The Sun’s Magnetic Field: From Minutes To Millennia

By: Mathew Owens

The Sun’s magnetic field varies on all observed time scales. Knowing how the solar magnetic field has changed in the past helps us plan for hazardous conditions in the space environment in the future. It is also important for modelling the Earth’s climate system. In order to reconstruct past solar variability, we must call on a diverse range of data sources.

Over minutes to days, changes in the solar field can be directly observed by space-based telescopes and in situ monitoring spacecraft. Variability on these short time scales is referred to as “space weather” as it can adversely affect space- and ground-based technologies, as well as posing a health hazard to astronauts and passengers/crew on high-altitude flights [1]. To help mitigate the worst effects, the UK Met Office routinely issues a space-weather forecast for the next few days.

A near-continuous record of spacecraft observations back to the mid 1960s shows that the solar magnetic field also has strong periodic behaviour in the form of the approximately 11-year solar cycle. As shown in Figure 1, total solar irradiance (TSI) — the Sun’s energy input to the Earth’s climate system — varies closely with the Sun’s magnetic field and hence the solar cycle. The amplitude of the solar-cycle variation in TSI, however, is only around 0.1%, meaning it has a very small contribution to terrestrial global temperature variations, even on decadal time scales.

Figure 1: Total solar irradiance variations from the PMOD composite (Dudok de Wit, Kopp, Fröhlich, & Schöll, 2017). White and red lines show 27-day and 1-year average values, respectively. The solar cycle is indicated by the black-shaded area, which shows sunspot number, arbitrarily scaled. Figure taken from [2]

Space weather is also strongly linked with the solar cycle, at least in a probabilistic sense. This is best seen in geomagnetic records, which measure the disturbance of the Earth’s own magnetic field down at ground level, which in turn can be directly related to the strength of the solar magnetic field driving the disturbance. Geomagnetic records from sufficiently sensitive compass observations extend back around 160 years. Figure 2 shows that severe space-weather events roughly follow the 11-year solar cycle.

Figure 2:. Annual occurrence of space-weather storms of different magnitudes, with red, blue, and white indicating moderate (top 5% of all days in the study), severe (top 1%), and extreme (top 0.1%), respectively. The black shaded background shows sunspot number, arbitrarily scaled. Note the logarithmic scale and that zero occurrence has been set to 0.1 for plotting purposes. Figure taken from [2].

The solar cycle was first discovered  in the number of visible sunspots on the solar surface. This property can be reconstructed back over 400 years using records extending all the way back to Galileo’s first telescopic experiments in 1610.  Large variations in the magnitude of solar cycles suggest that “space climate” can also vary significantly. Reconstructing the solar magnetic field (and hence TSI) on time scales longer than a few centuries requires the use of even more indirect proxies.

One such proxy is galactic cosmic rays (GCRs), near relativistic charged particles which originate outside of the solar system, at astrophysical objects such as supernovae. As charged particles are deflected by magnetic fields, the Sun’s magnetic field partially shields Earth from GCRs. When GCRs do enter the Earth’s atmosphere, they collide with air molecules and create a shower of exotic decay products. These include isotopes which do not naturally occur, such as Carbon-14 and Beryllium-10. Such “cosmogenic” isotopes are removed from the atmosphere and deposited in biomass and ice sheets, respectively, providing natural records of GCR intensity, and hence the Sun’s magnetic field, over the last 9,400 years or so.

Figure 3: A summary of the long-term variations in the Sun’s magnetic field (HMF). Top: Millennial-scale reconstructions of total HMF from cosmogenic radionuclide data (Wu et al., 2018), scaled to match values in the bottom panel. Bottom: Sunspot (red), geomagnetic (blue), and spacecraft (pink) estimates of total HMF. Figure taken from [2].

Figure 3 shows a comparison of the different methods of reconstructing the Sun’s magnetic field. While the data sources differ wildly (state-of-the-art spacecraft, Victorian-era compasses, 17th-century telescope observations, tree trunks and ice sheets), the agreement is remarkably good. The Maunder minimum, from 1650-1715, is the period of weakest solar magnetic field in the 9,400 year reconstruction, while the 1960s were the strongest. Thus the sunspot record spans the full range of recent solar magnetic variability, making it invaluable for understanding space weather and solar forcing of terrestrial climate.

