Is the Montreal Protocol really working?

By Michaela Hegglin

The Montreal Protocol, which celebrated its 30th birthday last year, is an international treaty established in 1987 to protect the ozone layer from human-made ozone depleting substances. The Montreal Protocol has been hailed as the most effective international environmental agreement to date, and addressed one of the most pressing environmental issues of the 20th century. But … is the Montreal Protocol really working?

Montreal Protocol history in a nutshell
It was the English scientist James Lovelock who was the first to measure the abundance of chlorofluorocarbons (CFCs) with a homemade gas-chromatograph and to realise that these human-made substances were found ubiquitously in both the northern and southern hemispheres (Lovelock et al., 1973). The finding triggered Mario Molina and Sherwood Rowland’s hypothesis in the early 1970s (Molina and Rowland, 1974) that CFCs could only be destroyed in the stratosphere where they release chlorine atoms, which then would be able to destroy ozone catalytically and pose a threat to the ozone layer. The discovery of the Antarctic ozone hole in 1985 by Joe Farman and colleagues at the British Antarctic Survey (Farman et al., 1985) proved their hypothesis to be not only correct, but also far more threatening than had been imagined even by them. It spurred research activities to understand why such severe ozone depletion was found over Antarctica alone, and led to political action under the Montreal Protocol in 1987. The realization that more severe ozone depletion would spread further across the globe if we were to continue releasing CFCs into the atmosphere, along with technological advancements that made replacement of CFCs possible, helped governments to tighten the regulations on CFCs through several Amendments to the Montreal Protocol. 

Ozone layer research today
Almost 50 years after Molina and Rowland’s hypothesis, research on the stratospheric ozone layer is still ongoing, but now focuses on the question of whether the Montreal Protocol and associated Amendments is working and whether the ozone layer is beginning to recover. In particular, researchers now know about the confounding effects that climate change and tropospheric pollution can have on attempts to detect ozone recovery. In the WMO Scientific Ozone Assessment Report 2014, the key statements in the Summary for Policy Makers on this topic point out that indications of ozone recovery since 2000 are found in global total column observations (although not yet attributable to the decline in ozone depleting substances, ODS), and that ozone increases have been found in the upper stratosphere, half of which were attributable to ODS decline (with the other half attributed to climate-change and its effects on stratospheric temperatures) (WMO, 2014). Since the last assessment, studies by Shepherd et al. (2014) on the total column ozone evolution at mid-latitudes and Solomon et al. (2016) over Antarctica attributed ozone recovery to declining ODS concentrations with the help of complex model simulations that help distinguish ODS-related changes from those induced by climate parameters and other natural factors such as volcanic aerosol.   

A disconcerting finding …
More recently, however, a study published by Ball et al. (2018) found on the basis of observations alone that ozone in the lower stratosphere is in fact not recovering but in continuous decline (see Figure 1).  The study applied a more refined statistical method than usually used in the research field of stratospheric ozone, with which the authors were better able to take into account natural variations in ozone. The paper received much publicity, since its findings imply that the Montreal Protocol is not working as expected. On the other hand, some colleagues in the field were quick to denounce the paper and its conclusions. Is it time to worry?

Figure 1: Ozone changes as derived from different stratospheric ozone data records (taken from Ball et al., 2018).

While these findings are indeed disconcerting, the changes in the lower stratosphere do not seem to have had a discernible effect on total column ozone (at least not yet) (Weber et al., 2018). The changes are also (at least partially) compensated by increases in tropospheric ozone (Ball et al., 2018; Shepherd et al., 2014). Were the decline in lower stratospheric ozone to continue, however, the consequences could become more serious. In fact, scientists were expecting ozone decline in the lower stratosphere as a consequence of climate change due to a strengthening of the stratospheric circulation (Hegglin and Shepherd, 2009). These changes would lead to a substantial increase in harmful radiation reaching Earth’s surface in the tropics, where UV levels are low to begin with and where most people live.

The way ahead
What is ultimately needed to answer the question of whether the Montreal Protocol is working is to attribute the causes of the observed changes. Are they indeed the result of non-compliance with the Montreal Protocol’s regulations, or just the result of natural variability? Or are they instead due to climate change? If the latter were the case, the Montreal Protocol would be working but concern for the protection of the ozone layer would have to shift towards regulating climate change. More evaluations of the currently available ozone data record are needed, to confirm (or refute) the results of Ball et al. These evaluations should in particular take into account an aging fleet of ozone instruments flying in space, since they may well show signs of degradation potentially affecting the measurements. The finding also highlights that a renewed commitment to measure vertically resolved ozone in the stratosphere and ozone depleting substances in the troposphere is required to be able to satisfy future needs of monitoring the ozone layer. Both these are essential to see whether trends are to continue and to help attribute the changes to either increasing greenhouse gases or ozone depleting substances.


