The Global Carbon Project’s TRENDY MIP, the 2018 European Drought MIP, the SDGVM Model, and me

By: Patrick C. McGuire

The annual TRENDY MIP was started in 2010 in order to compare different land models and their ability to model the sources and sinks of carbon in the land (i.e., to/from the soil and the vegetation from/to the atmosphere). TRENDY provides model-mean and model-spread estimates of the uptake of carbon by the land throughout the globe for the Global Carbon Project, resulting in an annual publication about the Global Carbon Budget. The Global Carbon Budget attempts to balance the carbon budget for the entire globe, including the anthropogenic carbon emissions (whether from burning fossil fuels or from changes in land use) and the changing carbon reservoirs in the ocean, atmosphere, and the land. As far as I know, TRENDY is not a typical acronym, where each letter corresponds to a word, but instead, it refers to ‘trends’ in the carbon stores and fluxes with time.

I arrived at the University of Reading from my former home in Berlin in July 2017 to work as a Land Surface Processes Computational Scientist here. My involvement in the Global Carbon Project’s TRENDY MIP can be traced back to January 2018 when Tristan Quaife (University of Reading) asked me to convert data for the CALIBRE agroforestry project. He wanted me to take the forcing/driving weather data from ISIMIP up to the year 2100, which was in NETCDF format, and convert it to an ASCII/text format used by the Sheffield Dynamic Global Vegetation Model (SDGVM; Woodward, Smith, and Emanuel, 1995; Woodward and Lomas, 2004; Walker et al., 2017). The idea was to then run SDGVM with this weather data and with a variety of agroforestry reforestation scenarios in the UK in order to see if there was an optimal agroforestry strategy. I was not involved in running the SDGVM model further for the CALIBRE agroforestry project, but my work on converting the driving weather data for SDGVM did catch the eyes of both Tristan and his colleague, Anthony Walker. Anthony has been a developer of SDGVM for a number of years, and he is based at Oak Ridge National Laboratory in Tennessee, in the USA.

Anthony and Tristan subsequently asked me in March 2019 if I could use SDGVM to participate in a MIP for comparing land models for the European Drought in 2018, which has been led by Ana Bastos. Ana was then at Ludwig Maximilians University in Munich, and she is now at the Max Planck Institute for Biogeochemistry in Jena, Germany. For this project on the 2018 European Drought, I was able, firstly, to adapt Anthony’s R scripts to convert the ERA5 driving data (from the NETCDF format to SDGVM text format) over the European region for 1979-2018 that Ana had provided; secondly, to run SDGVM with this converted data; and thirdly, to produce the daily and monthly NETCDF output files (converting from SDGVM text format to NETCDF format) for SDGVM. I then delivered those NETCDF output files from SDGVM to Ana, which she analyzed for the 2018 European Drought MIP. She has subsequently published three first-author journal papers, including myself as the unique SDGVM co-author, in Bastos et al. (2020a-b, 2021). One rule for the 2018 European Drought MIP was that only one member of each model’s team could be a co-author. This rule was passed down to the 2018 European Drought MIP from the TRENDY MIP’s set of rules. Otherwise, surely Anthony and Tristan would have been co-authors, as well.

From what I understand, some years earlier, Tristan had approached the TRENDY MIP organizers, suggesting that Anthony, as a SDGVM expert, could run SDGVM for the TRENDY MIP, and hence, SDGVM could further complement the other DGVMs that were already part of the TRENDY MIP. Anthony was able to get involved in the TRENDY MIP with SDGVM, and the first year that he was a co-author on the subsequent annual Global Carbon Budget paper was in 2016 (Le Quére et al. 2016). Anthony also ran SDGVM for the TRENDY MIPs, which were published in 2017, 2018, and 2020. Due to my work on the 2018 European Drought with SDGVM, Anthony and Tristan asked me to run SDGVM for the TRENDY MIP, which was published as part of the Global Carbon Budget in 2019 (Friedlingstein et al. 2019). This involved the conversion of CRU weather input data (Harris et al. 2014) from NETCDF format to SDGVM text format and the landcover data (Land-Use Harmonization based upon LUH2 v2h from 1700-1950, and based upon HYDE from 1951-2018) from NETCDF to the SDGVM text format; running the SDGVM under several different TRENDY experimental protocols; and converting the SDGVM text-formatted output files to NETCDF format. This sounds straightforward, but it also involves checking for mistakes and ensuring that the output data is reasonable and makes sense. Overall, this part of the process started for me in mid-July 2019, and I delivered the first SDGVM runs to the TRENDY team in mid-August 2019, and the publication of the GCB paper was in December 2019. The Global Carbon Budget paper in 2019 already has almost 800 citations! Anthony did the SDGVM runs for TRENDY in 2020, and I was asked to do the SDGVM runs for TRENDY this year (2021), wherein I made my first delivery this year in mid-September. The 2021 Global Carbon Budget publication (Friedlingstein et al. 2021) should be submitted in a week or so from the time of publication of this blog. So stay tuned!


Bastos, A., P. Ciais, P. Friedlingstein, S. Sitch, J. Pongratz, L. Fan, J.-P. Wigneron, U. Weber, M. Reichstein, Z. Fu, P. Anthoni, A. Arneth, V. Haverd, A.K. Jain, E. Joetzjer, J. Knauer, S. Lienert, T. Loughran, P.C. McGuire, H. Tian, N. Viovy, and S. Zaehle, “Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity”Science Advances, 6, 24, EABA2724 (2020a).

Bastos, A., Z. Fu, P. Ciais, P. Friedlingstein, S. Sitch, J. Pongratz, U. Weber, M. Reichstein, P. Anthoni, A. Arneth, V. Haverd, A. Jain, E. Joetzjer, J. Knauer, S. Lienert, T. Loughran, P.C. McGuire, W. Obermeier, R.S. Padrón, H. Shi, H. Tian, N. Viovy, and S. Zaehle, “Impacts of extreme summers on European ecosystems: a comparative analysis of 2003, 2010 and 2018”Philosophical Transactions of the Royal Society B, 375, 20190507 (2020b).

Bastos, A., R. Orth, M. Reichstein, P. Ciais, N. Viovy, S. Zaehle, P. Anthoni, A. Arneth, P. Gentine, E. Joetzjer, S. Lienert, T. Loughran, P.C. McGuire, J. Pongratz, and S. Sitch, “Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019”Earth System Dynamics, 12, pp. 1015-1035 (2021).