(There’s also a movie of Figure 3 available here:


[1] Cannon et al., 2013, “Extreme space weather: impacts on engineered systems and infrastructure”, Royal Academy of Engineering Report

[2] van Driel-Gesztelyi and Owens, 2020, Solar Cycle, Oxford Research Encyclopedia of Physics, doi:10.1093/acrefore/9780190871994.013.9


Posted in Climate, Climate modelling, Solar radiation, Space, space weather | Leave a comment

The Climate Feedback: More Than The Sum Of Its Parts

By: Jonah Bloch-Johnson

One of the main numbers that climate scientists use to predict global warming is the “climate feedback,” which measures how effective warming is at countering the effects of CO2. CO2 reduces how much energy the planet sheds to space. That causes that energy to build up, which warms the planet. Warming causes more energy to be shed to space, countering CO2’s effect. The climate feedback measures how much more energy gets shed to space for each degree of warming. The bigger the feedback is, the less the planet needs to warm.

To talk about the climate feedback, I’m going to use a metaphor. Imagine that we have a newspaper, with a newsroom full of reporters typing away at their laptops, trying to finish enough stories to fill that day’s issue. If the reporters are hardworking, then they won’t need that much time to finish all their stories, just as a big climate feedback leads to not needing that much warming to counter the effect of a CO2 increase. On the other hand, if the reporters are lazy, chitchatting with each other, staring off into space, etc., it will take a lot longer for them to fill the paper. They’re not very efficient, which is like having a very weak climate feedback, which leads to a lot of warming.

Of course, reporters are all quite different to each other – some are more efficient, others much less so – and the same is true of different regions of the Earth: some, like the parts of the Tropics where air ascends, have very strong climate feedbacks, while other areas have a very weak feedback. It’s as if we had two reporters, Trisha and Evan; Trisha is an ace reporter able to write perfect copy at a moment’s notice, while Evan takes forever to finish even the most basic assignments.

Recent climate studies (e.g., Andrews et al. 2015, Zhou et al. 2017, Dong et al. 2019, Bloch-Johnson et al. 2020) have noticed that the climate feedback seems to get weaker over time. There’s a logic to this – early on, your newsroom has both Trisha and Evan working, but Trisha finishes her assignment quickly and goes home early (that is, the ascending tropics finish much of their warming relatively quickly) while Evan takes longer. By the end of the night, your newsroom’s efficiency reflects Evan’s efforts rather than your two reporters combined, so that the newspaper ends up taking longer to finish than you would initially expect. In the same way, the amount of warming we end up with can be larger than we would initially have expected since at first more warming occurs in the ascending Tropics.

In order to understand this changing feedback, a new field of study has emerged in which the effects of warming in each region of the Earth are estimated in isolation, and the combined effect is assumed to be the sum of the contributions from each region. This is a bit like guessing the consequences of hiring both Trisha and Evan for your newsroom by considering how they work in isolation. But of course, if we then put them in a room together, it seems quite likely that they will influence each other – they might encourage each other, or distract each other, or drive each other up the wall, and generally do things we would never have guessed from seeing them on their own. In other words, the newsroom’s efficiency will be different than just taking the sum of its parts.

Figure 1: In this figure, we have taken the HadCM3 climate model and abruptly doubled its CO2 concentration. As it warms in response, its climate feedback gets weaker with time (black line). We can try to recreate this behavior by reducing the Earth to just two different places: the part of the tropics where air ascends, and everywhere else. If we measure the feedback associated with each region in isolation, we would estimate the feedback to evolve as in the blue line. But if we instead look at how the feedbacks in these two regions interact at different temperatures, we get the red line, which more closely estimates the true feedback reduction.