Ball, W. T., Alsing, J., Mortlock, D. J., Staehelin, J., Haigh, J. D., Peter, T., Tummon, F., Stübi, R., Stenke, A., Anderson, J., Bourassa, A., Davis, S. M., Degenstein, D., Frith, S., Froidevaux, L., Roth, C., Sofieva, V., Wang, R., Wild, J., Yu, P., Ziemke, J. R., and Rozanov, E. V., 2018. Evidence for a continuous decline in lower stratospheric ozone offsetting ozone layer recovery. Atmos. Chem. Phys., 18, 1379-1394,

Farman, Joseph C., Brian G. Gardiner, and Jonathan D. Shanklin., 1985. Large losses of total ozone in Antarctica reveal seasonal ClOx/NOx interaction. Nature 315, no. 6016: 207.

Hegglin, M. I., and T. G. Shepherd, 2009. Large climate-induced changes in UV index and stratosphere-to-troposphere ozone flux. Nature Geoscience 2, 687-691.

Lovelock, J. E.; Maggs, R. J.; Wade, R. J., 1973. Halogenated Hydrocarbons in and over the Atlantic. Nature 241, no. 5386: 194. doi:10.1038/241194a0.

Molina, Mario J., and F. Sherwood Rowland, 1974. Stratospheric sink for chlorofluoromethanes: chlorine atom-catalysed destruction of ozone. Nature 249.5460: 810.

Shepherd, T. G., D. Plummer, J. Scinocca, M. I. Hegglin, C. Reader, V. Fioletov, E. Remsberg, T. von Clarmann, H. J. Wang, 2014. Reconciliation of halogen-induced ozone loss with the total-column ozone record. Nature Geoscience, 7 (6), 443–449, doi:10.1038/NGEO2155

Solomon, Susan, Diane J. Ivy, Doug Kinnison, Michael J. Mills, Ryan R. Neely, and Anja Schmidt, 2016. Emergence of healing in the Antarctic ozone layer. Science: aae0061.

Weber, M., Coldewey-Egbers, M., Fioletov, V. E., Frith, S. M., Wild, J. D., Burrows, J. P., Long, C. S., and Loyola, D., 2018. Total ozone trends from 1979 to 2016 derived from five merged observational datasets – the emergence into ozone recovery. Atmos. Chem. Phys., 18, 2097-2117,

Posted in Atmospheric chemistry, Climate, Environmental hazards, Stratosphere | Tagged | Leave a comment

Improving estimates of soil moisture over Ghana

By Ewan Pinnington

This work aims to improve estimates of soil moisture over Ghana as part of the ERADACS project. In regions where the population relies on subsistence farming it is soil moisture, rather than precipitation per se, that is the critical factor in growing crops. The production of improved soil moisture forecasts should therefore enhance the drought resilience of these regions through improved capacity for early warning of agricultural drought. The seasonal cycles of precipitation and soil moisture over Ghana are shown in a video here, in this video we can see how the response of soil moisture to increased rainfall is lagged as it takes time for the rain to infiltrate into the soil.

Mathematical models of the land-surface are useful tools to inform soil moisture forecasts, but model errors are problematic. In order to improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model over Ghana we have combined satellite observations of precipitation and soil moisture with model predictions using the technique of data assimilation.

We have built a four-dimensional variational data assimilation system that ingests soil moisture observations from the European Space Agency (ESA) Climate Change Initiative to update the soil model parameters of JULES. In our experiments we drive the JULES model with precipitation observations from TAMSAT. Figure 1 shows a data assimilation experiment for one grid box. In this experiment we assimilated one year of soil moisture observations (2009) and then ran a five year hindcast (2010-2014) to judge the model performance against independent data. We can see that the JULES model being run with updated parameters after data assimilation (dark grey line) fits the ESA observations of soil moisture better than our prior model run. We can also see the reduction in bias for the hindcast period over the whole of Ghana in Figure 2, we see that before data assimilation the model is too wet over much of the country and that this bias is reduced after data assimilation.

Figure 1. Soil moisture data assimilation results for a north Ghana grid. Light grey line: prior JULES trajectory. Dark grey line: posterior JULES trajectory. Black dots: ESA CCI soil moisture observations. Faint grey vertical lines: error bars for observations. Vertical dashed line represents the end of the assimilation window.

Figure 2. JULES modelled soil moisture bias over Ghana for period 2010-2014. Left: before data assimilation. Right: after data assimilation.

Overall we find a 44% reduction in root-mean-squared error for our 5-year hindcast after assimilating a single year of soil moisture observations to update model parameters. The initial results of using this system are encouraging, but more work is needed to judge our results against “ground-truth” observations of soil moisture. From this work we also conclude that rainfall data has the greatest impact on model estimates during the seasonal wetting-up of soil, with the assimilation of remotely sensed soil moisture having greatest impact during drying down. For more information on this work please see our Hydrological and Earth System Sciences Discussions paper (Pinnington et al., 2018).

