Is Europe At Risk From Hurricanes?

Posted on 27 June, 2022 by danaallen

By: Reinhard Schiemann

Growing up in Europe late last century, I would have been a little surprised at this question, and my knee-jerk answer would have been a firm no: hurricanes happened on TV in far-away tropical places, bending and breaking Caribbean palm trees, but not European oaks.

Some thirty years later, I have learnt that this question is worth unpicking a little more. It is true that most North Atlantic hurricanes form over the ocean at low latitudes before travelling west or northwest primarily making landfall over North America’s Gulf and Atlantic coasts where they can cause damage through the strong winds and rain they bring. Some hurricanes do however recurve into an eastward path and eventually reach Europe (Figure 1). They change as they do so, losing the characteristic eye, tending to weaken, and developing warm and cold fronts. In short, some (ex-)hurricanes reach Europe, but they are no longer hurricanes when they do.

Figure 1: Path and lifecycle of Hurricane Katia (August/September 2011).

Recent work at the University of Reading and the National Centre for Atmospheric Science has given us a better idea of how often such storms affect Europe, what properties they have, how damaging they are, and what factors control their incidence. Baker et al. (2020) show that these so-called post-tropical cyclones (PTCs) are rare; about two PTCs make landfall in Europe per year on average with some years seeing none at all and more than five landfalls in other years. Interestingly, in a minority of these storms, aspects of their tropical origin can be recognised even as they make landfall in Europe, and it is these storms that are the windiest PTCs to reach Europe.

Given PTCs are so rare, one might argue that they are a curiosity but not overly important as a source of hazardous weather affecting Europe. To assess their importance fairly, PTCs need to be put in the context of the hundreds of midlatitude storms that affect Europe each year and do not originate in the tropics. This is one of the issues addressed by Elliott Sainsbury, SCENARIO PhD student at the University of Reading. Elliott and his colleagues have shown that, while only about 1% of all storms affecting Europe are PTCs, they constitute about 8% of the systems attaining storm-force winds (Sainsbury et al 2020, Figure 2).

Figure 2: (left) Normalised frequency of post-tropical cyclones (PTCs) and, other, midlatitude cyclones (MLCs) affecting Europe and attaining a given surface windspeed, and (right) the fraction of all storms which are PTCs and attain a given windspeed

In other work, they determined what controls the large variations in the year-to-year number of PTCs (Sainsbury et al. 2022). They show that the number of hurricanes recurving and entering the midlatitude North Atlantic in each year is primarily determined by the total number of hurricanes forming in the tropical Atlantic in the first place. This latter number, also called the activity of the hurricane season, can be predicted with some skill ahead of the season as it is controlled by large-scale and predictable modes of climate variability such as the El Niño Southern Oscillation (ENSO) phenomenon. The results by Sainsbury et al. 2022 are therefore encouraging, as some of the seasonal predictive skill might extend to PTCs affecting midlatitude regions such as Europe.

Finally, it is logical to ask if the number or character of PTCs affecting Europe will change with global warming. The answer is, alas, not known. There is indeed some concern that more of these storms might reach Europe as the North Atlantic Ocean warms (Haarsma et al. 2013), yet climate model simulations do not agree on the future change – Elliott’s ongoing work shows that most models project an end-of-century decrease in the number of North Atlantic hurricanes offset by an increase in the fraction of hurricanes reaching the midlatitudes as PTCs. Crucially, the latest generation of models cannot be trusted to fully capture the physical processes controlling the character and trajectories of hurricanes, and further research and climate model development are needed to address this question with any degree of certainty.

References:

Baker, A. J., K. I. Hodges, R. K. H. Schiemann, and P. L. Vidale, 2021: Historical Variability and Lifecycles of North Atlantic Midlatitude Cyclones Originating in the Tropics. Journal of Geophysical Research: Atmospheres, 126(9), 1–18, https://doi.org/10.1029/2020JD033924.

