Has The Atlantic Ocean Circulation Been In Long-term Decline?

By: Jon Robson

A number of recent high-profile studies have strongly suggested that an important part of the North Atlantic Ocean circulation – the AMOC – has declined and that it is edging closer to a tipping point. Such a long-term decline would have important implications for regional weather and climate for Europe and North America, and a collapse of the AMOC could have serious consequences globally. However, in the sixth assessment report for the Intergovernmental Panel On Climate Change (IPCC) working group 1, the confidence in a long-term 20th Century AMOC decline was assessed as low (down from medium confidence in the IPCC special report on Ocean and Cryosphere in a changing climate, SROCC). So what is going on, and how did we* come to that decision?

The AMOC – or specifically the Atlantic Meridional Overturning Circulation – is a system of currents that brings warm water from the lower latitude Atlantic to the higher latitude Atlantic (see schematic in figure 1). As such, it is a major player in the movement of heat and carbon through the climate system and is, hence, an important regulator of global climate.

Figure 1: Schematic of the AMOC circulation system. Red shows a simplification of warm upper ocean currents, including the gulf stream. Blue shows a simplification of denser (and colder) southward flowing water at depth. Also shown is the RAPID array, which has been observing AMOC at ~26N since 2004. From Srokosz and Bryden, 2015.

There is little doubt that we expect the AMOC to weaken due to increased greenhouse gas emissions. This is because a key driver of the AMOC is the formation of dense seawater as it gets colder and saltier in the northern North Atlantic and Arctic. So, as the world warms, the high-latitude ocean will warm and the melting of ice sheets will dump more freshwater into the ocean. This will decrease the rate at which dense water is formed and slow the AMOC.

However, although we have high confidence in a future AMOC decline, there remain very large uncertainties and many questions still exist. For example: How fast will the AMOC decline in the next few decades? When will the AMOC decline? or, has the AMOC already declined?

Unfortunately, that last question is difficult to assess as routine observations have only existed since the early 2000s. Therefore, we need to use a range of evidence – including observations, model simulations (including ocean-reanalysis, ocean-only and coupled), and indirect observations or “proxies” – to constrain what we think happened.

Over the recent periods (circa 1980-present), these different sources of evidence are all available in some form and, in a recent study**, we show that they generally agree that there has been significant variability in the AMOC and no discernable long-term trend. However, over the longer period of the 20th Century, we can only rely on coupled simulations and proxies.

One such “proxy”, or fingerprint, of an AMOC slowdown, is thought to be a cooling of the subpolar North Atlantic (that’s the bit roughly between 45-65°N) – at least once you make those temperatures relative to global surface temperatures. Such a “warming hole index” (as it is sometimes called) indicates that the subpolar North Atlantic has cooled significantly relative to the rest of the globe – indicating that the AMOC has declined. Furthermore,  many other AMOC proxies have also suggested a similar decline and that the AMOC is at its weakest for thousands of years.

However, the results from the proxies are in contrast to the results from coupled models which indicate that the AMOC increased over the 20th Century due to external forcing. Indeed, historical simulations made for the CMIP6 show an increase in the AMOC from 1850–1985 (see top panel of figure 1). This increase is largely due to a competition between historical greenhouse gas and anthropogenic aerosol precursor emissions (see bottom panel of figure 2). Simply put, more models now include aerosol-cloud interactions and, thus, simulate a stronger anthropogenic aerosol forcing which counters the greenhouse gas-induced weakening.

Figure 2: Shows the evolution of the with varying historical external forcings. Top shows the comparison between simulations from CMIP6 and CMIP5. Bottom shows the changes in AMOC in CMIP6 models when only one external forcing is changed at in turn, including greenhouse gasses (hist-GHG, green), and anthropogenic aerosol precursors (hist-aer, blue), and natural changes (e.g. sun or volcanic eruptions, hist-nat, yellow). Taken from Menary et al, 2020.

But what line of evidence is more believable? Well, this is where the waters start to get a bit murkier.

Indeed, there are many reasons to be sceptical about the – usually low resolution – coupled model simulations. For example, there are many shortcomings in how ocean models represent the North Atlantic including the formation of the dense “headwaters” of the AMOC. CMIP6 historical simulations also struggle to simulate other aspects of the climate related to aerosol changes, including Northern Hemisphere temperatures and top of atmosphere shortwave radiation. So, shouldn’t we just trust the proxies?

Well, the problem is that, in the absence of AMOC observations, model simulations have been used to test and (in some cases) calibrate the AMOC proxies. In other words, the different lines of evidence are not fully independent. Furthermore, some studies suggest that the temperature based proxies may not work so well for picking out historically forced variability, and other studies have highlighted that other processes may be contributing to such AMOC “fingerprints”. Finally, there are many proxies, and not all of them agree.

Therefore, to reflect these counteracting lines of evidence we chose to reduce the certainty of a long-term 20th Century AMOC decline to “low confidence” in IPCC AR6.

However, it is important to underline that a long-term decline of the AMOC is a plausible interpretation of the evidence that we have. Furthermore, If the AMOC has declined significantly already, then this would be further – and worrying – evidence that current coupled models may systematically underestimate the sensitivity of the AMOC to greenhouse gasses and the likelihood of a rapid decline in the AMOC. A dangerous position to be in, indeed!

Therefore, there is an urgent need to better understand the AMOC and the current mismatch between model simulations and proxies. To make progress we need to continue to bring a range of observations, models, proxies, and other tools to understand the drivers of the AMOC variability and changes, and to understand the representation of the AMOC in models.

Ultimately, to predict the overall trajectory of the AMOC over the next few decades we still have more to do to understand the AMOC in the past.


*All IPCC WG1 authors who were involved in summarising the AMOC were involved in discussing the confidence statements. These covered Chapter 2 (Karina von Schuckmann (LA) and Gerard McCarthy (CA)), Chapter 3 (Shayne McGregor (LA) and myself (CA)) and Chapter 9 (Sybren Drijfhout (LA)).

** Unfortunately Jackson et al, 2022 is behind a paywall – please email me for a preprint!


Caesar, L., Rahmstorf, S., Robinson, A. et al. Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature 556, 191–196 (2018). https://doi.org/10.1038/s41586-018-0006-5

Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Chang. 11, 680–688 (2021). https://doi.org/10.1038/s41558-021-01097-4

Jackson, L.C., Kahana, R., Graham, T. et al. Global and European climate impacts of a slowdown of the AMOC in a high resolution GCM. Clim Dyn 45, 3299–3316 (2015). https://doi.org/10.1007/s00382-015-2540-2

Weijer, W., Cheng, W., Garuba, O. A., Hu, A., & Nadiga, B. T. (2020). CMIP6 models predict significant 21st century decline of the Atlantic Meridional Overturning Circulation. Geophysical Research Letters, 47, e2019GL086075. https://doi.org/10.1029/2019GL086075

Jackson, L.C., Biastoch, A., Buckley, M.W. et al. The evolution of the North Atlantic Meridional Overturning Circulation since 1980. Nat Rev Earth Environ 3, 241–254 (2022). https://doi.org/10.1038/s43017-022-00263-2

Caesar, L., McCarthy, G.D., Thornalley, D.J.R. et al. Current Atlantic Meridional Overturning Circulation weakest in last millennium. Nat. Geosci. 14, 118–120 (2021). https://doi.org/10.1038/s41561-021-00699-z

Thornalley, D.J.R., Oppo, D.W., Ortega, P. et al. Anomalously weak Labrador Sea convection and Atlantic overturning during the past 150 years. Nature 556, 227–230 (2018). https://doi.org/10.1038/s41586-018-0007-4

Menary, M. B., Robson, J., Allan, R. P., Booth, B. B. B., Cassou, C., & Gastineau, G., et al. (2020). Aerosol-forced AMOC changes in CMIP6 historical simulations. Geophysical Research Letters, 47, e2020GL088166. https://doi.org/10.1029/2020GL088166

Li, F., Lozier, M. S., Danabasoglu, G., Holliday, N. P., Kwon, Y., Romanou, A., Yeager, S. G., & Zhang, R. (2019). Local and Downstream Relationships between Labrador Sea Water Volume and North Atlantic Meridional Overturning Circulation Variability, Journal of Climate, 32(13), 3883-3898. Retrieved Apr 27, 2022, from https://journals.ametsoc.org/view/journals/clim/32/13/jcli-d-18-0735.1.xml