In the last few months, I’ve been doing some research that suggests that this same principle might hold for regional climate feedbacks. I have taken a computer model of the climate called “HadCM3” and subjected it to a sudden doubling of its CO2 concentration (figure 1). As the climate responds, its feedback gets less strong (black line in figure 1), leading to more warming than we would initially expect: ~4.2ºC of globally averaged warming in total. If we try to model this response by estimating the feedback strength associated with both the ascending Tropical region and the rest of the world, each considered in isolation from the other, we would greatly underestimate the reduction in feedback strength over time (blue line), and as a result, underestimate the long-term warming, projecting it to be ~2ºC. If we instead study how the feedback associated with each region can depend on the state of the other region, by allowing there to be “cross terms” between the warming in the two regions – that is, if we first measure Trisha’s behaviour when Evan acts in different ways, and vice versa, before guessing how they will collaborate in the newsroom – then we get a much better estimate of the feedback, its change over time, and the resulting warming (red line).

In conclusion, climate feedbacks, like teams of people, can interact, behaving in more complicated ways than the sum of their isolated parts. Understanding these interactions is key to forecasting global warming.


Andrews et al., 2015; Zhou et al. 2017;

Dong et al. 2019; Bloch-Johnson et al., 2020

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Forecast West Africa by Dr. Bunny Rabbit

By: Paul-Arthur Monerie


*“Forecast” stands here for the projection of changes in precipitation at the end of the 21st century (a 40-year period average, between 2060 and 2099) relative to the end of the 20th century (1960-1999), in summer (JAS) and for an ensemble of CMIP6 simulations.

(1*) Monerie, P.-A., Wainwright, C. M., Sidibe, M. & Akinsanola, A. A. Model uncertainties in climate change impacts on Sahel precipitation in ensembles of CMIP5 and CMIP6 simulations. Clim. Dyn. 55, 1385–1401 (2020).

(2*) Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016). 2016

(3*) Monerie, P.-A., Sanchez-Gomez, E., Gaetani, M., Mohino, E. & Dong, B. Future evolution of the Sahel precipitation zonal contrast in CESM1. Clim. Dyn. (2020).

(4*) Monerie, P.-A., Pohl, B. & Gaetani, M. The fast response of Sahel precipitation to climate change allows effective mitigation action. npj Clim. Atmos. Sci. 4, 24 (2021).

Posted in Africa, Atmospheric circulation, Climate, Climate change, Monsoons | Leave a comment

Metrology, Earth Observation and Climate Data

By: Jonathan Mittaz 

Metrology is the science of measurement which both defines the System International (SI, The International System of Units, 2019) as well as mathematical frameworks for measurement uncertainties (for example see the GUM: Guide to the expression of Uncertainty in Measurement, 2008). In an ideal world all measurements would be linked back to the SI and hence to a fixed, unchanging reference. Such traceability would, of course, also be the gold standard for satellite climate data records but we are yet to attain this.

Over the past 10-15 years there has been a move to incorporate metrological principles into Earth Observation data. At first there were the QA4EO principles (Quality Assurance Framework for Earth Observation: Principles 2010) leading to an initial principle that:

Data and derived products shall have associated with them a fully traceable indicator of their quality

Fully traceable quality was not fully defined but it basically means that all data needs information to help users determine how good the data is in a way that traces back to a reference, ideally to the SI. Since then, a number of initiatives have started to fill in the details of how to provide quality information for climate data. For example, the ESA Climate Change Initiative (ESA CCI) now has a requirement on providing uncertainties (something that was generally lacking in previous data), and projects such as the Copernicus Climate Change Service (C3S) Data Store provides quality information for some datasets including independent quality assessments. From a metrological perspective, however, what is needed is proper traceability where all uncertainties are traced from their physical origins through to the final signal in a bottom-up manner. In the case of earth observation (EO) data this means tracing errors from both the instrumentation (which measures the incoming signals) as well as fully understanding the errors associated with any retrieval process (where instrument measurements (such as radiances) are converted into the particular geophysical parameter of interest (such as a temperature)).

