Co-Producing New Sub-Seasonal Weather Forecasts in Africa

By: Linda Hirons

Weather-related extremes affect the lives and livelihoods of millions of people across tropical Africa. Access to reliable, actionable weather information is key to improving the resilience of African populations and economies. Specifically, at the extended sub-seasonal timescale (forecasts of 1-4 weeks ahead), improved weather information could be transformational in building better early warning systems for the extreme events which cause infrastructural and societal damage. However, the uptake and availability of accurate weather information and services on these extended timescales remain very low across the continent.

Recent scientific advances have improved our understanding of what drives changes in weather on these timescales (e.g., the Madden Julian Oscillation (MJO); Zaitchik 2017) and subsequent modelling advances have enabled us to better represent these drivers (e.g., Vitart et al 2017) and their local impacts across Africa (e.g., de Andrade et al 2021). While these scientific and modelling advances are necessary to improve forecasts it is becoming increasingly clear that they are not sufficient to translate advances in knowledge into real tangible societal benefits. This requires a more collaborative and iterative approach where knowledge from scientists is combined with knowledge from local forecasters and knowledge from the specific decision-making context of forecast users to jointly co-produce (e.g., Vincent et al 2018) bespoke weather and climate services which can be truly effective.

Figure 1: The building blocks (a) and principles of good co-production (b) introduced in Carter et al. (2019) 

Through a Real-Time Pilot Initiative of the WMO Sub-seasonal to Seasonal Prediction Project, the GCRF African-SWIFT and ForPAc projects ran a two-year, sub-seasonal forecasting testbed (Hirons et al 2021) – a forum where prototype forecast products were co-produced and operationally trialled in real-time. Launched in November 2019 in Kenya, the testbed brought together national meteorological services, universities and forecast users from across tropical Africa, to use a co-production approach (Figure 1; Carter et al 2019) to improve the appropriate use of sub-seasonal forecasts. This testbed made real-time, sub-seasonal forecast data from the European Centre for Medium-Range Weather Forecasts (ECMWF) available to users in a range of sectors, including energy, health, agriculture, disaster risk reduction and food security across tropical Africa.

The sub-seasonal testbed has been providing co-produced, tailored forecast products and advisories to weather-sensitive sectors across Africa (Hirons et al 2021). Examples here from users in the energy sector in Kenya and the health sector across the Sahel exemplify the local application and benefits of new testbed forecast products.

In Kenya, sub-seasonal forecasts co-produced by the Kenya Meteorological Department and the Kenya Electricity Generating Company (KenGEN), which is responsible for supplying more than 70% of Kenya’s electricity, supported improved hydropower planning. Hydropower accounts for approximately 45% of KenGEN’s total supply and fills the gaps when other sources like solar or wind are unreliable. It uses fast-moving water to produce electricity so Kenya relies on key dams for sufficient water storage. Previously dam levels would have been systematically lowered before the start of the rainy season in anticipation of significant rainfall. However, if rains failed, drought could cause considerable interruptions to the power supply and increase reliance on diesel generators. Through the Testbed KenGEN has been incorporating the sub-seasonal rainfall information into their dam management decisions enabling them to maximise dam levels without overflowing and causing downstream flooding. During the Testbed Kenya has experienced uninterrupted power, even through periods of drought, and has eliminated emergency diesel generators from the national electricity grid entirely.

Figure 2: Example of the vigilance map for the emergence of meningitis outbreaks in Africa co-produced with GCRF African SWIFT project and WHO.

Across the Sahel GCRF African SWIFT researchers and forecast producers have been working closely with the World Health Organisation (WHO) to supply bespoke, multi-variable sub-seasonal forecast information for meningitis vigilance across 26 countries in the meningitis belt. It is well known that meningitis outbreaks are more likely in warm, dry conditions, particularly after dust events. Previously the observed environmental conditions were used to determine the likelihood of outbreaks. However, by combining forecasts of temperature, relative humidity and wind speed and direction with dust forecasts, the sub-seasonal testbed has extended the lead time of the existing vigilance maps by up to 2 weeks (Figure 2). Working closely with the WHO has shown that this information has huge implications for improving preparedness action and making timely life-saving interventions to prevent outbreaks.

The GCRF African SWIFT sub-seasonal testbed is coming to an end this year and the focus will be on ensuring that the knowledge co-produced through these collaborative partnerships can be institutionalised and become part of in-country standard operational procedure to ensure project-initiated services are sustained. However, continuing to provide these new services requires national meteorological agencies in Africa to continue to have access to sub-seasonal data in real-time. Surely these direct and tangible societal benefits are enough to convince data providers?