Friedlingstein, P., M.W. Jones, M. O’Sullivan, R.M. Andrew, J. Hauck, G.P. Peters, W. Peters, J. Pongratz, S. Sitch, C. Le Quéré, D.C.E. Bakker, J.G. Canadell, P. Ciais, R. Jackson, P. Anthoni, L. Barbero, A. Bastos, V. Bastrikov, M. Becker, L. Bopp, E. Buitenhuis, N. Chandra, F. Chevallier, L.P. Chini, K.I. Currie, R.A. Feely, M. Gehlen, D. Gilfillan, T. Gkritzalis, D.S. Goll, N. Gruber, S. Gutekunst, I. Harris, V. Haverd, R.A. Houghton, G. Hurtt, T. Ilyina, A.K. Jain, E. Joetzjer, J.O. Kaplan, E. Kato, K. Klein Goldewijk, J.I. Korsbakken, P. Landschützer, S.K. Lauvset, N. Lefèvre, A. Lenton, S. Lienert, D. Lombardozzi, G. Marland, P.C. McGuire, J.R. Melton, N. Metzl, D.R. Munro, J.E.M.S. Nabel, S.-I. Nakaoka, C. Neill, A.M. Omar, T. Ono, A. Peregon, D. Pierrot, B. Poulter, G. Rehder, L. Resplandy, E. Robertson, C. Rödenbeck, R. Séférian, J. Schwinger, N. Smith, P.P. Tans, H. Tian, B. Tilbrook, F.N. Tubiello, G.R. van der Werf, A.J. Wiltshire, and S. Zaehle, “Global Carbon Budget 2019”Earth System Science Data, 11, pp. 1783-1838 (2019). 

Friedlingstein, P., M.W. Jones, M. O’Sullivan, R.M. Andrew, D.C.E. Bakker, J. Hauck, C. Le Quéré, G.P. Peters, W. Peters, J. Pongratz, S. Sitch, J.G. Canadell, P. Ciais, R.B. Jackson, P. Anthoni, N.R. Bates, M. Becker, L. Bopp, T.T.T. Chau, F. Chevallier, L.P. Chini, M. Cronin, K.I. Currie, L. Djeutchouang, X. Dou, W. Evans, R.A. Feely, L. Feng, T. Gasser, D. Gilfillan, T. Gkritzalis, G. Grassi, L. Gregor, N. Gruber, Ö. Gürses, I. Harris, R.A. Houghton, G.C. Hurtt, Y. Iida, T. Ilyina, I.T. Luijkx, A. Jain, S.D. Jones, E. Kato, D. Kennedy, K.K. Goldewijk, J. Knauer, J.I. Korsbakken, A. Körtzinger, P. Landschützer, S.K. Lauvset, N. Lefèvre, S. Lienert, J. Liu, G. Marland, P.C. McGuire, J.R. Melton, D.R. Munro, J.E.M.S. Nabel, S.-I. Nakaoka, Y. Niwa, T. Ono, D. Pierrot, B. Poulter, G. Rehder, L. Resplandy, E. Robertson, M. Rocher, C. Rödenbeck, J. Schwinger, C. Schwingshackl, R. Séférian, A.J. Sutton, T. Tanhua, P.P. Tans, H. Tian, B. Tilbrook, F. Tubiello, G. van der Werf, N. Vuichard, R. Wanninkhof, A.J. Watson, D. Willis, A.J. Wiltshire, W. Yuan, C. Yue, X. Yue, S. Zaehle, and J. Zeng, “ESSD Global Carbon Budget 2021”. Earth System Science Data Discussions (2021, in preparation).

Harris, I., P.D. Jones, T.J. Osborn, and D.H. Lister. “Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset”Int. J. Climatol., 34, 623–642 (2014).

Le Quéré, C., R.M. Andrew, J.G. Canadell, S. Sitch, J.I. Korsbakken, G.P. Peters, A.C. Manning, T.A. Boden, P.P. Tans, R.A. Houghton, R.F. Keeling, S. Alin, O.D. Andrews, P. Anthoni, L. Barbero, L. Bopp, F. Chevallier, L.P. Chini, P. Ciais, K. Currie, C. Delire, S.C. Doney, P. Friedlingstein, T. Gkritzalis, I. Harris, J. Hauck, V. Haverd, M. Hoppema, K. Klein Goldewijk, A.K. Jain, E. Kato, A. Körtzinger, P. Landschützer, N. Lefèvre, A. Lenton, S. Lienert, D. Lombardozzi, J.R. Melton, N. Metzl, F. Millero, P.M.S. Monteiro, D.R. Munro, J.E.M.S. Nabel, S.-I. Nakaoka, K. O’Brien, A. Olsen, A.M. Omar, T. Ono, D. Pierrot, B. Poulter, C. Rödenbeck, J. Salisbury, U. Schuster, J. Schwinger, R. Séférian, I. Skjelvan, B.D. Stocker, A.J. Sutton, T. Takahashi, H. Tian, B. Tilbrook, I.T. van der Laan-Luijkx, G.R. van der Werf, N. Viovy, A.P. Walker, A.J. Wiltshire, and S. Zaehle. “Global Carbon Budget 2016”Earth System Science Data, 8 (2016).

Walker, A. P., T. Quaife, P.M. van Bodegom, M.G. De Kauwe, T.F. Keenan, J. Joiner, M.R. Lomas, N. MacBean, C.G. Xu, X.J. Yang, and F.I. Woodward. “The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (V-cmax) on global gross primary production”New Phytol., 215, 1370–1386 (2017).

Woodward, F.I., T.M. Smith, W.R. Emanuel.  “A global land primary productivity and phytogeography model”Global Biogeochemical Cycles, 9, 471– 490 (1995).  

Woodward, F.I., and M.R. Lomas.  “Vegetation dynamics – simulating responses to climatic change”Biological Review, 79, 643– 670 (2004).

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Regional vs Global Models From The Perspective of a Polar Climate Scientist

By: Charlotte Lang

There is a debate in the world of polar climate and ice sheet surface modelling about global (GCM) versus regional (RCM) models and each side is trying to convince the other that they do better: global modellers insist that regional models don’t add much to the topic while regional modellers try to convince them that their models can complement or even improve the outputs of global models.

Although originally I was a regional modeller as a user of MAR (Modèle Atmosphérique Régional, Fettweis et al., 2017) in Liège, Belgium, I have since joined the dark side as a global modeller in December 2020 when I started a postdoc at NCAS. 

Do these 2 types of models really have to be opposed? Can’t they be used in a complementary way?

Let’s review a few of the advantages of each of them.

Resolution: advantage RCMs

For a long time, the biggest advantage of regional climate models was their ability to run at a higher spatial resolution (~10 km) than highly time consuming GCMs (~100 km), allowing them to better represent smaller scale processes and to be calibrated for specific climates (Fig. 1). For Greenland for example, it means a better representation of the narrow ablation zone, the marginal area of the ice sheet where the amount of melt occurring at the surface in summer exceeds the mass gained in winter through snowfall. In Svalbard, it means a better representation of the hilly topography that gave its name to the main island Spitsbergen (“pointed mountains” in Dutch) and a better representation of orographic precipitation.

Figure 1: Representation of GCMs and RCMs. Source: Ambrizzi et al. (2018); Figure 1.

On the other hand, running on a limited area means RCMs have to be told what happens at their boundaries, which have to be “forced” at regular time intervals by the outputs of a global model. That is the biggest criticism one can have against RCMs: they can only be as good as the model used to force their boundaries. Feed them with a “good” GCM and they might even improve their results (and therefore a better representation of orographic precipitation); feed them with a biased GCM and their outputs will display larger biases as well.