Pinnington, E., Quaife, T., and Black, E., 2017 (in review). Using satellite observations of precipitation and soil moisture to constrain the water budget of a land surface model. Hydrol. Earth Syst. Sci. Discuss.,

Posted in Africa, Climate, Climate modelling, data assimilation, Hydrology, land use, Numerical modelling | Tagged | Leave a comment

A simple way to find out where the moisture for regional rainfall comes from

by Liang Guo

Moisture tracing is an interesting scientific topic that has fascinated meteorologists and hydrologists for decades. Methods for tracing moisture are numerous, from observations to numerical modelling, from water isotopes to remote sensing, from online tracking to off-line tracking, and both Eulerian and Lagrangian methods are used.

A simple method involves a two-dimensional box model. To build a simple model, assumptions are needed. There are three assumptions:

  1. Vapour in the box remains constant at monthly time scales or longer;
  2. No matter from where the moisture comes, it is well mixed within the box;
  3. Evaporation and precipitation are constant within the box. Then, you can derive a simple relationship from the atmospheric water vapour conservation equation:

ρ = E / E+2Fin

This relationship is developed by Brubaker et al. (1993); ρ is the precipitation recycling ratio, which is the fraction of precipitation within the box that originates as the evaporation from the same box: E is the evaporation with the box and Fin is the horizontal moisture flux into the region, which is vertically integrated through the height of the box.

If the moisture does not come from the evaporation with the box, then it must come from outside in form of the moisture advection. Therefore,

α = 1-ρ = (2Fin)/(E+2Fin)

Where α is the ratio of precipitation arising from advected moisture to the total precipitation within the box.

If we further divide the Fin according to the directions, then we can calculate the contribution of advected moisture from different directions. Together with the contribution from the local evaporation, we can figure out from where the moisture to the precipitation within the box comes.

ρ + αW + αE + αN + αS =1

Where, W, E, N and S represent directions.

Take the central-eastern China for example (Figure 1, left). Applying the aforementioned equations to this region shows the seasonal cycle of the moisture contributions from all directions in Figure 1 (middle). It is clear that the summer monsoon (via the southern boundary) makes a significant contribution during the June-July-August, especially in July (40%). However, the contribution via the western boundary is equivalent or larger. In the winter, the moisture predominantly comes via the western boundary, although the mean precipitation is small (Figure 1, right).

Applying the simple model to a realistic case requires caution. However, similar results have been found from other studies using more sophisticated methods. Besides, a statistical test done by Guo et al. (2018) shows that about 70% of the precipitation interannual variation can be explained by the moisture flux via all these boundaries.

Figure 1 (Left) The study region. The boundary is divided into west (green), east (black), north (red) and south (blue). (Middle) Percentage contributions to precipitation from the moisture influxes from different directions, as well as from the local evaporation in grey. (Right) The mean seasonal cycles of precipitation calculated from the ERA-Interim re-analysis during 1979-2012, units mm/month. The precipitation is separated into colours according to the moisture contributions from each section of the boundary.


Brubaker, Kaye L., Dara Entekhabi, and P. S. Eagleson, 1993. Estimation of Continental Precipitation Recycling. Journal of Climate.

Guo, Liang, Nicholas P. Klingaman, Marie-Estelle Demory, Pier Luigi Vidale, Andrew G. Tuner, and Claudia C. Stephan, 2018. The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability. Climate Dynamics, on-line DOI: 10.1007/s00382-017-4064-4.


Posted in China, Climate, Climate modelling, Hydrology | Leave a comment

Stronger windstorms and higher wind risk in a warmer climate

By Oscar Martínez-Alvarado

The most devastating type of winter storms to affect north-west Europe are characterised by a descending jet of air, known as a sting jet, that can result in strong, localised surface winds and wind gusts in a region of the storm not normally associated with strong surface winds. The Great Storm, that ravaged southeast England 30 years ago on 16 October 1987, is a prominent example of this type of storm and the first published case in which a sting jet was identified. Since then, sting jets have been formally identified in several other storms and the term ‘sting jet’ has become common in the media, as shown by the recent coverage of the revolution in weather forecasting triggered by the Great Storm by the BBC and The Guardian.

Last year, a team of researchers from the Department of Meteorology published a study about the frequency of sting-jet windstorms between 1979 and 2012 (Hart et al. 2017). They found that about 32% of cyclonic storms over the North Atlantic between September and May have the potential of generating sting jets. Applying the same techniques as in Hart et al. (2017), we have gone one step further and have produced the first study on how sting-jet windstorms might be different in a warmer climate (Martínez-Alvarado et al. 2018). Our study assumes the most extreme scenario of climate change considered by the Intergovernmental Panel on Climate Change (IPCC), in which greenhouse gases continue to rise throughout the 21st century.