Keil, P., Mauritsen, T., Jungclaus, J. et al. Multiple drivers of the North Atlantic warming hole. Nat. Clim. Chang. 10, 667–671 (2020). https://doi.org/10.1038/s41558-020-0819-8

Moffa-Sánchez, P., Moreno-Chamarro, E., Reynolds, D.J., Ortega, P., Cunningham, L., Swingedouw, D., Amrhein, D.E., Halfar, J., Jonkers, L., Jungclaus, J.H., Perner, K., Wanamaker, A. and Yeager, S. (2019), Variability in the Northern North Atlantic and Arctic Oceans Across the Last Two Millennia: A Review. Paleoceanography and Paleoclimatology, 34: 1399-1436. https://doi.org/10.1029/2018PA003508

Bellomo, K., Angeloni, M., Corti, S. et al. Future climate change shaped by inter-model differences in Atlantic meridional overturning circulation response. Nat Commun 12, 3659 (2021). https://doi.org/10.1038/s41467-021-24015-w

Flynn, C. M. and Mauritsen, T.: On the climate sensitivity and historical warming evolution in recent coupled model ensembles, Atmos. Chem. Phys., 20, 7829–7842, https://doi.org/10.5194/acp-20-7829-2020, 2020.

Posted in Climate, Climate change, Climate modelling, North Atlantic, Oceans | Leave a comment

Antarctic Sea Ice: The Global Climate Driver Of The South

By: Holly Ayres

In the Northern Hemisphere, our closest region of sea ice (not to be confused with land ice) is the Arctic, a vast region of frozen ocean at the North Pole. Antarctica, a huge mountainous land mass at the South Pole, is geographically opposite to the Arctic. Surrounded by the Southern Ocean, sea ice, eastward currents and strong westerly winds, it is a unique, remote, and distant location to most of us. Around 90% of humans live in the Northern Hemisphere, which makes sense, since around 70% of land is in the Northern Hemisphere. We would be forgiven for assuming that changes to Antarctic sea ice would not impact us as much as changes to the Arctic.

Antarctic sea ice extent reaches a maximum mean of approximately 18.5 million km2 at its winter peak. In the summer months, Antarctic sea ice is almost completely melted by comparison, at a mean of approximately 3 million km2. Some sea ice remains around coastal areas and regions of the Weddell and Ross Seas and higher latitude regions. Figure 1: (left) Antarctic sea ice extent maximum September 2021, (right) Minimum February 2022. Images from the National Snow and Ice Data Centre, 2022.

How is it changing?

Past trends (pre-2016) in Antarctic sea ice extent show a small but significant decrease. Whereas, Arctic sea ice has been decreasing year on year, in line with the average global temperature increase. Many possible reasons for this contradictory trend in Antarctic sea ice have been proposed by a number of studies, including but not limited to, relationships to the stratospheric ozone hole above Antarctica and the positive trend in the Southern Annular Mode, via intensification of the westerly winds surrounding the region. Other theories involve the ‘ocean asymmetry’ to the Arctic, in addition to interactions with increased glacial melt and local pressures systems such as the Amundsen sea low (e.g. Turner et al., 2009; Polvani et al., 2011; Liu and Curry, 2010; Bintanja et al., 2013; Mackie et al., 2020).

However, in recent years, this trend has almost turned around, with a sea ice minimum in 2016/17, and again in February 2022, changing the significance of this increasing trend (Parkinson 2019). We still do not know a lot about the 2022 minimum, but multiple studies have shown that a combination of conditions caused the 2016 minimum. These include influences from El Nino Southern Oscillation and the Southern Annular Mode, changes to atmospheric wave patterns, and the opening of the Weddell Sea polynya (e.g. Turner et al., 2017; Schlosser et al., 2017; Stuecker et al., 2017; Meehl et al., 2019; Wang et al., 2019; Turner et al., 2020).

It is clear that various aspects of the climate have a big impact on Antarctic sea ice, but what about the other way around?

Figure 2: Arctic (top) and Antarctic (bottom) annual sea ice extent anomalies, showing reduction in Arctic sea ice extent and slight increase in Antarctic sea ice extent, from 1979 to 2022. Images from the National Snow and Ice Data Centre, 2022.

The future and impacts on the climate

Up until recently, it was thought that what happens in the Antarctic stays in the Antarctic- or at least in terms of the climate response to sea ice change. The region is sheltered, protected, and seemingly unaffected by the warming world beyond its reach, so why would sea ice impact anything other than the high latitude Southern Hemisphere?

Sea ice acts as a barrier between the ocean and atmosphere. When that barrier is melted, several things happen to the climate system. The area loses its reflective icy surface, meaning more solar radiation is absorbed by the ocean, causing further warming. This process is called ‘polar amplification’. In the winter months, the ocean is usually a little warmer than the air, due to the high specific heat capacity of water. Heat and gasses can now freely be exchanged between the two, and in the winter months, this means heat is released from the ocean to the atmosphere, that would not usually be released if the winter sea ice barrier were still intact. Sea ice also interacts with the ocean’s deep circulation. When sea ice forms, salt is rejected into the water column in a process called brine rejection. Changes in temperature and salinity control the ocean circulation, therefore sea ice plays a key role.

Recent climate modelling studies have assessed this in detail, being the first to assess the full ocean-atmosphere-ice coupled model impacts to Antarctic sea-ice loss (England et al. 2020a,b; Ayres et al., 2022). Antarctic sea-ice loss first triggers a heat flux response from the ocean to atmosphere, leading to local surface warming over the Southern Ocean. This warming leads to changes in the local pressure and wind systems, namely a negative Southern Annular Mode index and weaker westerly winds. Warming Southern Ocean surface temperatures spread both ways to the Antarctic continent and mid-latitudes oceans, and eventually the equator after several years. The warming changes the wind patterns in the tropical pacific, impacting ocean circulation and the upwelling of cool ocean waters, further warming the tropics. The warming is spread into the Northern Hemisphere through a large atmospheric wave, and eventually reaches the Arctic. Warming in the Arctic leads to sea ice loss, all triggered by that initial Antarctic sea-ice loss, several years before. Meanwhile, the ocean also responds to the Antarctic sea-ice loss, first warming and reducing the salinty of the Southern Ocean and weakening the wind driven easterly currents that surround Antarctica. This leads to further warming and salinty changes globally, across all oceans.

The Arctic plays a huge impact on the climate, observed globally to have connections with the world’s oceans and atmosphere. However, despite the majority of research focusing on the Arctic, it seems that changes to Antarctic sea ice may also play a huge role in the future of the global climate, even in the Northern Hemisphere.

Antarctic sea ice loss would impact the entire global climate, with impacts from the top of the atmosphere to the depths of the ocean, pole to pole.


Ayres, H. C., Screen, J. A., Blockley, E. (2022) The Coupled Climate Response to Antarctic Sea Ice Loss. J.Clim. https://doi.org/10.1175/JCLI-D-21-0918.1.

Bintanja, R., G. J. Van Oldenborgh, S. S. Drijfhout, B. Wouters, and C. A. Katsman, 2013: Important role for ocean warming and increased ice-shelf melt in Antarctic sea-ice expansion. Nat. Geosci., 6, 376–379, https://doi.org/10.1038/ngeo1767.

England, M. R., L. M. Polvani, and L. Sun, 2020a: Robust Arctic warming caused by projected Antarctic sea ice loss. Environ. Res. Lett., in press, 0–31, https://doi.org/10.1088/1748-9326/abaada.

England, M. R., L. M. Polvani, L. Sun, and C. Deser, 2020b: Tropical climate responses to projected Arctic and Antarctic sea-ice loss. Nat. Geosci., 13, 275–281, https://doi.org/10.1038/s41561-020-0546-9.

Liu, J., and J. A. Curry, 2010: Accelerated warming of the Southern Ocean and its impacts on the hydrological cycle and sea ice. Proc. Natl. Acad. Sci. U. S. A., 107, 14987–14992, https://doi.org/10.1073/pnas.1003336107.