Ice sheet surface processes: advantage RCM

When the progress in computing allowed GCMs to increase their spatial resolution, us polar climate and ice sheet surface modellers could still argue that we could better simulate the interactions between the climate and the ice sheets surface with RCMs. Indeed, some RCMs such as Liège’s MAR or IMAU’s RACMO (Noël et al., 2018) include snow modules allowing explicit simulation of the energy and mass transfer between the atmosphere and the surface of the ice sheets. Global models for their part didn’t include such complex models and users had to resort to forcing simpler and often empirical snow models with their climate, missing some of the feedback between the climate and the snow surface.

The rise of Earth System Models: advantage GCMs

More recently, regional climate models suffered a blow with the development of a new class of global models, Earth System Models (ESM). ESMs, like the British UKESM (Sellar et al., 2019), are complex models coupling many components of the Earth System: atmosphere, ocean, vegetation, biogeochemistry… UKESM also includes a complex model for the surface of ice sheets coupled to a thermo-mechanical ice sheet model (Smith et al., 2021) simulating the dynamics of the ice sheets, allowing it to take into account the effect of changing ice sheets on the climate and vice-versa. This feature is of particular interest in long term future projections (>2100) as the surface of the Greenland ice sheet is expected to lower as it melts, further increasing the near-surface temperature and therefore surface melt through the melt-elevation feedback that RCMs and their fixed geometry can’t represent (Fig. 2). A changing ice sheet geometry could also modify the atmospheric circulation on and around the ice sheet, including a weakening (resp. strengthening) of the katabatic winds, which could further enhance (resp. dampen) the positive feedback loop.

Figure 2: Melt-elevation feedback in a fixed vs dynamical ice sheet geometry.

Efforts have been made to couple RCMs to ice sheet models, like the PARAMOUR-EOS project ( aiming at coupling several RCMs to ice sheet and ocean models over the Greenland and Antarctic ice sheets but the use of coupled models is still quite marginal in polar climatology.

Furthermore, the system of elevation classes in UKESM allows it to downscale the climate and surface variables needed to force the ice sheet model from the lower resolution atmospheric grid onto a much higher resolution ice sheet grid. And here one of the last advantages of working with an RCM disappears.

Yet regional climate models are still useful in many aspects. Forced by reanalyses, they can rapidly produce high resolution simulations of regional weather events like the extreme melt of the surface of the Greenland Ice sheet in 2012 or the deadly floods that affected Belgium and Germany this summer. Forced by an ESM climate, they can rapidly run with a focus on specific surface ice sheet processes whose numerical representation need improving or are yet to be included in the models. RCMs are not dead (yet)!


[1] Ambrizzi, T., M. Reboita, R. Rocha, and M. Llopart, 2018:  The state-of-the-art and fundamental aspects of regional climate modeling in South America. Annals of the New York Academy of Sciences, 1436,

[2] Fettweis, X., and Coauthors, 2017: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model. The Cryosphere, 11, 1015–1033,

[3] Noël, B., and Coauthors, 2018: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958 – 2016). The Cryosphere, 12, 811–831,

[4] Sellar, A. A., and Coauthors, 2018: UKESM1: Description and evaluation of the U.K. Earth System Model. Journal of Advances in Modeling Earth Systems, 11, 4513–4558,

[5] Smith, R. S., and Coauthors, 2021: Coupling the U.K. Earth System Model to dynamic models of the Greenland and Antarctic ice sheets. Journal of Advances in Modeling Earth Systems, 13, e2021MS002520,


Posted in Climate, Climate modelling, Cryosphere, Polar | Leave a comment

Modelling Dust Extremes Over East Asia

 By: Dhirendra Kumar

Mineral dust plays an important role in the earth system due to its interaction with climate, ecosystems, biogeochemical cycles, and human society in direct and indirect ways [1,2,3]. Their interactions with the weather and climate occur at multiple spatial and temporal scales. While dust aerosols affect the radiation budget by absorbing, reflecting, and scattering the incoming solar and outgoing terrestrial radiation, they can have many more indirect consequences in terms of modification of the cloud microphysical properties to changes in the hydrological cycles. These mineral dust aerosols mostly originate over the semi-arid to arid regions of the world such as Northern Africa, Arabian Peninsula, Southwest Asia, and northern Chinese region in the northern hemisphere [4]. In terms of the direct impacts of dust, it affects the visibility and air quality over downstream regions where dust is transported towards, thus affecting the well-being of the exposed population.

Figure 1: Atmospheric haze over Beijing during a recent massive dust storm on 15 Mar 2021.

The extreme episodes of dust over the East Asian region are very frequent and affect the major cities in China, Japan, South Korea, and far east regions. With the highest frequency during the spring season, the individual events may occur and persist for 2-3 days at least and have a wide range of impacts in the day-to-day life of large populations. A recent massive dust storm during March 2021 is a recent examples (Fig. 1) when compounded by the hot and dry conditions and heavy north-westerly winds, the Beijing skies experienced one of the worst air quality episodes in a decade [5].

Using a combination of the state-of-the-art CAMS (Copernicus Atmospheric Monitoring Service) reanalysis [6] which includes information on dust aerosols, as well as high-resolution climate model simulations, we are trying to understand the characteristics of these dust storms and their associated meteorological drivers. Based on case studies of extreme dust episodes, we try to understand the contrasting meteorological drivers for different cases having impacts over different regions. We find that extreme dust emission episodes originate over the Gobi and inner Mongolian deserts resulting in high dust aerosol load over the adjacent regions such as the Taklamakan desert, Gobi Desert, Beijing, and other far Eastern areas due to different transport conditions. An animation showing impacts in terms of high dust AOD (Aerosol Optical Depth) values over the larger East Asian region is shown in Fig. 2. The dust can also reach up to the southern parts of the Chinese territory and thus engulfs major cities including Beijing, Shanghai, and Nanjing.Figure 2: Daily variation of Dust Aerosol Optical Depth over the East Asia region during the spring from the CAMS dataset.

We also look at the large-scale meteorological conditions during these events using the reanalysis as well as the model simulations. We find that the high-resolution model can resolve the major characteristics of these dust storms and provide greater insights about the spatial structure of the dust emissions and associated impacts over the region. The models also accurately represent the large-scale meteorological drivers such as the surface and low-level circulations, and the synoptic conditions. Although the models can efficiently resolve the broad-scale features during extreme dust episodes, they have inherent systematic errors, as they overestimate the dust emission while underestimating the AOD values.

The experiences gained from such investigations provide further avenues for understanding the extreme dust cycle, its behaviour, and complex interactions within the climate system from the perspective of climate change. In addition, it also highlights the strengths and weaknesses of the model outlining the need for further improvements in the representation of processes related to mineral dust emission and transport.


[1] Kaufman, Y., D. Tanré, and O. Boucher, 2002: A satellite view of aerosols in the climate system. Nature, 419, 215-223, doi:10.1038/nature01091.

[2] Li, Z., F. Niu, J. Fan, Y. Liu, D. Rosenfeld, and Y. Ding, 2011: Long-term impacts of aerosols on the vertical development of clouds and precipitation. Nature Geoscience, 4, 888-894, doi:10.1038/ngeo1313.

[3] Choobari, O., P. Zawar-Reza, and A. Sturman, 2014: The global distribution of mineral dust and its impacts on the climate system: A review. Atmospheric Research, 138, 152-165, doi:10.1016/j.atmosres.2013.11.007.