Haarsma, R. J., W. Hazeleger, C. Severijns, H. de Vries, A. Sterl, R. Bintanja, et al., 2013: More hurricanes to hit western Europe due to global warming. Geophysical Research Letters, 40(9), 1783–1788, https://doi.org/10.1002/grl.50360.

Sainsbury, E. M., R. K. H. Schiemann, K. I. Hodges, L. C. Shaffrey, A. J. Baker, and K. T. Bhatia, 2020: How Important Are Post‐Tropical Cyclones for European Windstorm Risk? Geophysical Research Letters, 47(18), https://doi.org/10.1029/2020GL089853.

Sainsbury, E. M., R. K. H. Schiemann, K. I. Hodges, A. J. Baker, L. C. Shaffrey, and K. T. Bhatia, 2022: What Governs the Interannual Variability of Recurving North Atlantic Tropical Cyclones? Journal of Climate, 35(12), 3627–3641, https://doi.org/10.1175/JCLI-D-21-0712.1.

Posted in Climate, Europe, extremes, North Atlantic, Windstorms | Leave a comment

Forecasting Rapid Intensification In Hurricanes And Typhoons.

Posted on 20 June, 2022 by danaallen

By: Peter Jan Leeuwen

We all know the devastating power of hurricanes, typhoons, and their Southern Hemisphere counterparts. It is crucial that we predict their behaviour accurately to avoid loss of life and to better guide large-scale infrastructure operations. Although tremendous progress has been made, especially in predicting their propagation path, the intensity or wind forecasts are much more difficult. This is related to the fact that the path of a hurricane is largely determined by the large scale atmospheric environment, and we know that environment quite well. However, intensity has to do with small-scale details in the core regions of hurricanes, and these are much harder to predict. The largest unknown is the mysterious rapid intensification, in which the wind speed in a hurricane can increase from 50 km/h to an astonishing 300 km/h in two days.

Figure 1: a) Satellite view of Hurricane Patricia just before landfall, and b) maximum wind at 10 m above the sea surface in Hurricane Patricia (Note 1 m/s corresponds to 3.6 km/h).

 Hurricane Patricia (see figures 1a and b)  in 2015 holds the rapid-intensification record and we have studied her in detail. Fortunately, we had an exceptionally detailed data set of temperature, humidity and wind fields in the inner region of the Hurricane from aircraft measurements. (Indeed, they did fly the plane straight through the core of the Hurricane…)  This provided an unprecedented view of the inner structure of the Hurricane, but also allows us to study the influence of these observations on prediction.

For this prediction we update the model fields, such as the temperature field and the wind field, using a technique called data assimilation. Data assimilation is a systematic method to incorporate observations into computer models (see e.g. the open access book Evensen et al, 2022, with over 40,000 downloads). For the results below we use a state-of-the-art Local Ensemble Transform Ensemble Kalman Filter, abbreviated to LETKF (see Tao et al. 2022 for details of this study). We run two experiments, one in which we assimilated only large-scale satellite data, and one in which we added the aircraft data of the inner hurricane regions. This resulted in two forecast ensembles, the yellow-brown lines and the blue lines in figure 2.

Figure 2: The strength of the wind as function of distance to the centre of the Hurricane.  Data from two forecast ensembles, one ensemble based on only satellite data (yellow-brown) and one ensemble based on both the satellite and the aircraft data (blue). The purple lines are not important here. Note that the aircraft data give rise to much higher velocities because they resolve much smaller scales.

Figure 2 shows that the ensemble based on the aircraft data (blue lines) shows much higher wind speeds, and these hurricanes all develop a rapid intensification phase and become major category 5 hurricanes. The yellow-brown lines do not use the aircraft data, have much lower wind speeds, and do not develop into strong hurricanes. We conclude that the detailed data in the inner part of the Hurricane are crucial for a proper prediction of the intensity of Hurricanes.