Work is now in progress to provide metrological traceability to earth observation data. For example, the Horizon 2020 FIDUCEO project has defined metrological methods for satellite data (see the FIDUCEO web site and Mittaz, Merchant & Woolliams 2019) and the Horizon 2020 GAIA-CLIM project (see the GAIA-CLIM website) looked at applying metrological principles to non-satellite measurements such as in-situ reference data. What these projects have shown is that uncertainties are, in fact, complex with both space and time variable uncertainties which also have a range of error correlations (which arise when parts of the errors used to determine the uncertainties are correlated over time or space and which must to be taken into account when propagating uncertainties see Mittaz, Merchant & Woolliams 2019). Some examples of the complexity of error in a sensor are shown in Figure 1 and Figure 2. Figure 1 provides an example of a FIDUCEO uncertainty tree for the SLSTR instrument (Sea and Land Surface Temperature Radiometer, Smith et al. 2021) which demonstrates that the uncertainties are invariably complex because the fundamental sources of error (the outer parts of the uncertainty tree in black text) are many. Figure 2 shows different uncertainties (independent, structured and common) from the Advanced Very High Resolution Radiometer (AVHRR) sensor and shows that different components of uncertainty can also have very different temporal characteristics which are also very different between different versions of the same instrument.

Figure 1: An uncertainty tree diagram for the SLSTR sensor (taken from Smith et al. 2021

Figure 2: Plots showing three different types of uncertainty (independent (red), structured (blue) and common (green)) for an infrared channel (10.8µm) of selected AVHRRs as a function of time.

The metrological community through the National Metrological Institutes (NMIs, of which the National Physical Laboratory is the UKs) have also been leading initiatives. These include the MetEOC projects (Metrology for Earth Observation and Climate), which aims to contribute to the establishment of a metrology infrastructure tailored to climate needs in readiness for its use in climate observing systems. At a European level there is also the European Metrology Network (EMN) for Climate and Ocean Observation, which is a network of European NMIs and affiliated partners to support the application of metrology to climate and ocean observations. Both these projects aim to continue applying metrological techniques to new instrumentation, new models, improved in-situ reference data and an improved understanding of satellite uncertainties.

Finally, there is the question of linking EO and climate data to the SI. Some in-situ references are already in the process of being traced to the SI (such the GCOS Reference Upper-Air Network (GRUAN) and the ESA Fiducial Reference Measurements for validation of Surface Temperatures data (FRM4STS)). But for satellite data there is currently no way do this as there are no in-orbit SI traceable references. But this is about to change. The upcoming TRUTHS mission (to be launched around 2028) and the similarly specified CLARREO Pathfinder mission (due for deployment in 2024) both aim to provide in-orbit SI traceable references in the reflectance (visible) domain. Therefore, within the next 10 years we should have the capability of providing satellite climate data which are traced to the SI, have metrologically based uncertainties and provide, for the first time, the best climate data records possible.


BIPM, 2008: The Guide to the Expression of Measurement, First Edition 2008,

BIPM, 2019: The International System of Units, 9th Edition,

C3S Data Store, 2021: Copernicus Climate Change Service Data Store, Accessed Nov. 2021,

CCI web site, 2021: Climate Change Initiative, Accessed Nov. 2021,

CLARREO web site, 2021: Climate Absolute Radiance and Refractivity Observatory, Accessed Nov. 2021,

Euramet for Climate and Oceans web site, 2021: Euramet for Climate and Oceans, Accessed Nov. 2021,

FIDUCEO web site, 2019: FIDUCEO Fidelity and uncertainty in climate data records from Earth Observations, Accessed Nov. 2021,

GAIA-CLIM web site, 2018: GAIA-CLIM Gap Analysis for Integrated Atmospheric ECV CLImate Monitoring, Accessed No. 2021,

MetEOC web site, 2021: Metrology for Earth Observation and Climate, Accessed Nov. 2021,

Mittaz, J., Merchant, C., and Woolliams, E., 2019: Applying principles of metrology to historical Earth observations from satellites, Metrologia, 56, 032002,