[4] Ginoux, P., J. Prospero, T. Gill, N. Hsu, and M. Zhao, 2012: Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Reviews of Geophysics, 50, doi:10.1029/2012rg000388.

[5] 2021: NPR Cookie Consent and Choices.,. (Accessed September 16, 2021).

[6] 2021: China sandstorms highlight threat of climate crisis. the Guardian,. (Accessed September 16, 2021).

Posted in Aerosols, Air quality, China, Climate, Climate modelling, Weather | Leave a comment

Using measurements of cloud ice to evaluate frozen particle scattering models

By: Karina McCusker

How do we measure cloud ice, and why do we need to?

Ice particles in clouds have complex geometries, making them more difficult to understand than droplets. As a result, ice clouds are a source of uncertainty in weather and climate simulations. To improve this, high-quality global observations are required. Microwave remote sensing instruments, such as radars and radiometers, allow observations of cloud over large areas on a continuous basis. This data is useful for improving microphysical schemes and evaluating numerical weather prediction and climate models.

Measurements of atmospheric cloud ice may also be assimilated into forecasts. For many years a major limitation to forecasts was that only clear-sky radiances were assimilated into numerical weather prediction models, and cloudy cells were discarded. This means a great deal of useful information was lost, thus in recent years a lot of focus was put into enabling all-sky assimilation of satellite radiances. At the ECMWF, all-sky assimilation of microwave data has been shown to have the largest relative impact on the quality of the 24-hour operational weather forecasts of all observations. Currently microwave data is assimilated using a combination of clear-sky and all-sky techniques, but by October an exclusively all-sky assimilation framework will be used for microwave observations.

To address the above points it is necessary to understand the relationship between the size and shape of an ice particle and its microwave scattering properties. To obtain information on particle properties, such as size, shape, and mass, direct measurements are also required. Thus, a novel dataset has been obtained as part of the PICASSO campaign, involving co-located in-situ aircraft observations with remote sensing measurements from ground-based radar. Unique tracking was used to ensure the same cloud was sampled by the aircraft instruments and the radars. Data from synchronized 3, 35, and 94 GHz radars was collected, allowing studies of how scattering by snowflakes changes with wavelength. Further details can be found in this blog post. Here we use the aircraft probes and multi-frequency radar information from the PICASSO dataset to begin to evaluate different ice particle shape models.

What are the benefits of using multiple radar frequencies?

Ice particles in clouds have a wide range of sizes, from frozen cloud droplets of about 10 μm to large aggregates of crystals that can reach 4-5 cm. At 3 GHz (i.e. 10 cm wavelength), the particle size is always less than the wavelength. This means the particles scatter in the Rayleigh regime. If we were to consider the case of homogeneous spheres in the Rayleigh regime, the amount of scattering would increase as the sixth power of particle size (D6). This is a bit more subtle for ice, where generally scattering is proportional to mass2. Either way, larger particles tend to scatter much more than smaller particles. For higher frequencies such as 94 GHz (shorter wavelengths), small particles scatter in the Rayleigh regime, but larger particles (which are comparable in size to the wavelength) will scatter in the Mie regime. This means that by using a combination of measurements at two different frequencies (i.e. the dual-frequency ratio; DFR), we can get a better indication of the size of the particles, consequently improving estimations of ice water content (IWC). Triple-frequency measurements have shown potential for providing information on particle shape/structure and density (e.g Kneifel et al. (2011; 2015)), along with potential to identify regions of aggregation, melting, and riming (Dias Neto et al., 2019). Stein et al. (2015) used triple-frequency observations to evaluate particle models, and we can perform similar experiments using the PICASSO dataset.

Why do we need to evaluate particle models?

RTTOV-SCATT is a fast multiple-scattering radiative transfer model designed to assimilate all-sky MW radiances in numerical weather prediction (Bauer et al., 2006; Saunders et al., 2020; Geer et al., 2021). Assimilation of observations requires accurate hydrometeor scattering models, and optimisation of particle representation is necessary in order to extend all-sky capabilities to include higher frequencies and observations from new sensors, e.g. the Ice Cloud Imager. The default in version 13 of RTTOV-SCATT is to use a range of realistic, non-spherical particles to represent frozen hydrometeors (i.e. snow, graupel, and cloud ice). These are obtained from the ARTS scattering database (Eriksson et al., 2018), as outlined in table 1 of Geer et al. (2021). Here we examine 4 particle mixtures from the ARTS scattering database – plates, columns, block columns, and ICON snow.

Examples of experiments performed
Results of the simulated IWC and radar reflectivity (Z) are shown in Fig. 1 for one of the aircraft runs on 13th February 2018. The in-situ measured particle-size distributions (PSDs) were used to perform the simulations. The red lines show the measurements obtained from the Nevzorov probe and the CAMRa 3 GHz radar, respectively. We find that none of the 4 particle mixtures simultaneously provide a good fit to IWC and Z. However, the block mixture tends to overestimate measurements of both quantities, and the column mixture underestimates measurements.

Figure 1: – (a) Simulated and measured IWC. The IWC measured using the Nevzorov probe is shown in red, with the IWC simulated using the in-situ measured PSDs and the 4 particle mixtures shown by the other colours, as outlined in the figure legend. (b) Same as panel (a) but for the 3 GHz radar reflectivity.

We also looked at the dual-frequency ratio, and found that columns predict a larger value of DFR(3,35) than the other mixtures (Fig. 2a), while ICON snow predicts a lower value of DFR(35,94) than the other shapes (Fig. 2b). Fig. 2c shows the DFRs calculated for all the runs during this case study, plotted in triple-frequency space with DFR(35,94) on the x-axis and DFR(3,35) on the y-axis. The dots are the values simulated using the in-situ measured PSDs, and the lines are simulated using exponential PSDs. The large variation of the dots from the lines shows that even if the chosen shape model is realistic, commonly-used parameterisations of the PSD (such as assumptions of exponential and gamma distributions) may still introduce a large error to the calculations.

We are currently in the process of comparing the DFR simulations to measurements in order to evaluate the particle models and determine whether any of them are realistic. This work will be useful to guide microphysical schemes and assumptions that are used in weather and climate models, and in data assimilation.

Figure 2:  (a) Simulated DFR calculated at 3 and 35 GHz for one of the aircraft runs. (b) Same as panel (a) but for 35 and 94 GHz. (c) The two DFRs calculated for all the runs during this case study, plotted in triple-frequency space with DFR(35,94) on the x-axis and DFR(3,35) on the y-axis. The dots are the values simulated using the in-situ measured PSDs, and the lines show the results calculated using exponential PSDs.

