gt;ari+aci&lt;/sub&gt;) and equilibrium climate sensitivity (ECS), also differences in ERF&lt;sub&gt;ari+aci&lt;/sub&gt; and ECS between the model versions were analyzed. ERF&lt;sub&gt;ari+aci&lt;/sub&gt; is weaker in E63H23 (&amp;amp;minus;1.0&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;sup&gt;&amp;amp;minus;2&lt;/sup&gt;) than in E61H22 (&amp;amp;minus;1.2&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;sup&gt;&amp;amp;minus;2&lt;/sup&gt;) (or E55H20; &amp;amp;minus;1.1&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;sup&gt;&amp;amp;minus;2&lt;/sup&gt;). This is caused by the weaker shortwave ERF&lt;sub&gt;ari+aci&lt;/sub&gt; (new aerosol activation scheme and sea salt emission parameterization in E63H23, more realistic simulation of cloud water) overcompensating the weaker longwave ERF&lt;sub&gt;ari+aci&lt;/sub&gt; (removal of an inconsistency in ICNC in cirrus clouds in E61H22).&lt;/p&gt; &lt;p&gt;The decrease in ECS in E63H23 (2.5&amp;amp;thinsp;K) compared to E61H22 (2.8&amp;amp;thinsp;K) is due to changes in the entrainment rate for shallow convection (affecting the cloud amount feedback) and a stronger cloud phase feedback.&lt;/p&gt; </jats:p>


Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity

Atmospheric Chemistry and Physics Copernicus GmbH 19 (2019) 6821-6841

S Fiedler, S Kinne, WTK Huang, P Räisänen, D O&amp;apos;Donnell, N Bellouin, P Stier, J Merikanto, T van Noije, R Makkonen, U Lohmann

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; This study assesses the change in anthropogenic aerosol forcing from the mid-1970s to the mid-2000s. Both decades had similar global-mean anthropogenic aerosol optical depths but substantially different global distributions. For both years, we quantify (i) the forcing spread due to model-internal variability and (ii) the forcing spread among models. Our assessment is based on new ensembles of atmosphere-only simulations with five state-of-the-art Earth system models. Four of these models will be used in the sixth Coupled Model Intercomparison Project (CMIP6; &lt;span class="cit" id="xref_altparen.1"&gt;&lt;a href="#bib1.bibx14"&gt;Eyring et al.&lt;/a&gt;, &lt;a href="#bib1.bibx14"&gt;2016&lt;/a&gt;&lt;/span&gt;). Here, the complexity of the anthropogenic aerosol has been reduced in the participating models. In all our simulations, we prescribe the same patterns of the anthropogenic aerosol optical properties and associated effects on the cloud droplet number concentration. We calculate the instantaneous radiative forcing (RF) and the effective radiative forcing (ERF). Their difference defines the net contribution from rapid adjustments. Our simulations show a model spread in ERF from &lt;span class="inline-formula"&gt;−0.4&lt;/span&gt; to &lt;span class="inline-formula"&gt;−0.9&lt;/span&gt;&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt;. The standard deviation in annual ERF is 0.3&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt;, based on 180 individual estimates from each participating model. This result implies that identifying the model spread in ERF due to systematic differences requires averaging over a sufficiently large number of years. Moreover, we find almost identical ERFs for the mid-1970s and mid-2000s for individual models, although there are major model differences in natural aerosols and clouds. The model-ensemble mean ERF is &lt;span class="inline-formula"&gt;−0.54&lt;/span&gt;&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt; for the pre-industrial era to the mid-1970s and &lt;span class="inline-formula"&gt;−0.59&lt;/span&gt;&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt; for the pre-industrial era to the mid-2000s. Our result suggests that comparing ERF changes between two observable periods rather than absolute magnitudes relative to a poorly constrained pre-industrial state might provide a better test for a model's ability to represent transient climate changes.&lt;/p&gt; </jats:p>


Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives.