Mackie, S., I. J. Smith, J. K. Ridley, D. P. Stevens, and P. J. Langhorne, 2020: Climate response to increasing antarctic iceberg and ice shelf melt. J. Clim., 33, 8917–8938, https://doi.org/10.1175/JCLI-D-19-0881.1.

Meehl, G. A., J. M. Arblaster, C. T. Y. Chung, M. M. Holland, A. DuVivier, L. Thompson, D. Yang, and C. M. Bitz, 2019: Sustained ocean changes contributed to sudden Antarctic sea ice retreat in late 2016. Nat. Commun., 10, 14, https://doi.org/10.1038/s41467-018-07865-9.

Parkinson, C. L., 2019: A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proc. Natl. Acad. Sci., 201906556, https://doi.org/10.1073/pnas.1906556116.

Polvani, L. M., D. W. Waugh, G. J. P. Correa, and S. W. Son, 2011: Stratospheric ozone depletion: The main driver of twentieth-century atmospheric circulation changes in the Southern Hemisphere. J. Clim., 24, 795–812, https://doi.org/10.1175/2010JCLI3772.1.

Schlosser, E., F. A. Haumann, M. N. Raphael, F. Alexander Haumann, and M. N. Raphael, 2017: Atmospheric influences on the anomalous 2016 Antarctic sea ice decay. Cryosph. Discuss., 13, 1–31, https://doi.org/10.5194/tc-2017-192.

Sea Ice Index, National Snow and Ice Data Center. Accessed April 8, 2022. https://nsidc.org/data/seaice_index/

Stuecker, M. F., C. M. Bitz, and K. C. Armour, 2017: Conditions leading to the unprecedented low Antarctic sea ice extent during the 2016 austral spring season. Geophys. Res. Lett., 1–12, https://doi.org/10.1002/2017GL074691.

Turner, J., and Coauthors, 2009: Non-annular atmospheric circulation change induced by stratospheric ozone depletion and its role in the recent increase of Antarctic sea ice extent. Geophys. Res. Lett., 36, 1–5, https://doi.org/10.1029/2009GL037524.

Turner, J., T. Phillips, G. J. Marshall, J. S. Hosking, J. O. Pope, T. J. Bracegirdle, and P. Deb, 2017: Unprecedented springtime retreat of Antarctic sea ice in 2016. Geophys. Res. Lett., 44, 6868–6875, https://doi.org/10.1002/2017GL073656.

Turner, J., and Coauthors, 2020: Recent Decrease of Summer Sea Ice in the Weddell Sea, Antarctica. Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL087127.

Wang, G., H. H. Hendon, J. M. Arblaster, E.-P. Lim, S. Abhik, and P. van Rensch, 2019: Compounding tropical and stratospheric forcing of the record low Antarctic sea-ice in 2016. Nat. Commun., 10, 13, https://doi.org/10.1038/s41467-018-07689-7.

Posted in Antarctic, Arctic, Atmospheric circulation, Climate, Climate change, Climate modelling, Cryosphere, Oceans, Polar | Leave a comment

Investigating Clouds With New Radar Technology

By: Christopher Westbrook

Since I joined the University of Reading in 2005 as a research assistant, I have been using radars at the Chilbolton Observatory to study the processes in clouds. I’m very excited at the moment to be part of a collaborative project to make unique measurements of clouds with a new “G-band” radar being developed at the observatory. This radar is unusual because it operates at much higher frequency (shorter wavelength) than conventional cloud radars, and this allows us to do some interesting things.

Radars work by sending out electromagnetic waves into the atmosphere. The oscillating electric field in these waves makes the bound charges in ice or water jiggle around, and this, in turn, creates new waves, some of which find their way back to the radar and are detected as echoes. So this is really useful – it means we can tell where the clouds are (by timing how long it takes for the echo to reach us), plus we can tell something about how much stuff is in the cloud: more particles = a bigger echo; bigger particles = a bigger echo too.

Here’s an example of the kind of data we get from radar. The sky on this day was patterned with cirrus clouds (photo on the right). Cirrus clouds are clouds with a brush-like structure that are present in the upper part of the troposphere where it is very cold. They are composed of small ice crystals falling through the air. The data on the left hand panel is from a radar that sits and looks upwards at whatever clouds drift past. On the horizontal axis, we have time, while on the vertical axis we have height. So you can see these cirrus clouds were present between about 6000 and 9000 metres in height. The strength of the echo is measured as a “radar reflectivity”, and is a logarithmic unit. The higher the number, the stronger the echo, and a change of 10 dB on the reflectivity scale corresponds to a 10-fold increase/decrease in echo strength.

So there is lots of useful information in radar, but you can see from what I wrote earlier that interpreting the echo strength is ambiguous: how do I tell if the reflectivity I measure is caused by a lot of small particles or only a few larger particles?

The answer is to use a mixture of different frequencies. To understand why this helps, we can look at some simulations my colleague Karina McCusker did when she was investigating how ice crystals scatter electromagnetic waves. In this colourful figure below, she is sending radar waves in from the top of each panel, and visualising what the wave inside the crystal looks like. What you can see is that as we increase the frequency of the radar (top left = lowest frequency, bottom right = highest frequency), we get more and more wavey structure within the crystal, and the patterns become more complex. This directly affects how strong the radar echo is – the more wavey patterns we have inside the crystal, the more interference there is between waves scattered from different parts of the crystal when they come back to the radar as an echo.

We can exploit this interference because it tells us about how many wavelengths fit inside a crystal. And the bigger the crystal, the more wavelengths fit into it, and the bigger our interference effects will be. So by measuring the amount of interference (by comparing the size of the echoes at 2 or more frequencies) we can tell how big the particles were!

To make this work, we obviously need the radar wavelength to be comparable to the crystal size. In conventional cloud radars, the wavelength is several millimetres to a few centimetres, which is quite large compared to many particles in the atmosphere, such as the small ice crystals in cirrus clouds. This is where our new G-band radar comes in! Its wavelength is around a millimetre, and so, uniquely, it can measure the size of these tiny crystals.

The radar itself has been developed by experts in millimetre wave technology at Rutherford Appleton Labs, University of St Andrews, and Thomas Keating Ltd. It is a “demonstrator” instrument – proving the technology and data analysing techniques for this new type of cloud radar. We have now operated the radar in a few case studies – here is an example of measurements of rain and ice crystals:

Again, we have time along the horizontal axis and height on the vertical axis. This scene contains a mixture of clouds containing ice crystals (above 1200 metres) and raindrops (below 1200 metres). Karina is working on analysing the ice clouds, in this case, to estimate how big the crystals were and what shapes they had. She is correlating this against measurements with cloud probes on board the FAAM research aircraft.

Meanwhile, Ben Courtier (who used to be a PhD student here at Reading, and now works at the University of Leicester) has just published the first paper from the new radar, showing that in the rain we can measure distribution of drop sizes and even figure out what the updraughts and downdraughts of the air were!

This is a new and experimental instrument. In the long term, we’d like to make the radar even better by increasing the amount of power it can transmit, and making it autonomous so we can collect data 24 hours a day, 7 days a week.

Posted in Climate, Clouds, Microphysics, radar, Remote sensing | Leave a comment

Climate Change In The Ionosphere

Christopher Scott

There is much work being done to assess the impact of climate change on the lower atmosphere. Since this is the part of the Earth’s atmosphere affecting the planet’s surface, where (the bulk of!) life on Earth resides, it is imperative that we understand the impact on our biosphere but the higher levels of our atmosphere, that straddle the boundary between Earth and space, are also thought to be affected.

Figure 1: The regions of the Earth’s upper atmosphere. The ionosphere lies within the Thermosphere.

With an increase in CO2 trapping heat in the lower atmosphere, Brasseur and Hitchman (1988) showed that the middle atmosphere is expected to cool. Roble and Dickinson (1989) extended this argument to the upper atmosphere. Assuming a doubling in the concentration of CO2 and methane at 60 km (as then predicted to occur by the mid 21st century), their results indicated that the mesosphere (see figure 1) would be expected to cool by around 5K while the thermosphere (above 200km) would be expected to cool by about 40K. While the thermosphere is difficult to observe directly, a small fraction of the gasses within it are ionised by incoming solar extreme ultraviolet and x-ray radiation to form the ionosphere (figure 2). This electrified region is known to reflect shortwave radio waves (indeed this property enables such signals to be transmitted around the world) and routine observations have been made of the Earth’s ionosphere, most notable at Slough, UK, since 1931 (figure 3).