Kneifel, S., M. S. Kulie, and R. Bennartz, 2011: A triple‐frequency approach to retrieve microphysical snowfall parameters, J. Geophys. Res., 116, D11203,

Kneifel, S., A. von Lerber, J. Tiira, D. Moisseev, P. Kollias, and J. Leinonen, 2015: Observed relations between snowfall microphysics and triple-frequency radar measurements. J. Geophys. Res. Atmos., 120, 6034– 6055,

Dias Neto, J., and Coauthors, 2019: The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation, Earth Syst. Sci. Data, 11, 845–863,

Stein, T. H. M., C. D. Westbrook, and J. C. Nicol, 2015: Fractal geometry of aggregate snowflakes revealed by triple-wavelength radar measurements, Geophys. Res. Lett., 42, 176–183,

Bauer, P., E. Moreau, F. Chevallier, and U. O’Keeffe, 2006: Multiple-scattering microwave radiative transfer for data assimilation applications, Quarterly Journal of the Royal Meteorological Society, Wiley, 132 (617), pp.1259-1281,

Saunders, R., J., and Coauthors, 2020: RTTOV-13 science and validation report, EUMETSAT NWP-SAF.

Geer, A. J., and Coauthors, 2021: Bulk hydrometeor optical properties for microwave and sub-mm radiative transfer in RTTOV-SCATT v13.0, Geosci. Model Dev. Discuss. [preprint],, in review.

Eriksson, P., R. Ekelund, J. Mendrok, M. Brath, O. Lemke, and S. A. Buehler, 2018: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Sys. Sci. Data, 10, 1301–1326,


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Projected longer dry spells under climate change occur during dry seasons not wet seasons

By Caroline Wainwright 

The latest Intergovernmental Panel on Climate Change (IPCC) report states that the global water cycle will intensify with continued global warming. This means fewer rainy days, but with more intense rain over many land regions, and more variability generally1 . More dry days and longer dry spells have the potential to lead to negative impacts on crop yields and food security, as reductions in water availability limit crop growth. The impacts on crops also depend on the timing of these longer dry spells in the annual cycle and future delays in the wet season are also reported in the IPCC report and by my previous research.

Exploiting the latest state of the art computer simulations, we have examined changes in wet and dry spell characteristics under future climate change across the tropics, applying novel techniques to diagnose wet and dry season changes. The results of our new research are summarised in the schematic below.

We find the start of wet season is projected to be delayed by up to 2 weeks by the end of the 21st century across South America, southern Africa, West Africa, and the Sahel. This is important since it can affect the planting of crops.

We also find a reduction in dry season rainfall and an increase in dry spell length during the dry season across Central and South America, southern Africa, and Australia. Mean dry season dry spell lengths are projected to increase by 5–10 days over northeast South America and southwest Africa. This may make the dry seasons more intense, negatively impacting perennial crops, such as cocoa, and crops grown during the dry season.

However, changes in dry spell length during the wet season are much smaller across the tropics. Therefore, agriculture grown solely during the wet season may be less affected by longer dry spells.

Temperature increases are projected to be larger in dry seasons than in wet seasons, with increases in dry season maximum temperatures found to be up to 3°C higher than the increases in wet season maximum temperatures over South America, southern Africa, and parts of Asia. In these regions, mean maximum temperatures greater than 35oC become more expansive with warming.

Overall, while we find that changes in dry spell length during the wet season are generally small, longer dry spells, fewer wet days, and higher temperatures during the dry season may lead to increasing dry season aridity and have detrimental consequences for perennial crops. As the latest IPCC report states, limiting human-induced global warming and associated changes in the water cycle requires rapid and sustained cuts in CO2, such that emissions are balanced by additional uptake by the land and ocean, along with strong reductions in other greenhouse gas emissions.Figure 1: Schematic summarizing the changes in wet/dry season rainfall and wet/dry spell lengths in wet/dry seasons found here; the top panel is for dry seasons and the bottom row is for wet seasons (including regions that are wet year-round). (top) Longer dry spells and lower rainfall during the dry season are found over Central and South America and southern Africa. Shorter dry spells and more rainfall during the dry season are found over East Africa and parts of Asia and the Sahel. (bottom) More rainfall in the wet season is found over East Africa and Asia. Less rainfall in the wet season is found over northern South America. Reductions in the length of wet spells in the wet season are found over South America and West and Central Africa.


Douville, H. et al. (2021) Water Cycle Changes. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, In press [Masson-Delmotte, V., et al.]

Dunning C.M., Black, E. and Allan, R.P. (2018), Later wet seasons with more intense rainfall over Africa under future climate change, J. Climate, 31, 9719-9738, doi: 10.1175/JCLI-D-18-0102.1.

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K.
Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press. 

Wainwright, C. M., Black, E., and Allan, R. P. (2021). Future Changes in Wet and Dry Season Characteristics in CMIP5 and CMIP6 Simulations. Journal of Hydrometeorology 22, 9, 2339-2357, doi: 10.1175/JHM-D-21-0017.1


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From Falling Paper Strips, Tossed Coins To Settling Snowflakes

By Majid Hassan Khan

Did you notice money raining down in part three of the Spanish TV series “Money Heist” (Spanish: La casa de Papel, “The House of Paper”) on Netflix? A blimp flew over Madrid and showered money. These falling paper bills fluttered, tumbled and followed random trajectories while descending to the streets. The behaviour has a similarity with the falling of leaves from a tree, descent of snowflakes and path of tossed coins in fountains and wishing wells.

It is a wonder why Newton never addressed the behaviour of falling leaves from the apple tree in his garden at Woolsthorpe Manor. He did perform experiments by dropping glass spheres and inflated hog bladders from a cathedral in London. A look at the influence of air around falling leaves or freely falling paper strips or the behaviour of water around a flipped coin in a pool was to come later. It took another two hundred years before a 22-year-old James Clerk Maxwell paid heed to the physics around falling paper slips. He observed the flutter and explained the changes in pressure distribution around the strip and even referred to the resistance offered to the paper by the air around it. Similar is the nature of fall when coins are tossed in fountains, pools and rivers. Falling coins rotate, flutter, tumble and descend on different trajectories.

A common term used to emphasize the similarity in physics between air and water is ‘fluid’. The story of fluid dynamics can be woven using ‘inertia’ and ‘friction’ of the flowing medium as the central characters. The idea of friction in fluids was proposed by many scientists who contributed to the evolution of the physics of fluids. Newton being the first, followed by Poiseuille and Hagen. Later Stokes further emphasised the notion of friction in fluids. The physics of fluids was evolving, and newer ideas were included to aid a better understanding. When Reynolds demonstrated the onset of turbulence in flow and Prandtl explained the effect of viscosity close to the surfaces of an object immersed in the flow, newer insights in the understanding of external flows started taking shape. Now it is widely known that the storyline is influenced by the geometry of the object around which the flow happens. Inertia and viscosity swap the roles of being the main protagonist based on the size and shape of the object around which the flow is observed.

With recent advances in fluid dynamics, we can explain that the flow physics of a freely falling paper strip is influenced by its initial state, the size and symmetry of the falling paper strip, the density difference of falling paper and air, and the viscosity of the fluid. The flows separating at the edges of the strip roll in vortices leaving an unsteady trail behind the falling paper. This unsteadiness causes an imbalance in the forces acting on the paper strip. The extent of the force imbalance on the paper strip due to the flowing fluid around it causes it to fall steadily, flutter, rotate, tumble, drift, oscillate or stably fall. All these fall behaviours are direct outcomes of the interaction of the air and the paper strip. Similar interactions happen when a coin is tossed in a wishing well or a pool of water. Flow separates at the edge of the coin, vortices are formed and shed in the wake. The coin falls steadily, flutters, rotates or shows chaotic behaviour. The physics of these simple freely falling objects when extended to ice particles and snowflakes helps understand the fall behaviour of snowflakes and the nature of flow around them which aids in explaining their growth rates.