Reviews of geophysics (Washington, D.C. : 1985) 56 (2018) 409-453

DP Grosvenor, O Sourdeval, P Zuidema, A Ackerman, MD Alexandrov, R Bennartz, R Boers, B Cairns, JC Chiu, M Christensen, H Deneke, M Diamond, G Feingold, A Fridlind, A Hünerbein, C Knist, P Kollias, A Marshak, D McCoy, D Merk, D Painemal, J Rausch, D Rosenfeld, H Russchenberg, P Seifert, K Sinclair, P Stier, B van Diedenhoven, M Wendisch, F Werner, R Wood, Z Zhang, J Quaas

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol-cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel-level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.


How Well Can We Represent the Spectrum of Convective Clouds in a Climate Model? Comparisons between Internal Parameterization Variables and Radar Observations

JOURNAL OF THE ATMOSPHERIC SCIENCES 75 (2018) 1509-1524

L Labbouz, Z Kipling, P Stier, A Protat


Understanding Rapid Adjustments to Diverse Forcing Agents

Geophysical Research Letters American Geophysical Union (2018)

C Smith, R Kramer, G Myhre, P Forster, T Andrews, O Boucher, D Fläschner, Ø Hodnebrog, M Kasoar, V Kharin, A Kirkevag, J-F Lamarque, J Mülmenstädt, D Olivié, T Richardson, B Samset, D Shindell, P STIER, T Takemura, A Voulgarakis, D WATSON-PARRIS


Quantifying the Importance of Rapid Adjustments for Global Precipitation Changes.

Geophysical research letters 45 (2018) 11399-11405

G Myhre, RJ Kramer, CJ Smith, Ø Hodnebrog, P Forster, BJ Soden, BH Samset, CW Stjern, T Andrews, O Boucher, G Faluvegi, D Fläschner, M Kasoar, A Kirkevåg, J-F Lamarque, D Olivié, T Richardson, D Shindell, P Stier, T Takemura, A Voulgarakis, D Watson-Parris

Different climate drivers influence precipitation in different ways. Here we use radiative kernels to understand the influence of rapid adjustment processes on precipitation in climate models. Rapid adjustments are generally triggered by the initial heating or cooling of the atmosphere from an external climate driver. For precipitation changes, rapid adjustments due to changes in temperature, water vapor, and clouds are most important. In this study we have investigated five climate drivers (CO2, CH4, solar irradiance, black carbon, and sulfate aerosols). The fast precipitation responses to a doubling of CO2 and a 10-fold increase in black carbon are found to be similar, despite very different instantaneous changes in the radiative cooling, individual rapid adjustments, and sensible heating. The model diversity in rapid adjustments is smaller for the experiment involving an increase in the solar irradiance compared to the other climate driver perturbations, and this is also seen in the precipitation changes.


Quantifying the Effects of Horizontal Grid Length and Parameterized Convection on the Degree of Convective Organization Using a Metric of the Potential for Convective Interaction

Journal of the Atmospheric Sciences American Meteorological Society 75 (2018) 425-450

BA White, AM Buchanan, CE Birch, P Stier, KJ Pearson


The chemistry-climate model ECHAM6.3-HAM2.3-MOZ1.0

GEOSCIENTIFIC MODEL DEVELOPMENT 11 (2018) 1695-1723

MG Schultz, S Stadtler, S Schroeder, D Taraborrelli, B Franco, J Krefting, A Henrot, S Ferrachat, U Lohmann, D Neubauer, C Siegenthaler-Le Drian, S Wahl, H Kokkola, T Kuehn, S Rast, H Schmidt, P Stier, D Kinnison, GS Tyndall, JJ Orlando, C Wespes


On the Limits of CALIOP for Constraining Modeled Free Tropospheric Aerosol

GEOPHYSICAL RESEARCH LETTERS 45 (2018) 9260-9266

D Watson-Parris, N Schutgens, D Winker, SP Burton, RA Ferrare, P Stier


Limited impact of sulfate-driven chemistry on black carbon aerosol aging in power plant plumes

AIMS Environmental Science American Institute of Mathematical Sciences (AIMS) 5 (2018) 195-215

M Z. Markovic, A E. Perring, R-S Gao, J Liao, A Welti, N L. Wagner, I B. Pollack, A M. Middlebrook, T B. Ryerson, M K. Trainer, C Warneke, J A. de Gouw, D W. Fahey, P Stier, J P. Schwarz

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