QA4EO Task Team, 2010: Quality Assurance for Earth Observations, Accessed Nov. 2021,

Smith, D., Hunt, S., Etzaluza, M., Peters, D., Nightingale, T., Mittaz, J., Wooliams, E., Polehampton, E., 2021: Traceability of the Sentinel-3 SLSTR Level-1 Infrared Radiometric Processing, Remote Sensing, 13, 374,

TRUTHS web site, 2021: Traceable Radiometry Underpinning Terrestrial- and Helio- Studies, Accessed Nov. 2021,


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Green shoots from the grassroots at the 26th Conference of the Parties

By: Chris Merchant

On the opening two days of COP26, I was in Glasgow to raise awareness of the climate and environmental data freely available from satellite observations of Earth. While the news media focus on big political headlines from COP26, I was greatly impressed by many people I met who usually won’t make the “front page”.

I’m part of the National Centre for Earth Observation (NCEO) and lead a project within the European Space Agency’s climate programme. I took my place at a stand next to the Planetarium in the Glasgow Science Centre (GSC). The stand (figure 1) was created by Space for Climate which is a network of commercial, academic and public sector partners working together to maximise the use of Earth observations to benefit climate action.

Figure 1: The Space for Climate stand at COP26. Note the “heat map” from space over Glasgow and the model of the upcoming satellite mission BIOMASS.

One of the attractions was the touch sensitive globe where the curious could explore animations of climate data and case studies of their use. We were on hand to answer questions about what they could visualise (figure 2).

Figure 2: Discussing data animations on the touch-sensitive illuminated globe of the Earth

Many passers-by were interested to hear about Earth-from-space information and thought the data relevant to their interests. Meanwhile, I had a fascinating time discovering what their interests were. I learned a lot as they enthused about their actions related to climate change. Here are some of the stories I heard (anonymised) from some remarkable people.

An employee of a large food-packaging business explained the difficulty presented to an environmentally conscious business by complex supply chains. They want their paper and card to be sourced from sustainably managed timber. Their major customers in turn increasingly demand this. The necessary chain of information is not necessarily easy to prove, and we discussed space observations as a way of validating statements about where timber was harvested from.

The director of a planetarium (not the GSC’s) talked to me about getting up-to-date data and visualisations of the climate status of planet Earth to include in their programme, “Turning the telescopes to point downward” as he put it. Data from NCEO members can definitely help him with public communication on how the Earth is changing.

I had more than one conversation about pensions, to my surprise. And not from anyone trying to sell me one! Pension schemes are huge investors, and decarbonisation of society will be influenced by their investments. The parents of one impressive teenager related to me how their son ended up in a pension board meeting directly lobbying for a more climate-friendly investment strategy. Another delegate explained that “greening my pension” is an effective strategy for reducing my carbon footprint. And it could also be lucrative, since many sustainable investments have given better returns than general funds in recent years. (Disclaimer: this does not constitute financial advice.)

The south American lady who explained to me her business hugely reducing waste from women’s sanitary products was very impressive. She faces resistance to change, as seems to be a common experience of pioneers. But she palpably gained encouragement from meeting people at COP who appreciated her innovations to reduce everyday resource consumption.

I mention only in passing the economist helping businesses assess finance strategies in the light of climate change, the non-executive director of a start-up aiming to build “the best batteries in the world” in a carbon-neutral factory in England, and the aspiring restorer of Cumbrian peatlands (which can draw down and store carbon dioxide from the air).

I hope that gives some idea of how varied and inspiring the delegates I met were, actively working at the grassroots of climate action. The political outcomes of COP26 may hasten or hinder their progress, but it is these delegates whom I met, and countless others like them, who will drive the practical transition to the low-carbon future we need.


Posted in Climate, Climate change, earth observation, net zero carbon, Remote sensing | Leave a comment

Advance Of The Indian Monsoon Onset

By: Arathy Menon

The Indian monsoon provides water for agriculture, industry and the basic water needs of more than a billion people. The monsoon onset usually takes place in south India during the beginning of June and the monsoon rains then advances in a north-westward direction, over a period of six weeks, covering the entire country by around mid-July. It is interesting to note that the monsoon rains progress in a direction perpendicular to the direction of the mean winds which are southwesterly during the monsoon season from June to September.