Carter, S., Steynor, A., Waagsaether, K., Vincent, K., Visman, E., 2019. Co-production of African weather and climate services. Manual, Cape Town: SouthSouthNorth.

de Andrade, F. M., Young, M. P., MacLeod, D., Hirons, L. C.Woolnough, S. J. and Black, E. (2021) Subseasonal precipitation prediction for Africa: forecast evaluation and sources of predictability. Weather and Forecasting, 36 (1). pp. 265-284. ISSN 0882-8156 doi:

Hirons L., Thompson, E., Dione, C., Indasi, V.S., et al. Using co-production to improve the appropriate use of sub-seasonal forecasts in Africa. Climate Services, 23. 100246. ISSN 2405-8807 (2021)

Vincent, K., Daly, M., Scannell, C., Leathes, B., 2018. What can climate services learn from theory and practice of co-production? Climate Services. 12, 48-58.

Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,  2017. The sub‐seasonal to seasonal prediction (S2S) project database. Bull. Am. Meteorol. Soc. 98, 163–173

Zaitchik, B.F., 2017. Madden-Jullian Oscillation impacts on tropical African precipitation. Atmospheric Research.184, 88-102.

Posted in Africa, Climate, Co-production, drought, Energy meteorology, Forecasting Testbed, Madden-Julian Oscillation (MJO), Predictability, Rainfall, Renewable energy, Seasonal forecasting, subseasonal forecasting, Tropical convection, Weather forecasting | Leave a comment

Are There Climate Consequences of Using Hydrogen as a Replacement for Coal, Gas and Oil?

By: Keith Shine

There are many possible avenues to reduce carbon dioxide emissions. One of these is a shift to using hydrogen (H2) as a fuel source; it could potentially be used for many current CO2-emitting activities, including industry, heating in the home and transport. There would be many challenges, but it is widely regarded as one component of pathways to reach “net zero”, which aims to stabilise human-induced climate change. A recent Royal Society briefing provides much information on the technological and economic challenges of a move to a hydrogen economy.

As with all potential climate-change solutions, it is necessary to assess their environmental impact. I played a small role in a modelling study on the “Atmospheric Implications of Increased Hydrogen Use”, led by the University of Cambridge (Warwick et al., 2022), funded by the Government’s Department for Business, Energy & Industrial Strategy. Studies of hydrogen’s climate impact go back about 20 years (e.g., Derwent et al. 2006; Schulz et al. 2003; Warwick et al.  2004) but there is now more urgency in understanding the issues (e.g., Derwent et al., 2020; Paulot et al., 2021).

The first issue is how hydrogen is generated. The “feedstock” is simply water. But it takes energy to split hydrogen from water, and it matters where that energy comes from.  There are two low carbon methods. So-called “blue hydrogen” is generated using fossil fuels, but the CO2 produced is captured and stored rather than emitted into the atmosphere.  “Green hydrogen” is generated using renewable energy sources. My focus is the impact of any hydrogen leakage during production, storage and distribution (e.g., from pipework and valves). (The use of the hydrogen just leads to the generation of water.)

Hydrogen itself is of little direct concern, from a climate point of view, although it can impact air quality; the Cambridge study focused on hydrogen’s role in altering the chemistry of the atmosphere, thereby changing concentrations of gases that can influence climate.

A major route to climate impact is via changes in concentrations of a very reactive molecule, the hydroxyl radical (OH), a gas present in tiny quantities but which plays a key role in atmospheric chemistry. It is sometimes referred to as an “atmospheric detergent” as it hastens the removal of many atmospheric pollutants. Leakage of hydrogen reduces OH concentrations, so reducing this cleansing capacity.

The effects of both the hydrogen itself and its impact on OH include increased concentrations of methane, tropospheric ozone and stratospheric water vapour; all these lead to climate warming. It is important to quantify these impacts, and identify uncertainties, to be clear that the climate advantages of reduced CO2 emissions far outweigh the impacts of increased hydrogen use.

My involvement in the Cambridge study was to help quantify the 100-year Global Warming Potential (GWP(100)), a metric to characterise the climate impact of emissions of a gas (relative to the emission of an equal mass of carbon dioxide). GWP(100) is just one possible metric to quantify climate impacts of emissions and in itself is quite contentious: see this blog post by my colleague Bill Collins. But contentious or not, it is widely used in policy applications, including national and international policy agreements.