Our results show that the proportion of cyclonic storms with the potential to generate sting jets increases to around 45% in the warmer climate. Furthermore, while the proportion of explosively-developing storms (low pressure systems whose central pressure falls very rapidly) is similar in the two climate simulations, the proportion of these storms with the potential to generate sting jets increases from 9% to 14% in the warmer climate (Figure 1). In a previous blog entry, Giuseppe Zappa discussed the changes that cyclonic storms might undergo under climate change. Among these changes he mentioned an increase in cyclones associated with extreme rainfall, related to a larger amount of moisture in the atmosphere. We think that this larger atmospheric moisture content is the reason behind the increase in the frequency of storms capable to generate sting jets. However, more work is needed to confirm this.

Figure 1: Infographic illustrating the number per winter season and percentage of all identified cyclones categorised by type of development (explosive or non-explosive) and potential to generate sting jets. A mixed symbol is used to represent the dominant types of cyclones where the rounded percentages do not add to 100% (Martínez-Alvarado et al. 2018).

We also looked at the wind risk posed by these storms for the UK and northern Europe. We found that the risk of wind speeds larger than 35 m/s over both regions increases, and that a large proportion of that increase is due to explosively-developing sting-jet storms (Figure 2). One factor to consider when looking at these results is that the models we used tend to underestimate wind speed. Therefore, this wind risk is likely to be larger in the real world.

Figure 2: Events per year of strong resolved-wind events in storms with (red shading) and without (blue shading) the potential to produce sting jets (Martínez-Alvarado et al. 2018).


Hart, N.C., S.L. Gray and P.A. Clark, 2017. Sting-Jet Windstorms over the North Atlantic: Climatology and Contribution to Extreme Wind Risk. J. Climate, 30, 5455–5471, DOI: 10.1175/JCLI-D-16-0791.1 

Martínez-Alvarado, O., S.L. Gray, N.C.G. Hart, P.A. Clark, K.I. Hodges and M.J. Roberts, 2018. Increased wind risk from sting-jet windstorms with climate change. Environ. Res. Lett., DOI: 10.1088/1748-9326/aaae3a

Posted in Climate, Climate change, Climate modelling, Environmental hazards, extratropical cyclones, Weather | Tagged | Leave a comment

High speed mathematics: reducing the computation time for weather forecasting

By Sarah Dance

Several times a day, around 10 million observations of the atmosphere are processed by operational weather services, in order to produce the next weather forecast. At the University of Reading, we have been using mathematics to understand and control the amount of computer-time taken in the forecasting process.

In numerical weather prediction, heterogeneous observations are weighted according to their uncertainty, to create our best estimate of the current state (winds, pressures, temperatures, moisture) across the globe.  This process is called data assimilation. A computer model then solves equations based on physical laws, to calculate the forecast from a few minutes to several days ahead.  The amount of computer-time taken in the data assimilation process is very important: a weather forecast that arrives after the weather has already happened is pretty useless!

The data assimilation process uses weighting matrices, describing our knowledge of the uncertainty in the observations.  We have shown how the sensitivity of the data assimilation solution, and the speed of the computer code in finding that solution, depends on the mathematical properties of the weighting matrices.

What now?
Observation uncertainty cannot be measured and must be estimated in statistical sense.  However, these estimated matrices may be noisy, and require “cleaning up” before they can be used practically.  Our results could be used to inform this clean-up process and, in turn, reduce the computational time taken for data assimilation.

Tabeart, J. M., Dance, S. L., Haben, S., Lawless, A., Nichols, N. and Waller, J., 2018. The conditioning of least squares problems in variational data  assimilation. Numerical Linear Algebra with Applications. (In Press)

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Thoughts on Standing up for Science workshop in London

By Amulya Chevuturi

On recently attending the “Standing up for Science workshop” in London, organized by “Voice of Young Science” (VoYS), I got a glimpse of the implications of my science beyond my own desk at work. I went to this workshop without too many ideas about this topic. Though I do follow science news avidly, I didn’t ever think I could be part of it just yet, if ever.  Over the course of this workshop, my ideas about involvement of young scientists in dissemination of science has changed rapidly.

The speakers from different backgrounds, experiences and perspectives gave us ideas from different points of view. The casual atmosphere and the easy interaction allowed people to communicate and raise questions without hesitation. I loved the inclination of VoYS towards strong scientific evidence, as usually propagation of false facts is the biggest challenge in science, especially in my field of climate science.

Another issue that really made me wary about standing up for science is the idea of speaking to a large audience. Personally, I have overcome my fear of public speaking, but I still don’t feel I would be articulate enough in front of a large audience. But this workshop provided us with different ways we can avoid pitfalls of media appearances or public debates. I was most encouraged by the experiences of young speakers, whom I could definitely relate to.

I met young scientists like me from very different specializations than mine, and it was heartening to hear that though we’re doing very different things, we all seem to be facing the same basic challenges and have the same underlying fears. This cemented how cohesive the VoYS community is. Being part of such an active community makes me feel comfortable while simultaneously drives me towards a feeling of let’s do something.