These model predictions can be studied further using techniques from causal discovery developed for Hurricane dynamics (Van Leeuwen et al. 2021). Causal discovery methods try to find cause and effect relations in hurricane evolution. The weaker Hurricanes that do not develop rapid intensification have different connections between the temperature and the wind fields than those hurricanes that do show rapid intensification. Specifically, what is needed for rapid intensification is a collaborative action of the temperature and humidity at the sea surface, strong upward motion in the core region, and rain and snow formation in the region close to the centre of the Hurricane, as well as strong heating of the centre region from the stratosphere. All these work together to heat up the core region of the Hurricane, which provides the energy to increase the winds. These winds bring in more humidity near the sea surface, leading to more rain and snow formation, leading to further heating etc. If all these processes work in Harmony rapid intensification is the result. In contrast, when one of these processes is out of sink, as with the yellow-brown lines, the Hurricane does not grow fast and rapid intensification does not occur.

Concluding, although our understanding keeps increasing there are still many missing parts. One way forward is to use better ways to bring the observations into the prediction models. The methods used today, such as the LETKF mentioned above, are based on linearizations that do not allow us to extract all relevant information from the data. This can lead to incorrect interpretation of the causal relations between hurricane variables. New fully nonlinear data-assimilation methods have been developed (e.g. Hu and Van Leeuwen, 2021) and we are working on implementing these in Hurricane prediction models to improve predictions and to understand these major ‘freaks of nature’ better.

References:

Evensen, G., F.M. Vossepoel, and P.J. van Leeuwen (2022) Data Assimilation Fundamentals, Springer, doi: 10.1007/978-3-030-96709-3  (free to download)

Hu, C-C, and P.J. van Leeuwen (2021) A particle flow filter for fully nonlinear high-dimensional data assimilation., Q.J. Royal Meteorol. Soc.,  doi:10.1002/qj.4028

Tao, D., van Leeuwen, P. J., Bell, M., and Ying, Y. (2022). Dynamics and predictability of tropical cyclone rapid intensification in ensemble simulations of Hurricane Patricia (2015). Journal of Geophysical Research: Atmospheres, 127, doi:10.1029/2021JD036079

Van Leeuwen, P.J., M. DeCaria, N. Chakraborty, and M. Pulido (2021) A new framework for causal discovery, Chaos, 31, 123128, doi:10.1063/5.0054228

Posted in Climate, data assimilation, Predictability, Tropical cyclones | Leave a comment

A Different Kind Of Turbulence

Posted on 13 June, 2022 by danaallen

By Miguel Teixeira

It might be thought that turbulence is essentially the same everywhere. However, its mixing efficiency depends not only on its intensity (as might be expected intuitively), but also on more subtle properties, such as its anisotropy (which components of the velocity fluctuations are dominant). These characteristics are determined by the mechanisms that generate the turbulence. For example, it is known that convective turbulence (generated by positive heat fluxes into the atmosphere or negative heat fluxes out of the ocean) typically mixes more efficiently than shear-generated turbulence induced by the no-slip boundary condition at the ground. This is not only because heating is often a more potent source of energy than wind shear, but also because convective turbulence is dominated by its vertical velocity fluctuations (which have a greater mixing ability – in the vertical), whereas shear turbulence is dominated by horizontal velocity fluctuations.

Even ignoring thermal effects, turbulence in the oceanic boundary layer has a totally different character from that of atmospheric boundary layer turbulence, because of the different boundary conditions at the air-water interface relative to those at the ground. While, in the atmosphere over flat terrain (undulating terrain is different), the no-slip boundary condition generates shear-driven turbulence, in the oceanic boundary layer, a sheared water current exists near the surface, forced by the wind stress, but it is not the most important aspect. The wind stress also generates surface waves, which have an associated Lagrangian transport in the direction of wave propagation, named “Stokes drift”. This element totally changes the character of the turbulence.