Warwick et al. (2022) concluded that hydrogen’s GWP(100) was 11±5; about half came from its impact on methane and about a quarter each came from its impact on tropospheric ozone and stratospheric water vapour (some of which was due to a knock-on effect of methane changes).  Clearly uncertainties are substantial, one of which is the atmospheric lifetime of hydrogen which is believed to be 2 to 3 years. As noted above, it is removed by reaction with OH but it is also removed by reactions with soil; the strength of this “soil sink” is particularly uncertain.

So hydrogen leakage does have a higher climate impact (as measured by GWP(100)) than CO2 per kg emitted.  However, hydrogen emissions would be much smaller than the CO2 emissions that they would replace. For one illustrative future scenario, Warwick et al. (2022) estimate that hydrogen’s climate impact would be around 0.4 to 4% (for hydrogen leakage rates of 1 to 10% respectively) of the avoided “CO2-equivalent” emissions. This is all promising but nevertheless there can be no complacency. Leakage rates must be minimised. Remaining uncertainties in quantifying the climate impact must be reduced. The Natural Environment Research Council recently announced a funding opportunity “Environmental response to hydrogen emissions” to help reduce uncertainties.

Electric Car: BMW I Hydrogen Fuel Cell version of the X5 SUV (photo Marco Verch, Creative Commons 2.0)


Derwent, R., P. Simmonds, P., S. O’Doherty, A. Manning, W. Collins, and D. Stevenson, D 2006: Global environmental impacts of the hydrogen economy. International Journal of Nuclear Hydrogen Production and Applications, 1, 57-67 10.1504/IJNHPA.2006.009869

Derwent, R. G., D. S. Stevenson, S. R. Utembe, M. E. Jenkin, A. H. Khan, and D. E. Shallcross, 2020: Global modelling studies of hydrogen and its isotopomers using STOCHEM-CRI: Likely radiative forcing consequences of a future hydrogen economy. International Journal of Hydrogen Energy, 45, 9211-9221. 10.1016/j.ijhydene.2020.01.125

Paulot, F., D. Paynter, V. Naik, S. Malyshev, R. Menzel, and L. W. Horowitz, 2021: Global modeling of hydrogen using GFDL-AM4.1: Sensitivity of soil removal and radiative forcing. International Journal of Hydrogen Energy, 46, 13446-13460. 10.1016/j.ijhydene.2021.01.088

Schulz, M.G., T. Diehl, G.P. Brasseur, and W. Zittel, 2003: Air Pollution and Climate-Forcing Impacts of a Global Hydrogen Economy. Science, 302, 624-627, DOI: 10.1126/science.1089527

Warwick, N. J., S. Bekki, E. G. Nisbet, and J. A. Pyle, 2004: Impact of a hydrogen economy on the stratosphere and troposphere studied in a 2-D model. Geophysical Research Letters, 31. 10.1029/2003gl019224

Warwick, N., P. Griffiths. J. Keeble, A. Archibald, J. Pyle and K. Shine, 2022 Atmospheric implications of increased hydrogen use. Department for Business, Energy & Industrial Strategy Policy Paper

Posted in Atmospheric chemistry, Climate, Climate change, Greenhouse gases, Renewable energy | Leave a comment

Fieldwork Without The Footprint

By: Joy Singarayer

Over the past two years, we have all faced challenges to our working patterns due to the Covid-19 pandemic. Researchers undertaking overseas fieldwork have found many ways to redefine, reschedule, and adapt their approaches in light of travel restrictions (Forrester, 2020). My colleagues and I faced similar challenges when we began a project in the very first month of the first UK lockdown of 2020. While there have been many issues, there have also been opportunities for us to begin to reflect on our responsibilities to communities and individuals involved in field research, and to the carbon footprint of the project.

Until recently, I had not really given a lot of thought to how the data I was using to compare to climate model simulations was extracted, who was involved, or whether they were appropriately acknowledged. My research has focussed on past (prehistoric) climate change, primarily in the tropics, and the data I was using has been processed from the mud at the bottom of lakes or the stalactites from caves taken from around the world by many other scientists over decades. However, a recent decision to venture into new research avenues led to a collaboration with Prof. Nick Branch (SAGES) and scientists from the UK and South America, which has fieldwork as a central part of the project.

The aim of the research is to examine the impacts of current and future climate change on water supplying ecosystems for agriculture in the Peruvian Andes. Our project is called CROPP (Climate Resilience and fOod Production in Peru) and is funded by the Royal Academy of Engineering. It brings together an international and multidisciplinary team of social scientists, hydrologists, ecologists, climatologists, and NGOs to understand the Andean water systems and their contribution to resilience in the face of climate change. This means working directly with remote farming communities and a large funding commitment in the fieldwork budget for the UK team to undertake annual trips to Peru.