So for all those early career researchers, who, like me, may not have entertained the idea of their voices being heard, such a workshop would be your starting point. Or for even those who want an active role in public discussions about science, but don’t know how to go about it; Voice of Young Science will provide you with a launch pad to start speaking about science to the public on any platform.

Sense about Science is an independent campaigning charity that challenges the misrepresentation of science and evidence in public life. We advocate openness and honesty about research findings, and work to ensure the public interest in sound science and evidence is recognised in public discussion and policymaking.

Voice of Young Science is a unique and dynamic network of early career researchers across Europe committed to playing an active role in public discussions about science. By responding to public misconceptions about science and evidence and engaging with the media, this active community of 2,000+ researchers is changing the way the public and the media view science and scientists.

For more information on future VoYS workshops, see the links above. The University of Reading and the Royal Meteorological Society are two of many partners of Sense about Science and VoYS.

Captions for photographs below imagesAudience (young scientists) listening intently


Scientists on ‘how to present your work to the media’

Journalists on ‘what the media is looking for from scientists’


Getting to know each other


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Stronger turbulence causes a stir

By Paul Williams and Luke Storer

Our new study calculating that climate change will strengthen aviation turbulence has caused a stir on social media. Most of the online comments about the article have been positive – albeit expressing a little anxiety at the prospect of experiencing a doubling of the amount of severe turbulence later this century.

The new paper, as well as our previous study on this topic in Nature Climate Change, was peer-reviewed by international experts in aviation turbulence and found to be scientifically correct. However, as is commonplace in the public discussion about climate science today – at a time when opinions seem to count more than evidence and facts – a small number of non-expert commentators have misunderstood the scientific details and attempted to discredit the findings.

Some commentators say they have experienced less turbulence on their recent flights. While we do not doubt such claims, one individual person’s encounters with turbulence are obviously a very small sample from a very large distribution of possibilities. The volume of global airspace sampled by even the most frequent of fliers is tiny. Also, as we have pointed out in a third study on this topic, aircraft bumpiness depends on a number of extraneous factors in addition to the strength and frequency of atmospheric turbulence.

Some commentators assert that we “fudge” the input parameters to obtain the answers we want. This is simply untrue, as anyone reading our paper can see. The key input parameters are the fractions of the atmosphere containing light, moderate, and severe turbulence. We know these fractions from detailed in-flight measurement campaigns. Our input parameters are objectively constrained by these measurements. For example, we know that severe turbulence is found in around 0.1% of the atmosphere at typical flight cruising altitudes. This percentage value allows us to define thresholds for severe turbulence in our calculations, and to count how often those thresholds are exceeded when the climate changes. There is no fudging, because the in-flight measurements give us no freedom of choice to do anything other than what we have done.

A final source of confusion seems to be the response of the jet streams to climate change. Although much research remains to be done in this area, we know that the jet streams are driven by the equator-to-pole temperature difference: the stronger the temperature difference, the more sheared the jet stream. In the lower atmosphere, melting Arctic sea ice is causing the polar regions to warm more quickly than the tropical regions. Therefore, the lowest part of the Northern Hemisphere jet stream is expected to weaken with climate change.

Many online critics mistakenly think the same conclusion applies at flight cruising altitudes. In fact, the opposite is true. In the upper atmosphere, water vapour feedbacks are causing the tropical regions to warm more quickly than the polar regions. Therefore, the upper jet stream is expected to become more strongly sheared with climate change, increasing the fluid-dynamical instabilities that generate turbulence.

Our three peer-reviewed studies represent the cutting-edge scientific knowledge regarding how turbulence in the atmosphere is changing, and the impacts those changes could have on aviation. As scientists, that is all we can do.




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Biomass burning in South America: Impacts on the regional climate

By Gillian Thornhill

Deforestation in south America has many environmental impacts, including loss of habitat, soil erosion, changes to the water cycle and the reduced capacity of the CO2 sink the vegetation provides. Where the vegetation is burned, an additional climate impact comes from the release of smoke aerosols into the atmosphere, which affects the regional climate due to changes in the radiation reaching the surface and changes in cloud cover resulting from atmospheric heating by the aerosol.  The wind circulation, surface temperatures and precipitation in the region are also affected by increases in aerosol from biomass burning.

In order to investigate the impact of biomass burning aerosols (BBA) on the regional climate, we compared two simulations of the global atmosphere using the Met Office Unified Model HadGEM3. This work was undertaken as part of the South American Biomass Burning Analysis (SAMBBA) project, which included aircraft observations of biomass burning aerosols to provide constraints on the aerosol properties in the model. We used two realistic levels of biomass burning emissions, one case taken from a high emissions year and one from a low emissions year, and ran the model for 30 years to average out inter-annual variability. The model output from the two cases was compared for September (the month with highest smoke emissions), by taking the September means over the 30 year run.