Figure 2: The structure of the Earth’s ionosphere. The upper F-region, in particular, the F2 layer have been the focus for studies into the response of the ionosphere to climate change.

Rishbeth (1990) pointed out that the thermal contraction of the thermosphere is expected to lower the ionospheric F2 layer by 15-20 km, with this estimate being supported by subsequent modelling work (Rishbeth and Roble, 1992). With over ninety years of data now available, this trend should be detectable in the ionospheric records. The first to report on such observations was Bremer (1992) using data from the ionosonde station at Juliusruh in Germany. These data suggested that the height of this layer averaged over seasons, dropped by 8km in 33 years. Subsequently, many others reported observations from different ionospheric stations and the results were far from consistent. Some locations, such as Stanley in the Falkland Islands (Jarvis, 1998), showed a similar decrease, while others, such as Slough in the UK, showed no decrease at all (Bremer, 2004).

Figure 3: Long-term variation in the ionosphere at Slough/Chilton (UK) and Stanley (Falkland Islands). Note the approximately 11 year variation in response to changes in solar x-ray and EUV emissions throughout the solar activity cycle. The peak concentration in each ionospheric layer is expressed in MHz. This radio frequency, (f, Hz), is related to the electron concentration (N, m-3) by the relation f = 8.98√N.

There are several reasons for this apparent disagreement and, as Rishbeth points out in his subsequent article (Rishbeth, 1999), we need to understand the impact of each of these before we have any hope of reliably teasing out a signal due to climate change.

Firstly, while radio measurements of the strength (concentration) of ionisation are very accurate, heights are initially calculated assuming that the radio waves are travelling through free space. A radio wave being reflected from an upper layer has to travel through underlying ionisation and this slows the signal, leading to heights of the upper layers being over-estimated (so-called virtual heights). The influence of this underlying ionisation can be accounted for but any gaps in this information will lead to an uncertainty in the true height of these layers.

Secondly, the ionosphere is created by solar ionisation which is known to vary over an approximately eleven year cycle. While we now have accurate space-based measurements of solar emissions, ionospheric measurements from before the space age require the use of proxies for this radiation, such as the F10.7 index, based on solar radio emissions. In order to be able to remove any bias these indexes need to be carefully calibrated with modern spacecraft observations.

Figure 4: A Coronal Mass Ejection (CME) as imaged by Heliospheric Imager onboard the NASA STEREO mission. The Sun is just outside the frame to the right of the image and the CME is travelling from right to left. The two bright objects are the planets Venus (left) and Mercury (right). Image produced by RAL Space (www.stereo.rl.ac.uk).

Thirdly, solar radiation is only one way that the Sun can affect the Earth’s upper atmosphere. Throughout the solar cycle, vast eruptions of magnetised plasma from the solar atmosphere are ejected through the solar-system (figure 4). If one of these travels past Earth, the magnetic field within the ‘Coronal Mass Ejection’ (CME) can interact with the Earth’s magnetic field, leading to energetic plasma being accelerated into the Earth’s upper atmosphere at the poles, heating the atmosphere there (figure 5). Such heating stirs up the Earth’s atmosphere, causes it to expand and temporarily alters the composition in the upper atmosphere. While some attempts have been made to remove the impact of the heating caused by such events (e.g. Jarvis et al, 1998), the atmospheric composition is more complex to determine, especially in historical records where no direct spacecraft measurements could be made. Scott et al (2014) demonstrated that the annual variability of the ionospheric F-region in long-term records was consistent with changes to the thermospheric composition. A subsequent paper (Scott and Stamper, 2015) showed that these long-term trends varied with location in a way that mapped very closely to the observed trends in ionospheric height (Bremer, 2004), indicating that changes to the chemical composition of the upper atmosphere may be masking any effect due to climate change.

Figure 5: A schematic showing how energetic particles streaming into the Earth’s atmosphere at the Earth’s polar regions heat the upper atmosphere there, causing it to upwell and subsequently circulate around the globe, temporarily altering the chemical composition of the upper atmosphere.

While it is still possible that a consistent signature of climate change can be extracted from the global ionospheric records, there is much more careful analysis required in order to separate it from the impacts of space weather and global circulation.


Brasseur, Guy & Hitchman, Matthew, 1988: Stratospheric Response to Trace Gas Perturbations: Changes in Ozone and Temperature Distributions. Science, 240, 634-7, https://doi.org/10.1126/science.240.4852.634.

Bremer, J., 1992: Ionospheric trends in mid-latitudes as a possible indicator of the atmospheric greenhouse effect. Journal of Atmospheric and Terrestrial Physics, 54, 1505-1511, https://doi.org/10.1016/0021-9169(92)90157-G.

Bremer, J., 2004: Investigations of long-term trends in the ionosphere with world-wide ionosonde observations*, Advances in Radio ScienceKleinheubacher Berichte, 10.5194/ars-2-253-2004.

Jarvis, M. J., Jenkins, B., and Rodger, G. A., 1998: Southern hemisphere observations of a long-term decrease in F region altitude and thermospheric wind providing possible evidence for global thermospheric cooling, J. Geophys. Res., 103, 20774–20787, https://doi.org/10.1029/98JA01629.

Rishbeth, H., 1990: A Greenhouse effect in the ionosphere?, Planetary and Space Science, 38, 945-948, https://doi.org/10.1016/0032-0633(90)90061-T.

Rishbeth, H. & Roble, R.G., 1992: Cooling of the upper atmosphere by enhanced greenhouse gases – Modelling of thermospheric and ionospheric effects, Planetary and Space Science, 40, 1011-1026, https://doi.org/10.1016/0032-0633(92)90141-A.

Roble, R. & Dickinson, Robert, 1990: How will changes in carbon dioxide and methane modify the mean structure of the mesophere and thermosphere?, Geophysical Research Letters, 16, https://doi.org/10.1029/GL016i012p01441.

Rishbeth, H., 1999: Chances and changes: The detection of long-term change in the ionosphere, Trans. Amer. Geophys. Union (EOS), 80, 590 & 593.

Scott, C. J., Stamper, R., and Rishbeth, H., 2014: Long-term changes in thermospheric composition inferred from a spectral analysis of ionospheric F-region data, Ann. Geophys., 32, 113–119, https://doi.org/10.5194/angeo-32-113-2014.

Scott, C. J. and Stamper, R., 2015: Global variation in the long-term seasonal changes observed in ionospheric F region data, Ann. Geophys., 33, 449–455, https://doi.org/10.5194/angeo-33-449-2015

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Tropical Cyclone Precipitation In High Resolution Model

By: Benoit Vanniere

I’ll take the opportunity of this blog to present a result which continues to puzzle me and which I still haven’t found a full explanation for: why does tropical cyclone precipitation depend so little on the resolution of climate models?

This result was obtained by analysing the models of HighResMIP, a protocol that compares high-resolution global climate models, introduced in CMIP6. European research centres coordinated their contributions to HighResMIP in the Horizon2020 European project PRIMAVERA and performed simulations in the range of horizontal resolutions going from 1 to 1/4degree. This upper limit of ¼ degree was not the result of a random choice but was rather guided by feasibility and physical considerations. First, it matched the capability of an emerging class of GCMs, given model performance and CPU power, at the time the protocol was designed. Second, at resolutions finer than ¼ degree, atmospheric models enter a grey zone in which the assumptions used to parameterise atmospheric convection can be questioned.

In some cases, regional climate models may prove sufficient to assess the impact of resolution on physical processes and would represent a cheaper alternative to global models. However, to answer some other scientific questions, it is not possible to use regional climate models. For instance, if one wanted to evaluate the upscale effect of mesoscale processes and fine-scale air-sea interactions on the global circulation and teleconnections. To answer all those questions, we need global high-resolution models.