Figure 1 shows foam alignments at the surface of a body of water. This foam, which was probably produced by micro-breaking or white-capping of the surface waves (also caused by the wind), is aligned perpendicularly to the crests of the waves (at least far from the shore) and is also roughly aligned with the wind. This happens because of the presence of coherent vortices (known as Langmuir circulations) in the water, with their axes aligned with both the wind and wave propagation direction. The foam collects at the convergence zones of these vortices at the air-water interface.

Figure 1: Windrows, or foam alignments at the air-water interface of a body of water, induced by the joint effects of a surface current induced by the wind and surface waves via Langmuir Circulations.

Although Langmuir circulations were initially studied as single-scale features, using modal growth linear theories (Craik and Leibovich, 1976), it has become clear that they have multiple scales, so they can be viewed as a separate type of turbulence (named Langmuir turbulence) (McWilliams et al., 1997), distinct from the shear-driven turbulence more common in non-convective boundary layers. The general configuration of Langmuir turbulence is described in Figure 2. The streamwise vortices that dominate Langmuir turbulence are typically aligned with the direction of propagation of surface waves, and within 5˚ to 15˚ of the wind direction. They are characterized by convergence zones (in blue) where foam (or floating debris) collects, vertical velocities of ~5 u*, where u* is the friction velocity in the water, and surface jets at the convergence zones in the direction of both the wind and waves, with velocity perturbations of ~10 – 15 u*. The upwelling/surface-divergence zones of Langmuir vortices are privileged locations for the emergence of plankton.

Figure 2: Schematic diagram showing the configuration of Langmuir turbulence vortices and the associated physical phenomena (from Smith, 2001).

The mechanisms underlying the structure of Langmuir turbulence, and its differences from shear turbulence, are explained in Figure 3. On the left panel, we can see how vertical vorticity (which always exists in turbulence) is tilted and stretched by the vertical profile of the Stokes drift associated with surface waves propagating from left to right. This Lagrangian transport has a maximum at the surface and decays to zero as depth increases. This causes a tilting of the vertical vorticity into the direction of the Stokes drift (which coincides with the direction of wave propagation), and stretching of this vorticity. We will call the wave propagation direction, which typically coincides with the direction of the wind stress and of the shear in the current induced by the wind, the streamwise direction. Stretching of the streamwise vorticity causes its amplification, making this vorticity become dominant in the turbulence. This explains the existence of coherent streamwise vortices in Langmuir turbulence. These vortices are dominated by velocity fluctuations in the spanwise and vertical directions, the latter strongly promoting vertical mixing.

On the right panel of Figure 3, we see how shear-driven turbulence differs from Langmuir turbulence in this respect. Vertical vorticity in the turbulence is equally tilted and stretched into the streamwise direction, now by mean shear in the wind-driven current. But the mean vorticity in the current is also tilted by the circulation induced by the turbulent vorticity, and this causes a partial cancellation of the latter. This is why shear-driven turbulence is not dominated by streamwise vortices, like Langmuir turbulence. Rather, velocity fluctuations in the streamwise direction (sometimes called “streaky structures”) are dominant.

Figure 3: Left: tilting and stretching of vertical vorticity into streamwise vorticity by the Stokes drift of surface waves; Right: tilting and stretching of vorticity in a shear flow (from Teixeira and Belcher, 2002).

The consequences of these differences for the transport of buoyant tracers trapped at the air-water interface are explained in Figure 4 (where a surface wave is assumed to propagate from left to right and/or a wind is assumed to blow from left to right). In wave-driven (or Langmuir) turbulence (diagram on the left), the flow is dominated by streamwise vortices, which at the surface induce primarily spanwise velocity fluctuations (arrows). The convergence zones of this velocity field lead to the concentration of buoyant tracers along lines aligned in the streamwise direction. In shear-driven turbulence (diagram on the right), there is also a tendency for buoyant surface tracers to align in the streamwise direction, but the mechanism that causes it is weaker. This is associated with the confluence that necessarily occurs at the entrance regions to maxima in the dominant streamwise velocity fluctuations (streaky structures).