To appreciate the fall behaviour of a freely falling paper Maxwell used a paper strip with sides two inches long and one inch wide. You can try dropping paper strips of varied sizes and observing their fall. I suggest you cut a paper strip 1 cm wide and about 5 to 10 cm long and drop it from a height with the larger side slightly tilted away from the horizontal (and the flat face of the strip looking at you). You will be amazed by the fall behaviour. After the initial settling the paper will orient itself in one direction of fall and will start spinning around an axis parallel to the longest side (an example of autorotation).  If you get the feel of the fall, you can use different shapes and enjoy the diverse fall behaviour of simple, harmless paper pieces and marvel at the complexity of the fluid dynamics of freely falling paper strip.

The understanding of fluid object interaction is of immense use in environmental science, atmospheric physics, insect flights and industrial needs. In meteorology, clarity about the fluid dynamics of falling objects helps establish the fate of ice particles and snowflakes in the atmosphere and in clouds.

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Data assimilation under dramatic growth of observational data and rapid advances in computer performance

By: Guannan Hu

The importance of data assimilation

Data assimilation (DA) is a technique used to produce initial conditions for numerical weather prediction (NWP). In NWP, computer models describing the evolution of the atmosphere are used to predict future weather based on current or previous weather conditions. These models are usually very sensitive to initial conditions, meaning that slight changes in the initial conditions can lead to completely different weather forecasts. The Data Assimilation for the Resilient City (DARE) project is investigating the use of novel observations such as temperature data from vehicles, smartphone data, river camera images, etc. for weather and flooding forecasting. Accurate forecasting of hazardous weather events can help us prepare in advance to protect lives and property and reduce economic losses.

DA is also used to create climate reanalyses, which are gridded datasets providing long-term historical estimates of climate variables covering the globe or a region. These datasets are used to monitor climate change.

The basic idea of data assimilation

Data assimilation blends observations with model forecasts to produce the best estimates of atmospheric and climate variables. For example, the air temperature on campus can be measured by a thermometer or predicted from past temperatures (and other relevant variables such as humidity and wind) using a computer model. Then we obtain the estimates for air temperature from two sources. We assume that the true temperature is somewhere in between. It can therefore be given by a weighted average of the two estimates, where the one with the smaller error has the greater weight as it is considered more reliable. This is a very simple example; the real data assimilation applications are much more complex and involve a huge amount of data.

The assimilation of novel observations

As computers become more powerful and the volume of observational data increases rapidly, data assimilation becomes increasingly important in improving the skills of weather forecasting. The assimilation of novel observations (e.g., geostationary satellite, radar data) has led to great improvement in forecast skill. Unlike thermometers and other conventional instruments, the weather satellite and Doppler radar measure the atmospheric variables indirectly. These observations need to be transformed for use in data assimilation procedures. This will cause so-called representation errors in addition to measurement errors. The observation error (includes representation and measurement errors) been found to be spatially correlated for some observation types, such as geostationary satellite data and Doppler radar radial wind. In practical applications, these error correlations are usually taken into account indirectly in data assimilation systems or removed by thinning the observations. These approaches might be suboptimal as they prevent us from making full use of the observations. Accurately estimating observation error correlations for satellite and radar data can be very challenging. Satellite observations can have a mixture of inter-channel and spatial error correlations. Doppler radar radial wind has the error correlation lengthscales that may not be isotropic; they vary with the height of the observations and the distance of the observations to the radar. In addition, explicitly including correlated observation error statistics may largely increase the computational cost of DA. The increase is mainly caused by the inversion of dense matrices and the parallel communication costs in the computation of matrix-vector products. Another issue with including correlated error statistics is that it may change the convergence behaviour of the minimization procedure in variational data assimilation, which solves a least-square problem.

The more and more wide application of data assimilation

Starting with its use in the NWP, DA is now attracting more and more interest from the wider scientific community. People with different backgrounds and from different research institutes, universities, and weather services around the world are not only committed to developing new methods but are also keen to apply this technique to new areas. For instance, DA can be combined with machine learning. DA can be applied to space weather forecasting and even used to monitor and predict a pandemic!


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How do we actually run very high resolution climate simulations?

By: Annette Osprey

High resolution modelling

Running very detailed and fine scale (“high resolution”) simulations of the Earth’s atmosphere is vital for understanding changes to the Earth’s climate, particularly extreme events and high-impact weather [1]. However, each simulation is 1) time-consuming to set up – scientists spend a lot of time designing the experiments and perfecting the underlying science, and 2) expensive to run – it may take many months to complete a multi-decade simulation on thousands of CPUs. But the data from each simulation may be used many times for many different purposes.

Under the hood

There is a lot of technical work that is done “under the hood” to make sure the simulations run as seamlessly and efficiently as possible and the results safely moved to a data archive where they can be made available to others. This is the work that we do in NCAS-CMS (the National Centre for Atmospheric Science’s Computational Modelling Services group), alongside our colleagues at CEDA (the Centre for Environmental Data Analysis) and the UK Met Office. My role is to work with the HRCM (High Resolution Climate Modelling) team, helping scientists to set up and manage these very large-scale simulations.

CMS is responsible for making sure the simulation code, the Met Office Unified Model (UM), runs on the national supercomputer, Archer2, for academic researchers around the UK. As well as building, testing and debugging different versions of the code, we need to install the supporting software that is required to actually run the UM (we call this the “software infrastructure”). This includes code libraries, experiment and workflow management tools [2], and software for processing input and output data. This is all specialist code that we need to configure for our particular systems and the needs of our users, and sometimes we need to supplement this with our own code.

Robust workflows

We call the end-to-end process of running a simulation the “workflow”. This involves 1) setting up the experiment (selecting the code version, scientific settings, and input data), 2) running the simulation on the supercomputer, 3) processing the output data, 4) then archiving the data to the national data centre Jasmin, where we can look at the results and share with other scientists. When running very high resolution and/or long-running simulations we need this process to be as seamless as possible. We don’t want to have to keep manually restarting the experiment or troubleshooting technical issues.

Furthermore, the volume of data that is generated from these high resolution simulations is incredibly large. It is too large to store all the data on the supercomputer, and it can sometimes take as long as the simulation to move the data to the archive. The solution therefore, is to process and archive the data as the simulation is running. We build this into the workflow so that it can be done automatically, and we have as many of the tasks running at the same time as possible (this is known as “concurrency”).

The HRCM workflow








Figure 1: An example workflow for a UM simulation with data archiving to Jasmin, showing several tasks running concurrently.

The image shows the workflow we have set up for our latest high resolution simulations. We split the simulation into chunks, running 1 month at a time. Once one month has completed, we set the next month running and begin processing the data we just produced. The workflow design means that the processing can be done at the same time as the next simulation month is running. First we perform any transformations on the data, then we begin copying it to Jasmin. We generate unique hashes (checksums) that we use to verify the data copy is identical to the original, so that we can safely delete it, clearing space for forthcoming data. Then we upload the data to the Jasmin long term tape archive, and we may put some files in a workspace where scientists can review the progress of the simulation.