Figure 1: An example, from one of our study areas, of the varied landscape in the Ancash region of Peru – Glacial mountain peak (Huascarán), agricultural fields, and ancient human-made water courses.

The initial excitement at the prospect of working across subject boundaries and continents turned to uncertainty about when travel would be allowed and what alternative approaches could be taken to progress the research. The collection and synthesis of secondary data was an obvious way to begin while we waited to see the full extent of the impact of covid. Several months and numerous international video meetings later, we knew that it would be the South American team undertaking the first fieldwork without direct UK input.

Through our partners in Peru, we have employed local research coordinators to engage with farmers (once safe to do so) to produce and collate agro-economic and social science information through conversations and diaries. During the field season last year, we were also able to hire local student research assistants to work with the South American team to conduct hydrological and ecological field research, with remote support from the UK hydrologists. The researchers have produced excellent new data and the approach has worked well. Significant modifications to the budgets were required but our funder has been incredibly helpful in allowing us the flexibility to do this. As a result, we are thankfully in a decent position at the end of the second year of the project, although there is a lot more to do before we can pull the results together. There are also some aspects of the fieldwork that the UK team will need to undertake in person this year.

That said, we have so far saved 15-20 tonnes CO2 (depending on the emissions calculator used) by reducing our international travel, which is roughly equivalent to the annual emissions of between one and two average UK individuals or between eight to eleven average individuals in Peru (note – these figures vary depending on whether you include imports/exports or just territorial emissions). This feels like a positive outcome that we would want to repeat in future projects. In some ways, the new fieldwork set up may also allow more effective community engagement via trusted local research coordinators.

However, there is much more for us to consider in terms of using this opportunity to set up ethical field research practices that address inequalities and the often extractivist nature of field research in the global south, whereby field data are taken and processed in the global north to create outputs without co-development or proper attribution (Bates, 2020; Dunia et al., 2020; Sukarieh and Tannock, 2019). This is particularly so if we are to continue to reduce international travel and undertake more remote field research involving research assistants in other countries. Dunia et al (2020), for example, outline ways to begin to approach this, from rethinking how we view co-authorship so that we include those facilitating research in remote settings, to proper compensation and insurance. There are also broader responsibilities beyond those directly undertaking field research. For example, funding agencies could request details about how the research field practice will be fair and transparent for all involved, and journal reviewers and editors should flag questions about this aspect of the research when manuscripts are submitted.

The changes we initially made to our field research were due to travel restrictions forced on us by covid, but are now undertaking a different journey exploring our responsibilities to construct fair, sustainable, and creative ways of working.


Bates, J, 2020. Reimagining fieldwork during and beyond the pandemic. Feminist Perspectives blog, Kings College London. At:

Dunia O. A., Eriksson Baaz M.,  Mwambari D., Parashar S., Toppo A.O.M. and Vincent J.B.M, 2020. The Covid-19 Opportunity: Creating More Ethical and Sustainable Research Practices. Items: insights from the Social Sciences. At:

Forrester, N., 2020. How to manage when your fieldwork is cancelled. Nature, Career Feature: doi:

 Sukarieh M. and Tannock S., 2019. Subcontracting Academia: Alienation, Exploitation and Disillusionment in the UK Overseas Syrian Refugee Research Industry. Antipode: a radical journal of geography, 51(2), 664-680.

Posted in Climate, Climate change, Covid-19, Data collection, Diversity and Inclusion, Fieldwork | Leave a comment

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).

Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Chang. 11, 680–688 (2021).

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).

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.

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).

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).

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).

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.

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

Keil, P., Mauritsen, T., Jungclaus, J. et al. Multiple drivers of the North Atlantic warming hole. Nat. Clim. Chang. 10, 667–671 (2020).

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.

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).

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,, 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.

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,

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,

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,

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,

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,

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,

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,

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,

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,

Sea Ice Index, National Snow and Ice Data Center. Accessed April 8, 2022.

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,

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,

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,

Turner, J., and Coauthors, 2020: Recent Decrease of Summer Sea Ice in the Weddell Sea, Antarctica. Geophys. Res. Lett., 47,

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,

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 (

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,

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,

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,

Rishbeth, H., 1990: A Greenhouse effect in the ionosphere?, Planetary and Space Science, 38, 945-948,

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,

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,

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,

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,

Posted in Climate, Climate change, Space, space weather | Leave a comment

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,

[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,

[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,

[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,


Posted in Climate, High-Resolution Climate modelling, Rainfall, Tropical cyclones, Water cycle | 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.

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,

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,

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., (

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,

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,<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,

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,

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