Figure 1 shows the September mean difference in the biomass burning aerosol optical depth between the high emissions case and the low emissions case, the largest differences being over the areas with largest smoke emissions, as we might expect. The aerosol can affect cloud cover by increasing cloud burn-off as the aerosol absorbs radiation and heats up the atmosphere around it, reducing the cloud fraction at the altitude of the aerosol layer (referred to as the semi-direct effect). In Figure 2 we see the decrease in cloud cover over the area of the main biomass burning aerosol, extending up to the north-east and slightly beyond the main area of biomass burning. There are also changes in the boundary layer height and an increase in the boundary layer stability due to the increased amount of aerosol, which can affect the formation of higher convective clouds; we think this mechanism is responsible for reducing higher level clouds in this area. The absorption of downwelling shortwave radiation by the BBA (Figure 3) results in a reduction at the surface, which lowers the surface temperature slightly (Figure 4); this effect competes with the reduction in cloud cover, which tends to increase shortwave radiation at the surface. In areas with the highest biomass burning, the reduction in the downwelling shortwave from absorption by the aerosol is the stronger process. Finally there is a drying effect in the region, with a reduction in the precipitation occurring in the high emissions case (Figure 5).

Figure 1 Difference in the Aerosol Optical Depth (AOD) at 0.44 microns for September between the high emissions case and the low emissions case (H-L). Stippling represents the 95% confidence level.

As the amount of biomass burning varies from year to year, investigating the impact of high emissions versus low emissions gives us an insight into how the level of biomass burning may affect the regional climate.

Further details and discussion

Figure 2 Difference in cloud fraction for September between the high and low emissions case (H-L). Stippling represents the 95% confidence level.

Figure 3 Difference in downwelling shortwave radiation at the surface for high-low emissions cases. Stippling represents significance at the 95% confidence level.

Figure 4 Difference in surface temperature in Sep. for high-low emissions cases. Stippling represents significance at the 95% confidence interval.

Figure 5 Difference in precipitation in September for high-low emissions cases. Stippling represents significance at the 95% confidence interval. (Note different contour colour scale)

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Bali volcanic eruption: Research to help reduce flight disruption caused by ash clouds

By Helen Dacre and Andrew Prata

The volcanic ash clouds released into the atmosphere by Mount Agung in Indonesia late last year brought back memories of the 2010 eruption of Eyjafjallajökull in Iceland, which caused chaos for holidaymakers in Europe. Airlines operating flights to and from Bali and its neighbouring Indonesian islands were disrupted in late-November last year; research is ongoing to reduce the impact of volcanic eruptions on aviation in the future.

Mount Agung had been showing signs of increased seismic activity since mid-September and throughout October (Figure 1). A new phase began on 21 November when an eruption produced ash and gas up to 12,000 ft (3600 m) above sea level. The height of the ash column increased during 25–28 November; reaching as high as 23,000 ft (7000 m) on 28 November.

Figure 1. Time series of seismic activity for Mount Agung. The y-axis indicates frequency of earthquakes/eruptions per day. Data and graphic courtesy MAGMA Indonesia (

On 27 November, ash was advected toward the south-south-west which eventually forced authorities to close Denpasar International Airport, where there had been reports of ash at ground level accumulating on aircraft. Satellite imagery captured glimpses of an ash-rich plume (Figure 2), but it was often obscured by meteorological clouds. Since the eruptions in November, Mount Agung has continued to produce minor puffs of steam and volcanic ash while favourable winds have allowed Denpasar Airport to remain open.

Figure 2. Himawari-8 true colour imagery on 26 November 2017. The true colour imagery was produced following the “hybrid, atmospherically corrected” (HAC) method described by Miller et al. (2016).

Due to the damaging effect of volcanic ash on jet engines – molten ash blocks engine cooling holes causing engines to overheat and shutdown – air travel is restricted in ash contaminated airspace. A prolonged eruption, such as the 2010 Eyjafjallajökull eruption in Iceland that grounded flights across Europe, will lead to inevitable economic damage to Bali and the surrounding area due to lost tourism and productivity. In fact, there are already reports of significant impacts on the tourism industry in Bali due to recent activity at Mount Agung.

The 2010 ash crisis exposed the fragility of air travel and raised questions about the resilience and vulnerability of the world’s critical airspace infrastructure. Since 2010, work in the understanding of ash damage to aircraft has developed rapidly. In particular, aircraft engine manufacturers are now in a much better position to advise on the levels of ash that their engines can safely tolerate.

New research will aid decision-making
In a report published in July 2016, Rolls Royce (the UK’s largest engine manufacturer) outlined new engine susceptibility guidelines, which describe engine tolerance limits in terms of a dosage (i.e. accumulated concentration over time). These guidelines are based on the latest field studies carried out on aircraft engines.

At the University of Reading we are working with Rolls Royce, British Airways and the Civil Aviation Authority (CAA) to develop a tool that is able to calculate the ash dosage encountered by an aircraft along its flight path, and its associated uncertainty, for the first time.