Helping climate scientists get on with science

The advances that we make for the high resolution simulations are made available to our other users, whatever the size of the run. Ideally the workflow design means that the only user involvement is to start the run going. In reality, of course, sometimes the machine goes down, connections are lost, the model crashes, (or the experiment wasn’t set up correctly!) Thus, we have built a level of resilience into our workflow that means that we can deal with failures effectively. So, scientists can focus on setting up the experiment and analysing the results, without worrying too much about how the simulation runs.


[1] Roberts, M. J., et al. (2018). “The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale” in Bulletin of the American Meteorological Society, 99(11), 2341-2359, doi:

[2] H. Oliver et al. (2019). “Workflow Automation for Cycling Systems,” in Computing in Science & Engineering, 21(4), 7-21, doi:

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The Other Climate Impact Of Aviation

By: Ella Gilbert

In-flight entertainment

Picture yourself in the window seat of an aeroplane, cruising along at 30,000 feet, occasionally admiring the clouds below and watching that cheesy blockbuster you were too shy to go and see in the cinema. If you’re like me, you might also be trying not to think about the impact of this flight on the climate – after all, we are increasingly reminded that travelling by air is one of the most carbon-intensive things we can do. 

But when you hear the phrase ‘climate impacts of aviation’, chances are you think about the emissions of greenhouse gases like carbon dioxide (CO2) from aircraft. Unfortunately, that’s only a third of the story. What you probably don’t think about are the non-CO2 impacts, which have a climate warming effect twice as large. Bad news if you’re already worried about that flight.

Flying from London to Inverness, for example, is equivalent to eating 13 beef steaks if we consider the CO2 emissions alone, while if we consider the non-CO2 effects it’s more like 24. And if you’re flying from London to San Francisco, those numbers rise to a whopper-ing 117 and 224 steaks[1]. Now, how’s that for an in-flight meal?

It’s not just CO2

Many of the non-CO2 impacts of aviation act in opposing directions. Some cool the atmosphere overall, while others warm it. To make matters more complicated, some effects even have different impacts on the climate over different timescales. Because these non-CO2 impacts are so complex and difficult to observe, there is still a great deal of uncertainty around their magnitude.

Advancing the Science for Aviation and Climate (ACACIA) is a multi-institutional European project trying to dispel some of the ambiguity about the various effects of aviation on climate, many of which you can see on the schematic below. At the University of Reading, we’re working on one of the most uncertain impacts: the effect of aviation aerosol-cloud interactions.

Figure 1: – Schematic overview of how aviation impacts the climate. From Lee et al. (2021)

 Aircraft emit lots of gases and particles at the high altitudes where they fly. Their exhaust plumes spew gases like CO2, nitrogen oxide (NOx) and water vapour, as well as soot and sulphur particles into the atmosphere.

Those soot and sulphur particles are also known as aerosols, and they act like tiny seeds on which ice crystals and liquid droplets can grow. In the right conditions, soot aerosols can trigger the formation of ice crystals, which make up cirrus clouds – the wispy, indistinct clouds you see high up in the sky.

A cloudy blanket

Cirrus clouds tend to warm the Earth overall. That’s because they are very thin and so let solar energy travel through them easily, but at the same time absorb lots of outgoing infrared radiation, preventing it from escaping to space and so warming the surface like a blanket (aka the Greenhouse effect). But aerosols change the properties of those cirrus clouds in ways we’re still learning about.

Think about your flight blazing its way through the sky, its engines releasing aerosols into the atmosphere. As long as the conditions are right for cloud formation, the more aerosols there are in the exhaust plume to act as seeds, the more ice crystals that will form in its wake.

Cloud properties like the number, size and mass of ice crystals influence a cloud’s ‘optical thickness’, which describes how easily radiation can travel through it and so the degree to which those clouds warm or cool the atmosphere.

It’s cirrus-ly complicated

Different characteristics of the cloud compete with each other to push the balance in favour of warming or cooling. For instance, clouds containing many small ice crystals will stick around for longer because it takes more time for crystals to get big enough to fall out of the cloud. Very small crystals (a few thousandths of a centimetre across) tend to reflect more solar energy back to space, which has a cooling effect, but most cirrus clouds contain ice crystals that are larger than this, and so have an overall warming effect.

Aircraft can change how much cirrus clouds warm the climate by injecting more aerosols into atmosphere and influencing how many ice crystals form, as well as their size, shape and lifetime.

Aviation-aerosol-cloud interactions are hugely complex and difficult to measure. And because cloud processes push and pull in different directions, we’re still finding out how aircraft aerosol emissions influence the overall characteristics of cirrus clouds. In fact, the question marks are so large that we don’t actually have a precise number to tell us whether their impact is to warm or cool the atmosphere.

Evidence suggests that it’s probably a warming effect, although a recent review study was unable to provide a best estimate of the effect of aerosol-cloud interactions, leaving a conspicuous gap, and an even newer study shows that the warming impact of aviation-aerosol-interactions may be negligible.

One thing at least is clear: it’s still very much a hot topic of research.

Filling in the blanks

Enter, stage left: ACACIA. Our main task at Reading as part of the ACACIA project is to use very fine-scale computer models (called large eddy simulation, or LES) to explore the processes acting on pre-existing cirrus clouds and to find out how they interact with emissions of aviation aerosols like soot.

Understanding these processes will help us quantify the exact effect of aviation aerosols on cirrus clouds: for instance, how do they impact the number of ice crystals that form? How fast do these crystals grow? How quickly do they disappear? How do the prevailing weather conditions impact these effects?

Reducing the non-CO2 impacts of aviation

Hopefully, the work of the ACACIA project will allow us to fill in some of the blanks when it comes to aviation’s effect on climate – the crucial first-step that will allow us to mitigate its effects. Understanding the science is key, and will allow us to develop solutions that reduce the non-CO2 impacts of aviation.

Using aviation fuels that have less soot, avoiding areas where contrails and cirrus clouds preferentially form or avoiding some airspaces entirely might all be helpful solutions – but more work is needed before these strategies can be implemented, especially because there is no clear winner and many proposed options come with trade-offs like increased CO2 emissions.

So – for now at least – your flight won’t be getting diverted away from those spectacular cirrus clouds. I’ll let you get back to watching Fast and Furious 82 now.


Defra/BEIS Greenhouse Gas Conversion Factors 2019

Kärcher, B. (2018). Formation and radiative forcing of contrail cirrus. Nature Communications 9, 1824

Kärcher, B., Mahrt, F. and Marcolli, C. (2021). Process-oriented analysis of aircraft soot-cirrus interaction constrains the climate impact of aviation. Nature Communications Earth & Environment 2, 113. 

Lee, D. S. and Coauthors (2021). The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmospheric Environment, 244, 117834.

Lee, D. S. (2021) Contrails from aeroplanes warm the planet – here’s how new low-soot fuels can help. The Conversation 18 June 2021. Accessed 26/07/2021. Available at:  

Liou, K.-N. (2005). Cirrus clouds and climate. AccessScience. Retrieved July 26, 2021, from

Lynch, D.K. (1996) Cirrus clouds: Their role in climate and global change. Acta Astronautica 38 (11), 859-863.