One example of such research topics is the interactions of Tropical cyclones (TC) with their large-scale environment:  what environmental factors control the interannual variability of TC frequency? how will it change in a warmer world? When starting this study, I was interested in the effect of the environment on TC precipitation.

When the horizontal resolution of a climate model is increased, TC become more intense, with deeper minimum sea-level pressure of TCs and stronger surface winds (e.g., Roberts et al., 2020). Because the latent heat released by precipitation plays a key role in TC intensification, we would also expect TC precipitation to reach larger values in high-resolution models.

Figure 1: (a) Distribution of TC minimum sea-level pressure, (b) precipitation averaged in a 1-degree cap and (c) precipitation averaged in a 5-degree cap, for five HighResMIP models (HadGEM3-GC31, ECMWF-IFS, EC-Earth3, CNRM-CM6-1 and CMCC-CM2). All quantities are 6-hourly. The low-resolution is represented by the solid curve and the high-resolution by the dashed curve.

We evaluate TC precipitation in five HighResMIP models, in which TCs have been systematically identified with the same tracking algorithm TRACK (Vannière et al., 2020). Precipitation was averaged, respectively, within a radius a 1-degree radial cap around the centre of the TC, to account for the inner core precipitation, and within a 5-degree radial cap, to account for the total TC precipitation. From Fig. 1b, HR models simulate larger precipitation rates in this inner region, as we anticipated.

More surprisingly, however, the climatological distribution of the total TC precipitation is in remarkable agreement between the low- and high-resolution configuration of a given model (Fig 1c), despite high-res models simulating more intense TCs (Fig 1a). In addition, the distribution of 5deg TC precipitation is much more sensitive to models’ formulation than to resolution.

Figure 2:  The azimuthally averaged moisture budget of the 200 strongest TCs in the Northern Hemisphere over the period [1950-2015] in (a) CNRM-CM6-1 and (b) EC-Earth3.

To better understand this, we computed the water budget of the composite of the 200 strongest TCs in each model (Fig. 2). At first order the main balance is between precipitation and moisture convergence. The evaporation flux at surface is a small term in comparison. When resolution increases, the bulk of precipitation and moisture convergence gets closer to the TC centre, but their contribution to the budget of the entire TC (i.e., the area below the curve) does not change much. Assuming the cyclone is axisymmetric, we define the radius of closure as the distance at which the radially integrated surface evaporation balances the TC precipitation. For the 5deg TC precipitation, we find a radius of closure of ~1500 km +/- 200km in the HighResMIP models. Those results are in line with those of Trenberth et al. (2007) who showed that the water budget of TCs Katrina and Ivan were closed at a radius of ~1600 km.  A radius of closure as large as 1500 km might seem very large considering that the largest surface evaporation occurs really close to the core. Although this large evaporation flux plays a crucial role in the dynamics of the storm by changing the specific humidity of an air parcel converging towards the centre of the storm and increasing the parcel’s convective available potential energy, it represents a negligible fraction of the total water budget!

Interestingly, we find that the radius of closure does not depend on model resolution. Hence, the moisture budget of a tropical cyclones is the result of a large-scale balance that low- and high-resolution models seem to represent equally well!

An implication of this result is that the details of physical processes in the inner core, does not play a major role on the climatological distribution of TC precipitation. Instead, this might be the signature of a mechanism acting at a scale larger than the inner core, which would be well captured by LR models. One potential candidate is the IR feedback, which was shown to participate in the intensification of tropical cyclones in some recent work (Ruppert et al. 2020). We can also speculate that this is the expression of a large-scale radiative constraint of TC convection: Jakob et al. (2019) showed that a significant fraction of the tropical atmosphere was in radiative convective equilibrium at time scales of 1 day and over areas comparable in size to the closure of the TC water budget.

Next steps? The analysis of the models of Horizon Europe project NextGEMS, comparing global storm resolving models, will allow us to test whether our results remain true at resolutions ~4km and when convection is resolved. The preparatory modelling campaign DYAMOND suggested this might be the case.


[1] Roberts, M., and Coauthors, 2020: Impact of model resolution on tropical cyclone simulation using the HighResMIP–PRIMAVERA multimodel ensemble. J. Climate. 33, 2557-2583, https://doi.org/10.1175/JCLI-D-19-0639.1

[2] Vannière, B., and Coauthors, 2020: The moisture budget of tropical cyclones in HighResMIP models: large-scale environmental balance and sensitivity to horizontal resolution. J. Climate 33, 8457-8474, https://doi.org/10.1175/JCLI-D-19-0999.1

[3] Ruppert, James H., and Coauthors, 2020: The critical role of cloud–infrared radiation feedback in tropical cyclone development. Proc. Natl. Acad. Sci. 117, 27884-27892, https://doi.org/10.1073/pnas.2013584117

[4] Trenberth, K., and Coauthors, 2007: Water and energy budgets of hurricanes: Case studies of Ivan and Katrina. J. Geophys. Res.: Atmos. 112, D23107,

[5] Jakob, C., and Coauthors, 2019: Radiative convective equilibrium and organized convection: An observational perspective. J. Geophys. Res.: Atmos. 124, 5418-543, https://doi.org/10.1029/2018JD030092


Posted in Climate, High-Resolution Climate modelling, Tropical cyclones, Water cycle | Tagged | Leave a comment

Climate Change 2021—The Physical Science Basis

By: Jonathan Gregory, Ed Hawkins, Matt Palmer

This document is a short summary of key points that are of current relevance to society from the physical science of climate change. It is based on the headline statements of the report published in 2021 by the Intergovernmental Panel on Climate Change (IPCC). The IPCC is a United Nations body responsible for providing impartial assessments of climate science. Its reports inform international negotiations on tackling climate change.

How and why has the climate changed?

Widespread and rapid changes have taken place in the atmosphere, in the ocean and on land. There is no doubt that the climate has warmed because human activities have increased the amounts of greenhouse gases in the atmosphere, especially CO2 (carbon dioxide) and CH4 (methane) (Fig. 1). Greenhouse gases warm the climate by making it harder for the atmosphere to radiate heat into the surrounding universe (space). They insulate the atmosphere against losing heat, rather like making it warmer inside a house by insulating the roof. The physics of this greenhouse effect has been understood for more than a century. The largest human-caused greenhouse effect on climate is due to the build-up of CO2, mostly from burning fossil fuels (coal, oil and gas).

Figure 1: Changes since the year 1 (of the Common Era, or AD) in the amount of CO2 in the atmosphere and in the global-average surface temperature. The industrial revolution and the burning of fossil fuels to produce energy began with the invention of the steam engine.

The warming causes changes in many other aspects of climate. The size and speed of recent changes in many aspects of the climate system exceed any seen for hundreds or thousands of years (Fig. 1). Many extreme events of weather and climate in every region across the globe have already become more severe or more frequent due to human-caused climate change (Fig. 2).

What climate change will happen in future?

Improved scientific knowledge has narrowed the range of warming expected as a result of increasing the amount of CO2 in the atmosphere.

Global-average surface temperature will continue to get warmer until at least the 2050s. Before 2100 it will be over 2°C warmer than it was before the industrial revolution, unless human-caused emissions of CO2 and other greenhouse gases into the atmosphere are greatly reduced in the coming decades.

Many aspects of climate change will grow larger as global-average temperature becomes warmer. Heatwaves, heavy rainfall and droughts will become more severe or more frequent (Fig. 2). Arctic sea ice, snow cover and permafrost will decrease.

Figure 2: This diagram depicts how much more often extremely high temperatures occur as the climate gets warmer. As an example, it considers the temperature which was reached only once in 50 years on average at any given place in the climate before the industrial revolution. This extreme happens more frequently in today’s climate, and will become still more common in warmer climates.

Rainfall over land will become more variable as global-average surface temperature becomes warmer. Wet and dry episodes will both become more severe. Rainfall will increase on average in monsoons and at high latitudes, but decrease in some regions at low latitudes.

If CO2 emissions increase, a larger proportion of the extra CO2 will remain in the atmosphere. This is because the proportion absorbed by the ocean, plants and soil will decline.

It would take hundreds to thousands of years to reverse many of the changes due to past and future greenhouse gas emissions. This especially applies to changes in the ocean, sea level and the ice sheets of Greenland and Antarctica. For some changes, there could be “no going back” in practice, because they would take far longer to reverse than the timescales relevant to society.