The tool demonstrates a method by which airline operators can calculate ash dosage along time-optimal flight routes during volcanic eruptions. It also provides an assessment of the uncertainty in ash concentration forecasts. In order to represent this uncertainty, we have constructed an ensemble: a set of model realisations created by perturbing various uncertain parameters used in the model. We then use “model agreement maps” to represent the percentage of ensemble members that resulted in an ash concentration above a certain peak concentration threshold. The percentages are then discretised into three categories: less likely (0–10%), likely (10–50%) and very likely (50–100%). This approach gives the stakeholder an appreciation for uncertainty in the model and encourages the use of uncertain information in operational decision-making procedures.

Figure 3 shows an annotated screenshot of the web-tool for a hypothetical eruption of Katla volcano (Iceland) in January 2017. In this example, a peak concentration of 4 mg m-3 was used to construct the model agreement maps. The tool comprises four components: (1) model agreement maps, (2) flight route information, (3) the duration of engine exposure vs. ash concentration (DEvAC) chart (see Clarkson et al. 2016 for details) and (4) the along-flight ash concentration and dosage.

Figure 3. Annotated screenshot of the ash dosage web-tool currently under development at the University of Reading.

The new knowledge developed in the project will be used by the CAA to support strategic decision-making, and will enable new regulations to be developed that are based on the latest understanding of volcanic ash risk to aircraft engines, resulting in a more resilient UK airspace infrastructure.


Clarkson, R. J., E. J. E. Majewicz, and P. Mack, 2016. A re-evaluation of the 2010 quantitative understanding of the effects volcanic ash has on gas turbine engines. Proc. Inst. Mech. Eng. G J. Aerosp. Eng., 230(12), 2274–2291, doi:10.1177/0954410015623372.

Miller, S. D., T. L. Schmit, C. J. Seaman, D. T. Lindsey, M. M. Gunshor, R. A. Kohrs, Y. Sumida, and D. Hillger, 2016. A sight for sore eyes: The return of true color to geostationary satellites. B. Am. Meteorol. Soc., 97, 1803–1816, doi:10.1175/BAMS-D-15-00154.1.

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Chaotic Convection

By Todd Jones

In the traditional global climate model (GCM) configuration, models simulate atmospheric motions explicitly on spatial grids with spacings on the order of 100 km. Motions on finer scales are not directly simulated. Instead, we use parameterizations, some mathematically simpler, perhaps partially empirical, description of the effects of these motions. To facilitate a harmonious and practical interaction between these scales, they are assumed to be distinct and separate, interacting through time tendencies of temperature and moisture.

Of course, in the real atmosphere, motions occur explicitly in a continuum between and beyond these scales, and it’s unlikely that a specific mean state taken over the area of a typical GCM cell would ever yield exactly the same small-scale motions if that same mean were ever to recur. Instead, it may be similar with some chaotic variability due to the atmosphere’s sensitive dependence on initial conditions. Unfortunately, a deterministic parameterization won’t provide that variability. They’ve historically had no clue about variability on the small scale, the convection that has occurred previously, or how either should influence its representation of convection [Reference 1].

There are clues that these missing pieces are needed for models to produce correct large-scale phenomena, such as the Madden-Julian Oscillation (MJO) or even the stratospheric Quasi-Biennial Oscillation [Refs 2-3], and precipitation statistics, particularly its timing and the occurrence of extreme events [Ref 4]. More recently, there have been efforts to address this deficiency, by adding various representations of small-scale variability and memory to existing convection schemes [Refs 5-7], and one of the remaining questions involves how to represent these ideas correctly in convective parameterizations.

Rather than somewhat arbitrarily stochastically perturbing an existing convective parameterization, some employ the superparameterization (SP) framework [Ref 8; Figure 1]. In SP, the conventional parameterizations are replaced by a cloud permitting model (CPM). A 32-column 2-dimensional curtain on a 4 km grid is placed in each GCM column of the Community Atmosphere Model (CAM). Changes in the state of the GCM column force better-resolved motions within the curtain, while parameterizing the microphysics, radiation, and turbulence on the 4- m grid, that is, at much finer time and space scales that should give more accurate results. Then the CPM reports to the GCM how much precipitation was produced and what temperature and moisture changes resulted from the convective-scale motions.

Figure 1. Schematic representation of the interaction between the global model’s resolved and unresolved scales in CAM, SP-CAM, and the new MP-CAM.