Figure 1: An animation showing the progression of the monsoon onset isochrones (Courtesy: India Meteorological Department).  

Based on an observational dataset, Parker et al., (2016) showed that during the beginning of the monsoon season, northwesterlies prevail in the mid-troposphere, and they carry dry air from the Afghanistan region right across the Indian subcontinent as far as the southeast coast of India. During the pre-monsoon period, this layer of dry air in the mid-troposphere suppresses deep convection and rainfall. As the monsoon season begins, these northwesterlies are moistened by the moist monsoon winds which support the development of shallow cumulus and congestus clouds, slowly eroding the dry-air intrusion from the southeast. As the season develops, the northern limit of the onset progresses towards the northwest as the rate of moistening of the dry layer from southeast increases, relative to the horizontal advection of the dry air from the northwest. Volonté et al., (2020) elaborated on this finding by showing that the north-westward progression of the monsoon is a non-steady process modulated by the balance of the interaction between the moist monsoon air mass and the dry northwesterly air mass.

Figure 2: A schematic from Parker et al., (2016) showing the situation around the time of onset (around the 1st of June, top panel) along northwest-southeast India, when the dry layer is still quite deep in the southeast but has been sufficiently moistened to allow the onset of deep convection there. The bottom panel shows the situation around the 15th of July when the onset has advanced to the northwest and the dry layer extends only a few hundred kilometres into the subcontinent.

In one of our recent pieces of research, we used the Met Office Unified model at 4 km grid-spacing and found that the land-atmosphere interactions also play a major role in the advance of the monsoon by modifying the local onset. During the beginning of local onset over a region, onset or pre-monsoon showers lead to an increase in soil moisture heterogeneity. This introduces a gradient in sensible heat flux over that region. Increased gradients in sensible heat generate local mesoscale circulations which favour an earlier triggering of rains. However, as the onset advances and the soil becomes wetter, surface fluxes become less sensitive to soil moisture and then the mid-tropospheric moistening plays the major role in the further progression of the monsoon.

Figure 3: The time evolution of mean sensible heat flux (red lines) and spatial standard deviation in sensible heat flux (blue lines) at 6:30 UTC over a region in central India. Solid lines represent the values from a model simulation using CCI land ancillaries and dashed lines show values from a model simulation using IGBP land ancillaries. As the onset advances, in June and July, the mean sensible heat flux (H) falls due to the increase in soil moisture from the rains. During June, i.e., during the beginning of the onset, the spatial variability in H is at a maximum due to patchiness in rains and these gradients can result in local mesoscale circulations and rainfall during the beginning of the local onset.

A proper understanding of the basic physical mechanisms of the onset advance is a prerequisite for improving the biases in weather and climate models, eventually improving forecasts. An agrarian society like India, whose economy is mainly based on rain-fed agriculture depends a lot on accurate monsoon predictions.


Menon, A., A. G. Turner, A. Volonté, C. M. Taylor, S. Webster, and G. Martin, 2021: The role of mid‐tropospheric moistening and land surface wetting in the progression of the 2016 Indian monsoon. Quart. J. Roy. Meteor. Soc.,

Parker, D. J., P. Willetts, C. Birch, A. G. Turner, J. H. Marsham, C. M. Taylor, S. Kolusu, and G. M. Martin, 2016: The interaction of moist convection and mid‐level dry air in the advance of the onset of the Indian monsoon. Quart. J. Roy. Meteor. Soc.142(699), 2256-2272.

Volonté, A., A. G. Turner, and A. Menon, 2020: Airmass analysis of the processes driving the progression of the Indian summer monsoon. Quart. J. Roy. Meteor. Soc.146(731), 2949-2980.







Posted in Climate, Monsoons, Numerical modelling, Rainfall, Tropical convection | Leave a comment

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).

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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).

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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,


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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).

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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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