Information relevant to assessing the risks from climate change and adapting to its effects

Normal variations in climate between years and decades will continue on top of human-caused climate change. These variations are important to consider in planning for the full range of possible changes, especially on regional scales in the near future. However, they have little effect on long-term trends.

Every region will experience impactful changes in climate and weather as global-average surface temperature gets warmer, such as more frequent or severe extreme events. These effects will be worse because impactful events of more than one type will occur together increasingly often. The greater the warming, the greater and more widespread the changes.

In assessing the risks of climate change, it is important to consider some even larger changes which we cannot rule out. These include collapse of ice sheets, abrupt changes in ocean currents, combinations of extreme events, and very large increases in global-average surface temperature.

Information relevant to limiting future climate change

To limit human-caused global warming, net CO2 emissions must be reduced to zero or below. Large reductions in emissions of other greenhouse gases are required as well. “Net zero emissions” of CO2 over a given period means that the amount of CO2 removed from the atmosphere during the period is equal to the amount added to the atmosphere during the period. Net negative emissions means that more is removed than added, so the amount in the atmosphere goes down.

Air pollution would reduce and air quality would improve within a few years after large reductions in emissions of greenhouse gases. We would see differences in trends of global-average surface warming after about 20 years. It would take longer for differences to become apparent in other impactful aspects of climate change.


IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [MassonDelmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press. https://www.ipcc.ch/report/ar6/wg1/#SPM

Posted in Climate, Climate change, Climate modelling, Environmental physics, Greenhouse gases, IPCC | Leave a comment

Where Do All My Balloons Go?

By: Andrew K. Mirza

Turbulence! If you have ever travelled by aeroplane, then you may have experienced atmospheric turbulence during the flight: It is when the captain switches on the seat-belt sign; requests all passengers return to their seats and buckle up; the flight attendants pause their dispensing of refreshments and stow away their trollies.  Then the aeroplane encounters turbulence that may range from a slight judder to something more intense.  As quick as it is encountered it passes.  The seat belt sign is switched off, the flight attendants resume their duties, and you are free to roam the cabin; the event is reduced to a passing remark such as “… it was a bumpy ride …” But what you experienced, however briefly, may have important implications for climate change.  Not so much because of the flight you’ve taken but in a more subtle way of accounting for how much of the greenhouse gases in the atmosphere can be attributed to a particular country.

This requires not only the simulation of the atmosphere but also the subsequent dispersion of greenhouse gases emitted from various processes of nature and human activity.  The DARE-UK project aims to detect and measure regional greenhouse gas emissions and estimate the fraction of those measurements that can be attributed to the UK.  Thus sophisticated computer models have been developed that can model dispersion which, when coupled with weather forecast models, aim to estimate where, when and how much of the greenhouse gases have come from or come to the UK.

One such computer model is called the Numerical Atmospheric dispersion ModEl – NAME. This model was originally developed by the Met Office in response to the Chernobyl nuclear accident and has since been put to use in many other applications: air quality forecasts,  emergency response to accidental chemical releases, and it is routinely used to model the dispersion of volcanic ash, e.g., the eruption of the Icelandic volcano Eyjafjallajökull.  NAME models the dispersion by releasing virtual particles within a virtual atmosphere, such as a weather forecasting model.

If you imagine these virtual particles as being balloons; hundreds or thousands of them are released from your garden every day.  Each balloon carries a package representing a fraction of your daily contribution to the global emissions of greenhouse gases.  They ascend and travel wherever the weather forecast modelled wind takes them.  Some will only travel a short distance while others may travel a long way.  However, whether your balloons travel a short or long distance will depend on atmospheric turbulence.  In particular, whether turbulence pushes your balloon through the interface between the boundary layer and the free atmosphere or causes it to bounce back downwards.  The boundary layer is the lower part of the atmosphere that is subject to friction due to the earth’s surface whereas the free atmosphere is the upper part of the atmosphere where surface friction has a very small effect.  Therefore, how we represent turbulence in the dispersion model is important.

Currently in NAME turbulence in the free atmosphere is assumed to have a fixed intensity that does not vary in space or time.  But we know from our experience in an aircraft this is not the case, turbulence varies in where it happens when it happens and in its intensity.  So my contribution to the DARE-UK project is to investigate and evaluate how varying the representation of free atmosphere turbulence in space and time may impact atmospheric dispersion.

Figure 1: Shows two plots which are vertical slices from near the surface up to 3~km. Figure 1(a) shows the current scheme for representing turbulence in the free atmosphere (light blue regions) which uses a fixed value whereas figure 1(b) shows the new scheme (yellows and pale blues) which varies according to how the wind speed changes in space and time.  Both models use the same scheme for turbulence in the boundary layer (dark blues).

Figure 2: Shows a horizontal slice just above the surface.  It is clear from these two figures that the turbulence due to the new scheme varies in space and intensity (yellow is low, pale blue is moderate).

Figure 3: shows the averaged difference between the fixed and variable schemes during a day, showing how the turbulence using the new scheme not only varies in time but also how its intensity evolves (areas in red).  The intensity will determine whether your balloon remains within the boundary layer, and is likely to remain in the UK, or it passes into the free atmosphere and is likely to travel beyond the UK.

My work now is to identify what impact this space-time varying turbulence has on the dispersion of your balloons, i.e., what weather regimes enhance or diminish turbulence in the free atmosphere.  To do this a tracer gas, Radon, will be attached to the balloons and allowed to disperse within the model atmosphere.  At the end of the modelling period, we compare the amount of Radon that reaches the monitoring sites that measure actual Radon.  This comparison will help us understand whether our new variable turbulence scheme is an improvement over the fixed turbulence scheme. If not then we can investigate why there is no improvement.  Knowing this will help us understand where your contribution to greenhouse gases ends up, i.e., whether your balloons stay mostly in the UK or travel to Europe, and, of course, vice-versa.  So that bumpy ride in the aircraft may help us to understand more clearly where all your balloons go.

If you’d like more information about the DARE-UK Project why not visit their website.


Brown, A., S. Milton, M. Cullen, B. Golding, J. Mitchell and A. Shelly, 2012: Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey. Bulletin of the American Meteorological Society 93, 12, 1865-1877, https://doi.org/10.1175/BAMS-D-12-00018.1.

Dacre, H. F., A. L. M. Grant, N. J. Harvey, D. J. Thomson, H. N. Webster and F. Marenco, 2015: Volcanic ash layer depth: Processes and mechanisms, Geophys. Res. Lett.42, 637–645, https://doi.org/10.1002/2014GL062454.

Jones, A., D. Thomson, M. Hort and B. Devenish, 2007: The U.K. Met Office’s Next-Generation Atmospheric Dispersion Model, NAME III. In Air Pollution Modeling and its Application XVII (Proceedings of the 27th NATO/CCMS International Technical Meeting on Air Pollution Modelling and its Application) (pp. 580–589). Springer. https://doi.org/10.1007/978-0-387-68854-1_62, (https://www.researchgate.net/publication/226303857_The_UK_Met_Office%27s_Next-Generation_Atmospheric_Dispersion_Model_NAME_III).

Manning, A. J., D. B. Ryall, R. G. Derwent, P. G. Simmonds and S. O’Doherty, 2003: Estimating European emissions of ozone-depleting and greenhouse gases using observations and a modeling back-attribution technique, J. Geophys. Res.108, 4405, D14, https://doi.org/10.1029/2002JD002312.

Thomson, D. J., W. L. Physick and R. H. Maryon, 1997: Treatment of Interfaces in Random Walk Dispersion Models. Journal of Applied Meteorology, 36, 9, 1284-1295, https://doi.org/10.1175/1520-0450(1997)036<1284:TOIIRW>2.0.CO;2.

Wada, A. and Coauthors, 2013: Quantification of emission estimates of CO2, CH4 and CO for East Asia derived from atmospheric radon-222 measurements over the western North Pacific, Tellus B: Chemical and Physical Meteorology65,1, https://doi.org/10.3402/tellusb.v65i0.18037.