Niklaß, M., Lührs, B., Grewe, V., Dahlmann, K., Luchkova, T., Linke, F. and Gollnick, V. (2019) Potential to reduce the climate impact of aviation by climate restricted airspaces. Transport Policy 83 102-110.

Poore, J. and Nemecek, T. (2018) Reducing food’s environmental impacts through producers and consumers. Science 360 (6392) 987-992.

Shine, K. and Lee, D. S. (2021) Commentary: Navigational avoidance of contrails to mitigate aviation’s climate impact may seem a good idea – but not yet. Green Air News 22 July 2021. Accessed 23/07/2021. Available at:

Skowron, A., Lee, D.S., De León, R.R., Ling, L. L. and Owen, B. (2021) Greater fuel efficiency is potentially preferable to reducing NOx emissions for aviation’s climate impacts. Nature Communications 12, 564.

Timperley, J. (2017) Explainer: The challenge of tackling aviation’s non-CO2 emissions. Carbon Brief 15 March 2017. Accessed 23/07/2021. Available at:

Timperley, J. (2020) Should we give up flying for the sake of the climate? BBC Future, Smart guide to climate change. Accessed 23/07/2021. Available at:

[1] Assuming an ‘average’ emissions intensity for beef per serving of 7.5 kgCO2e after Poore & Nemecek (2018), average flight distances of 723 km and 8629 km for flights to Inverness and San Francisco, respectively, domestic aviation emissions intensity of 133 g and 121 g per passenger kilometre for CO2 and non-CO2 impacts, respectively, and long-haul aviation emissions intensity of 102 g and 93 g per passenger km for CO2 and non-CO2 effects, respectively, after BEIS/Defra emissions conversion factors 2019. See also:



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Soil Moisture Monitoring with Satellite Radar

By: Keith Morrison-Department of Meteorology & Will Maslanka-Department of Geography & Environmental Science

Everyone knows about the impacts from intense and/or prolonged rainfall – flooding, like that experienced in the Thames Basin during the Summer of 2007, and the Winter of 2013/14. Whilst hard-engineering defences (such as raising the height of riverbanks, or construction of flood defences) can be good at dealing with flooding events by keeping water within the river, they can have negative impacts upon natural processes, such as increased deposition and erosion of sediment, and changes to the wildlife habitat. Some hard-engineering practices, such as straightening river meanders, cause river flows to speed up, potentially leading to greater flood risks downstream. Rather than exacerbating flood risk downstream, soft-engineering practices, such as Natural Flood Management (NFM) can be used to slow the flow of water before it enters the watercourse and store the water upstream.

The NERC-funded LANDWISE project (LAND management in lowland catchments for risk reduction) seeks to assess the impact and effectiveness of realistic and scalable land-based NFM measures, to reduce the risk from surface run-off, and groundwater within the Thames Basin. These land-based measures include the planting of more trees in riparian zones (the area along the riverbank), floodplain restoration, and soil and land management changes. The LANDWISE research is done in a multi-disciplinary fashion, by joining together the collective expertise of hydrologists, geologists, farmers, local flood forums, conservation Non-governmental organisations (NGOs) and policy makers, to maximise the impact of the research, and to ensure that the resulting research is greater than the sum of the individual efforts.

One area of focus is that of soil and land management changes; the impact that differing farming practices (such as crop choice and tillage practices) can have on altering infiltration or storage of rainfall in the soil as soil moisture. Soil moisture retrieval from satellite-based radar observations is well established, with various in-service satellite products. However, the resolution of the products are coarse (>1 km), as they are based on spatially averaged measurements from. Instead, this study utilises the higher resolution available from the Sentinel-1 synthetic aperture radar satellite constellation, to work within farmers’ fields, at scales between 1 km and 100 m.

The radar reflectivity of a soil arises from the dielectric contrast at the air/soil boundary, which is set by the soil type and its moisture state. However, moisture retrieval is complicated by the additional sensitivity of the radar to the surface roughness of the soil. To get around this issue, rather than dealing with absolute soil moisture, the LANDWISE project has been looking at relative surface soil moisture (rSSM) using the TU Wien Change Detection Algorithm [1]. This assumes that both the soil type and surface roughness are static parameters. Thus, short-timescale fluctuations present in the backscatter are indicative only of soil moisture changes. By looking at the relative soil moisture, it is possible to create a moisture time series. In this scheme, observations are scaled between the wettest and driest periods, and assuming that the wettest and driest periods are associated with the largest and smallest backscatter values, respectively.

The LANDWISE project has used data from Sentinel-1 to produce an rSSM time series over the Thames basin between October 2015 to December 2020. Some resolution is sacrificed in order to reduce randomly occurring fluctuations, by spatially averaging the imagery onto a 100m grid. Figure 1a shows a snapshot of the rSSM differences across the Thames Basin on 11th of September 2018. A clear band of higher rSSM values can be seen, with lower values to the north and south of it. This band of higher rSSM values can be attributed to a localised shower (Figure 1b) that passed over prior to the time of the satellite acquisition (approx. 18:00 UTC).

Figure 1a: rSSM values across the Thames Basin for the 11th September 2018. Areas denoted in grey are neglected as they are associated with urban areas.

Figure 1b: 12-hourly rainfall accumulation, before the orbit overpass. Rainfall amounts below 0.25mm in 12 hours have not been plotted for clarity.

Rather than looking at a snapshot, Figure 2 looks at the catchment for the river Kennet, a sub-catchment of the Thames Basin, in terms of the temporal changes in rSSM, in both the spatial (top) and in a 7-orbit smoothing (bottom). The expected annual cycle can be seen in the timeseries; an increase in rSSM during the winter before decreasing over the spring and summer as the weather becomes drier, before increasing again during the autumn and winter. However, the soil moisture appears to increase over the summer, when anecdotally it can be expected to be at its lowest during this time of the year. This can be seen during the summer of 2018, when the rSSM values increase slightly over the course of the summer; a period of time when very little rainfall fell over the Thames region [2]. This apparent increase is not due to an increase in soil moisture, but due to an increase in radar backscatter, as the contribution from vegetation (predominately agricultural crops) increases over the growing season, before dropping away after the harvest. Current work is focussed on deriving a correction for seasonal variations in vegetation cover, based on multiple satellite viewing geometries.

Figure 2: (Top) rSSM images for the Kennet Catchment area. Areas denoted in grey are either outside the Kennet Catchment, or have been neglected as urban areas. (bottom) Spatially average rSSM values for the individual orbit (black line) and for a 7-orbit moving average (red line).


[1] Bauer-Marshallinger, B., Freeman, V., Cao, S., Paulic, C., Schaufler, S., Stachl, T., Modanesi, S., Massari, C., Brocca, L., and W. Wager, 2019: Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles, IEEE Trans. Geosci. Remote Sens., 57, 520-539,

[2] Turner, S., Barker, L., Hannaford, J., Muchan, K., Parry, S., and C. Sefton, 2021: The 2018/2019 drought in the UK: a hydrological appraisal., Weather, 99, 1-6,



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