SP-CAM provides individual convective realizations with their sensitive dependence on initial conditions and small-scale structures as well as convective memory, as the convection within the curtain is only initialized at the start of the full simulation, rather than at each GCM time step. We know that it provides an improved solution compared to CAM because of this. What we would like to find out, though, is whether it is possible to create a more deterministic parameterization (like that of CAM) that can retain the benefits of SP-CAM. To this aid in understanding some aspects of this issue, a model was developed for my PhD research at Colorado State University. Shown at the bottom of Figure 1 is the multiple-superparameterization (MP) configuration of the CAM. In MP-CAM we employ 10 CPMs running independently. Each CPM is initialized with different thermal perturbation fields to get things moving, and due to sensitive dependence on initial conditions, they will always be doing something different. In the CPM-domain-mean sense, though, they will remain close together as they each see the same GCM state. Following the CPM computations, their mean column tendencies are averaged in an ensemble sense and passed to the GCM. In this way, the convective effects are more like an “expected mean” that a deterministic parameterization tries to produce and the benefits of simulation at finer scales are retained.

Comparing multi-decadal climate simulations in these frameworks, a number of interesting results emerge [Ref 9]. The models produce slightly different climate features, but of interest to many is the representation of intraseasonal variability (Figure 2). In these wavenumber-frequency power spectra diagrams of outgoing longwave radiation (OLR), we see that MP simulation, with its more smoothed and deterministic representation of the small-scale, shows only slight degradation in the MJO signal (the power peak near eastward wavenumber 1, frequency longer than 30 days). By this estimate, it appears that losing the stochastic nature of the SP tendencies has a negative impact on the result, though one may also reasonably conclude that the bulk of the improvement over the standard CAM is retained, a function of better-resolved motions with convective memory.

Figure 2. Ratios of symmetric spectral power to a smoothed background power for OLR for NOAA observations, CAM, SP-CAM (Control), and MP-CAM (Ensemble). Dispersion curves of the linear shallow water equations are shown in solid black for equivalent depths of 12, 25, and 50 metres. Wave types are Equatorial Rossby (ER), inertio-gravity (IG), and Kelvin.

The nature of the MP approach also allows for study of the range of potential solutions under the same large-scale state. For instance, each CPM produces a different value for grid-cell precipitation, and analysis of that spread can provide insight into the geographic locations and large-scale atmospheric structures that are associated with unpredictable convective precipitation (Figure 3). I encourage those interested in seeing how difficult-to-predict precipitation related to measures of CAPE, atmospheric stability, and critical column water vapour to check out my dissertation [Ref 9] and keep an eye out for two papers currently in preparation for submission to J. Adv. Model. Earth Syst. (JAMES).

Figure 3. Average values across 5 Aprils of CPM-ensemble mean (left) and standard deviation (right). The MP-CAM framework allows for identification of regions of difficult-to-predict precipitation.


[1] Jones, T. R., and D. A. Randall, 2011: Quantifying the limits of convective parameterizations. J. Geophys. Res. Atmos., 116 (D8), doi:10.1029/2010JD014913.

[2] Ricciardulli, L., and R. R. Garcia, 2000: The excitation of equatorial waves by deep convection in the NCAR Community Climate Model (CCM3). J. Atmos. Sci., 57 (21), 3461–3487, doi: 10.1175/1520-0469(2000)057⟨3461:TEOEWB⟩2.0.CO;2.

[3] Neelin, J. D., O. Peters, J. W. B. Lin, K. Hales, and C. E. Holloway, 2008: Rethinking convective quasi-equilibrium: Observational constraints for stochastic convective schemes in climate models. Philos. Trans. R. Soc. A, 366 (1875), 2581–2604, doi:10.1098/rsta.2008.0056.

[4] Li, F., D. Rosa, W. D. Collins, and M. F. Wehner, 2012: “Super-parameterization”: A better way to simulate regional extreme precipitation? J. Adv. Model. Earth Syst., 4 (2), doi:10.1029/ 2011MS000106.

[5] Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ecmwf ensemble prediction system. Q.J.R. Meteorol. Soc., 125 (560), 2887–2908, doi: 10.1002/qj.49712556006.

[6] Plant, R. S., and G. C. Craig, 2008: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci., 65 (1), 87–105, doi:10.1175/2007JAS2263.1.

[7] Berner, J., U. Achatz, L. Batté, L. Bengtsson, A.d. Cámara, H.M. Christensen, M. Colangeli, D.R. Coleman, D. Crommelin, S.I. Dolaptchiev, C.L. Franzke, P. Friederichs, P. Imkeller, H. Järvinen, S. Juricke, V. Kitsios, F. Lott, V. Lucarini, S. Mahajan, T.N. Palmer, C. Penland, M. Sakradzija, J. von Storch, A. Weisheimer, M. Weniger, P.D. Williams, and J. Yano, 2017: Stochastic Parameterization: Toward a New View of Weather and Climate ModelsBull. Amer. Meteor. Soc., 98, 565–588,

[8] Khairoutdinov, M., D. Randall, and C. DeMott, 2005: Simulations of the atmospheric general circulation using a cloud-resolving model as a superparameterization of physical processes. J. Atmos. Sci., 62 (7), 2136–2154, doi:10.1175/JAS3453.1.

[9] Jones, T. R. (2017), Examining chaotic convection with super-parameterization ensembles, PhD Dissertation, Colorado State University, Fort Collins, CO.



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