White, E. D. and Co-authors, 2019: Quantifying the UK’s carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network, Atmos. Chem. Phys.19, 4345–4365, https://doi.org/10.5194/acp-19-4345-2019.

Posted in Atmospheric dispersion, Boundary layer, Greenhouse gases, Numerical modelling, Turbulence, Wind | Leave a comment

Why should we care about sea ice floes?

By: Adam Bateson

One of the most frequently used visual devices to illustrate climate change is that of a polar bear on sea ice surrounded by open ocean (Fig. 1). Polar bears are today identified as a vulnerable species, with sea ice decline the primary reason for this assessment. This is one of several reasons why we need to understand how the Arctic sea ice cover is likely to change in future. Sea ice is also of relevance to meteorology and climatology since the sea ice cover acts as a barrier to the exchange of heat, moisture, and momentum at high latitudes. Furthermore, the retreat of sea ice is a positive feedback in our climate system. Sea ice reflects over 50% of the incident solar radiation (snow-covered sea ice can reflect 90% of this radiation) whereas open ocean reflects only about 6%. The replacement of sea ice cover with open ocean therefore enhances any warming effect. This effect is referred to as the ice-ocean albedo feedback (or the ice-albedo feedback, a more general term).

Figure 1: Polar bears have been described as the ‘poster animal’ of climate change. Photo credit: Hans-Jurgen Mager at Unsplash.com.

It is therefore important to ensure that we have reasonable confidence in how the sea ice is likely to change in the future. However, a recent analysis of climate model performance in simulating the Arctic sea ice found that models generally underestimated the sensitivity of September sea ice area to a given amount of global warming (Notz et al., 2020). Why are climate models failing to adequately capture the sensitivity of sea ice area to warming? This could be due to an inadequate representation of the model forcing that drives changes in the sea ice cover e.g. wind speed, surface temperature. Alternatively, or in addition, it could be due to missing or poorly captured sea ice physics.

My own research focuses on improving the representation of sea ice physics within models of sea ice. Sea ice and its interaction with the ocean and atmosphere is complicated (Fig. 2). It is not possible to accurately represent all processes whilst maintaining computational efficiency within sea ice and climate models. Instead, we must identify and develop aspects of the model physics that will best enable us to answer research questions of interest.

Figure 2: A cartoon schematic to illustrate several important processes that determine the evolution of sea ice and its interactions with the atmosphere and ocean. Note this figure just gives an example of the complexity of the sea ice-ocean-atmosphere system and is far from exclusive in highlighting important physical processes. Figure is a reproduction of Fig. 2 from Lee et al. (2012).

Sea ice is made up of individual pieces of ice that we call floes. Observations of these floes show that they can range in size from scales of just metres to tens of kilometres (Fig. 3). However, sea ice models have historically assumed floes adopt a constant size if they explicitly consider the size of individual floes at all. The size distribution of a set of floes is generally referred to as the Floe Size Distribution, or FSD. Floe size can impact several processes within sea ice including the volume of melt from the side of floes (lateral melt) and momentum exchange between the sea ice and the atmosphere and ocean. A recent study found that sea ice extent can reduce by about 20% for a distribution of floes with a diameter of 3 m compared to floes with a diameter of 300 m (the standard value used in the Los Alamos Sea Ice model, more commonly referred to as CICE) just by accounting for the impact of floe size on lateral melt rate (Smith et al., 2022). This therefore makes the improved representation of floe size a potential target to improve sea ice model performance.

Figure 3: Sea ice is made up of individual pieces of ice called floes. These floes can vary in size from just metres to tens of kilometres. Figure is a reproduction of Fig. 3 from Williams et al. (2016) under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/us/).

There are several processes thought to influence floe size including lateral melting, wave break-up, brittle fracture, and floes welding together. Several recent studies have proposed models of the FSD: some prioritise physical fidelity by allowing the shape of the FSD to emerge from the model (e.g. Roach et al., 2018, 2019); whereas others prioritise computational efficiency and make assumptions about FSD shape (e.g. Bateson et al., 2020). A recently submitted study compared these different approaches to modelling the FSD and considered the advantages of disadvantages of each (Bateson et al., 2021). In practice, the better approach depends on both the research question at hand and the resources available. To return to the research question under consideration here, the study found that, when optimised against observations of floe size, neither FSD model produced a significant improvement in simulating sea ice area. However, this study also highlighted several limitations in this conclusion such as impacts of floe size not considered in the study e.g. on the rheology of sea ice.

It is unlikely that there is going to be a single ‘silver bullet’ to improve the representation of Arctic sea ice in climate models. Progress will be incremental as we improve our understanding of relevant physical processes in sea ice and better capture atmospheric and oceanic forcing of sea ice.


Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K. and Aksenov, Y, 2020: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020.

Bateson, A. W., Feltham, D. L., Schröder, D., Wang, Y., Hwang, B., Ridley, J. K., and Aksenov, Y, 2021: Sea ice floe size: its impact on pan-Arctic and local ice mass, and required model complexity, preprint, The Cryosphere Discuss.https://doi.org/10.5194/tc-2021-217 (in review).

Lee, C. M., and Coauthors, 2012: Marginal Ice Zone (MIZ) Program: Science and Experiment Plan, APL-UW 1201 October 2012, 51 pp, https://apl.uw.edu/research/downloads/publications/tr_1201.pdf

Notz, D., and Coauthors, 2020: Arctic sea ice in CMIP6, Geophys. Res. Lett., 47, https://doi.org/10.1029/2019GL086749.

Roach, L. A., Horvat, C., Dean, S. M. and Bitz, C. M, 2018: An Emergent Sea Ice Floe Size Distribution in a Global Coupled Ocean-Sea Ice Model, J. Geophys. Res. Ocean., 123, 4322-4337, https://doi.org/10.1029/2017JC013692.

Roach, L. A., Bitz, C. M., Horvat, C. and Dean, S. M., 2019: Advances in Modeling Interactions Between Sea Ice and Ocean Surface Waves, J. Adv. Model. Earth Syst., 11, 4167–4181, https://doi.org/10.1029/2019MS001836.

Smith, M. M., Holland, M., and Light, B, 2022: Arctic sea ice sensitivity to lateral melting representation in a coupled climate model, The Cryosphere, 16, 419–434, https://doi.org/10.5194/tc-16-419-2022.

Williams, G. D., and Coauthors, 2016: Drones in a cold climate, Eos, 97, 1-6,  https://doi.org/10.1029/2016eo043673.

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

Atmospheric CO2, fossil fuel emissions, Shared Socioeconomic Pathways and the Paris Agreement.

By: Tristan Quaife

The Paris Agreement, which is signed by 193 countries belonging to the United Nations Framework Convention on Climate Change, aims to limit the rise in global mean temperature to 2°C, and ideally 1.5°C, above pre-industrial levels. To achieve either of these goals will require a significant slowdown in the rate of increase of the concentration of greenhouse gases in the atmosphere. In particular, this means reducing carbon dioxide (CO2) emissions caused by burning fossil fuels, the production of cement and land use changes such as forest clearance. There are also methods proposed for removing CO2 from the atmosphere, such as increasing tree planting, but it is highly unlikely that we can meet the Paris targets without also making significant reductions in our emissions. This blog post explains some of the context.

The current atmospheric carbon dioxide concentration is around 420 parts per million (ppm), compared to a preindustrial level of around 280ppm. Since the late 1950’s there have been regular, high-quality measurements made of atmospheric carbon dioxide concentration at the Mauna Loa Observatory in Hawaii. A plot of these data is shown in Figure 1. By comparison with other observations taken around the world we know that the Mauna Loa record is representative of the global concentration. Note that at the start of the time series the data are not far off the pre-industrial level, hence the majority of the increase takes place in the last 60 years of human history.

Scientists also have a good understanding of the sinks and sources of this atmospheric carbon. There are reliable estimates of emissions from fossil fuel burning and cement production which are acquired from a variety of sources, including economic data. Emissions from land use change are far less certain, but represent a smaller, albeit still significant contribution. We also have a high level of confidence from different observational data sets and modelling studies, of how much carbon is being taken up by the oceans and land.

Figure 1: The Keeling curve; atmospheric CO2 concentration measured at Muana Loa, Hawaii. Image produced by the Scripps CO2 programme

Climate models are a key tool for helping us interpret what the likely impact increased CO2 levels will have on global temperatures, and what the onward impacts of that will be (such as, for example, the associated increase in sea level rise due to melting of ice sheets). In a recent study, led by Ranjini Swaminathan from the University of Reading and the UK National Centre for Earth Observation, we examined the data from a large number of climate models, run under different socio-economic scenarios (the “Shared Socio-economic Pathways” or SSPs for short) to see in which years different global temperature thresholds would be crossed by the models. The results are shown in Figure 2. It’s a complex plot but contains lots of useful information. The temperature thresholds are on the y-axis, and the different SSPs are indicated by the different coloured bars. Each dot represents the year at which an individual climate model passes the corresponding temperature threshold for a given SSP. The white boxes show the median year of threshold exceedance based on all the models running that scenario. The column on the right explains how many models (if any) do not exceed the threshold.

Figure 2: Year of threshold exceedance for CMIP6 models (individual dots), for different Shared Socioeconomic Pathways (indicated by colours). The number of models that stay below the given threshold is shown on the right-hand side. Taken from Swaninathan et al. (2022).

Focusing on the 1.5°C and 2°C thresholds, we can see some interesting outcomes of this exercise:

  1. Under all SSP scenarios we tested there are some models that exceed 2°C.
  2. The majority of models predict we will exceed 1.5°C even under SSP1-1.9.
  3. It is only SSP1-1.9 where the majority of models don’t exceed 2°C.
  4. Other than for the two SSP1 scenarios all models exceed 2°C.

The significance of this is that the SSP1 scenarios, which carry the subtitle “Sustainability – Taking the Green Road”, all represent significant reductions in fossil fuel emissions, a transition toward greater use of renewables and rapid increases in technology for mitigation and adaptation. SSP1-1.9 is the most optimistic of all scenarios and sees the world moving to net-zero carbon emissions by mid-century.  There is an excellent explainer of the different SSPs here if you want to read more here.

An important question to ask, then, is where our knowledge of the actual CO2 concentration from the Mauna Loa observations, and other data, puts us in relation to the different SSP scenarios. This is illustrated in Figure 3, which shows the CO2 concentrations corresponding to the different SSPs with the Mauna Loa CO2 observations overlayed. The observation data are the same as in Figure 1, but with the seasonal cycle removed to make the comparison with the SSPs clearer. In one sense, there is not much to see here – the SSPs start in 2015, and so they haven’t diverged greatly yet and the CO2 observations sit nicely on top. But this means we are still in control of whether or not we take the route implied by SSP1-1.9, and have a fighting chance of avoiding a 2°C rise (and maybe even a 1.5°C rise). But if we want to do this so we must reduce the rate of emissions quickly over the next 30 years.   

Figure 3: Atmospheric CO2 concentrations for the different SSPs and from the Muana Loa observations. SSP data from Meinshausen et al. (2020) and observational data from here.


Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J. 2020: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev.13, 3571–3605,
Swaminathan, R., Parker, R. J., Jones, C. G., Allan, R. P., Quaife, T., Kelley, D. I., de Mora, L., and Walton, J. 2022: The Physical Climate at Global Warming Thresholds as Seen in the U.K. Earth System Model, Journal of Climate35(1), 29-48.

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How To Find A Planet

By Jochen Broecker

To make this clear straight away: this entry will only marginally touch upon weather and climate, but it will not be entirely unrelated altogether. Since you are reading this blog you must be interested in the natural sciences in general, and this makes it likely that you are (or have been at some point in your life) interested in stars and planets. You will know the difference between stars and planets. You will also know that that some of our planets were known already in antiquity whist others were not, but rather were discovered only after “science” took hold in the 18th century. These were Uranus and Neptune (and Pluto if you count it as a planet), while Mercury, Venus, Mars, Jupiter, and Saturn were known to all civilisations with a recorded history. They appear, for instance, in what is known as the “mul.apin”, an astronomical compendium compiled around 1000BCE in Babylonia.

But what exactly do we mean by “known”? It is probably safe to assume that already in the paleolithic age people were aware of the fact that while most stars appear to have a fixed position relative to one another, some travel across the sky, faster or slower, in a stately progression along a relatively narrow path which the sun and the moon also take. Clearly, the key insight though is that these are actually just a few stars that reappear periodically. As a more specific example, that the morning star and the evening star are in fact the same object is a result which for me ranks high on the list of important scientific achievements. It bears all the hallmarks of great science: it relies on careful collection of long-term observations, requires a certain level of abstraction and creativity, and lacks any immediate practical use. This last point is important because it says something about the society if it sees value in such results nonetheless. Identifying the five classical planets may thus be seen as the starting point of astronomy as a science, or even science as a whole.

One way to appreciate scientific progress is to try and retrace the steps of the scientists. Clearly, this is not always feasible especially with regards to more recent scientific breakthroughs like nuclear fission or the Higgs boson (one might also easily forget that for each step science takes in the right direction there correspond numerous turns in the wrong direction). But with regards to early astronomy, nothing but clear skies and the naked eye are needed (and here is one overlap with meteorology). With a reasonably good set of binoculars, we are already in the same position as Galileo.

As with so many other things, I came to realise this through talking to my children. In late December we went through a period with Jupiter and Saturn standing low in the west just after the (early) sunset. For a year or so they had been fairly close to one another, so their relative movement became apparent. They also provided a point of reference for the Moon and for realising the big leaps the Moon makes across the sky from night to night. There’s a lot to explain and make sense of here.

Figure 1: An elaborate geometric construction explaining the apparent motion of Mercury from Earth by Ibn Al-Shatir (1304-1375). Similar epicycles were already present in Ptolemy’s model but Al-Shatir’s model had far better predictive power. Epicycles still appear in Copernicus’ heliocentric model and were rendered obsolete only by Kepler’s laws.

And that’s when I found that I had seen only four of the five planets in my life so far, with Mercury being the missing one. If you would like to try this yourself, use an interactive star map on the web such as stellarium-web.org. (Unfortunately we are entering a few months where only Jupiter can be seen in the first half of the night; but Venus currently appears at around six o’clock in the morning and can be seen even during the day if you know where to look.) Mercury is surprisingly hard to observe as it always stays close to the Sun. As it is usually completely drowned by the glare of the sun, it can only be observed during a short period either just after sunset or just before sunrise. Light pollution strongly affects the sky close to the horizon which makes the problem even harder.

But it is possible even in a place like Reading. Here’s what I did. Firstly, a small pair of binoculars will help. Find a place with a clear view of the western horizon where the sun sets; a high building is good since even trees or houses might obstruct the relevant area in the sky. Now two things have to come together: Mercury has to be maximally far away from the sun, and the western horizon has to be clear of any clouds. The first condition will be met again at the end of April this year. The second condition is fairly hard to predict even on the day. The skies might have been the bluest of blue during the day, yet even a small puff of steam in the wrong place might spoil it, since once the sun is down, atmospheric boundary layer activity will come to a rest, meaning that the cloud might remain there for a very long time (here’s another overlap with meteorology). I was unlucky on two days but on the third I was successful (I now know how it feels when the “launch window” of a space mission starts to close). Deep on the western horizon, which still had a tint of orange, I saw a bright white spot, brighter and whiter than Saturn which was close by. Mercury is more or less like a supersized version of our Moon; I can confirm that the light it reflects is very similar.

Having ticked off the classical planets, the next one on my list is Uranus. It is visible to the naked eye (without light pollution) and had made its way into star catalogues long before W. Herschel peeked at it through his telescope. Yet when I said earlier that “knowing” an object in the sky does not mean we recognise it as a planet, Uranus is a case in point. Uranus is a faint spot that moves very slowly in front of an ocean of other faint spots. Even Herschel did not recognise Uranus’ motion. Thanks to his magnificent homemade telescope, he realised that it was an extended disc, whilst stars appear as spots even in modern telescopes. The insight that it was actually a planet came only after more astronomers started looking at it. So I don’t expect to see more than a faint spot, and if it was only for my own skills and observations, rather than for the insights of our forefathers, I’d never know that Uranus is a planet, nor even what a planet is, probably.

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