Figure 4: Schematic diagrams showing the transport of buoyant tracers by (Left) streamwise vortices in Langmuir turbulence; (Right) streaky structures in shear-driven turbulence (from Teixeira and Belcher, 2010).

An up-to-date overview of Langmuir turbulence, including motivation of its importance, recent developments in theory, measurements and numerical modelling, and various applications, is provided in a recent short review by the author of this post , published in the latest edition of the Encyclopedia of Ocean Sciences (Teixeira, 2019).

References

Craik, A. D. D., and Leibovich, S. (1976) A rational model for Langmuir circulations. J. Fluid Mech., 73, 401-426. doi: https://doi.org/10.1017/S0022112076001420

McWilliams, J. C., Sullivan, P. P. and Moeng, C.-H. (1997) Langmuir turbulence in the ocean. J. Fluid Mech., 334, 1-30. doi: https://doi.org/10.1017/S0022112096004375

Smith, J. A. (2001) Observations and theories of Langmuir circulation: a story of mixing. In Fluid Dynamics and the Environment: Dynamical Approaches, Lecture Notes in Physics, vol. 566, 295-314, Ed.: Lumley, J.L., Springer. doi: https://doi.org/10.1007/3-540-44512-9_16

Teixeira, M. A. C. (2019) Langmuir circulation and instability. In Encyclopedia of Ocean Sciences (Third Edition), Eds. J. K. Cochran, H. J. Bokuniewicz, P. L. Yager, Academic Press, pp. 92-106. doi: https://doi.org/10.1016/B978-0-12-409548-9.04176-2

Teixeira, M. A. C. and Belcher, S. E. (2002) On the distortion of turbulence by a progressive surface wave. J. Fluid Mech., 458, 229-267. doi: https://doi.org/10.1017/S0022112002007838

Teixeira, M. A. C. and Belcher, S. E. (2010) On the structure of Langmuir turbulence. Ocean Modelling, 31, 105-119. doi: https://doi.org/10.1016/j.ocemod.2009.10.007

 

Posted in Boundary layer, Climate, Environmental physics, Fluid-dynamics, Oceans, Turbulence, Waves | Leave a comment

What are the challenges in forecasting the impacts of tropical cyclones?

Posted on 30 May, 2022 by timpoulter

By: Liz Stephens

Last year I joined the Meteorology department in a joint-post between the University of Reading and the Red Cross Red Crescent Climate Centre (RCCC), but I still suspect most people have no idea exactly what it is that I do! Apart from the administrative team in the Netherlands, the Climate Centre is virtual, we have 40+ team members based all around the world which makes for a wonderful blend of cultures (and a sometimes confusing use of humour and local proverbs!).

My role is as the Science Lead for Anticipatory Action, which means I help steer efforts to use forecasts to take actions to support vulnerable communities in advance of a disaster. We develop so-called Early Action Protocols (EAPs, plans to secure pre-agreed financing in advance of a disaster) for taking actions before different types of hazard. These EAPs are required to show evidence of forecast skill, which is often a challenge where forecast archives or observational data is limited.

This work aligns very well with the research I have been leading under the Science for Humanitarian Emergencies and Resilience research programme. We have supported the assessment of flood forecast skill in Uganda, which has informed where in the country Anticipatory Action is feasible. We have also produced flood forecast bulletins for the Foreign Commonwealth and Development Office (FCDO), firstly, for Tropical Cyclone Idai in 2019, but then with continued funding to produce these for many tropical cyclones with associated flood impacts in vulnerable countries.

Figure 1: Mocuba District in Nampula Province. (c) Mozambique Red Cross Society

The intersection between my two roles has been even more apparent in the last year. Super typhoon Rai (Odette) in the Philippines caused enormous impacts just before Christmas in 2021, but due to rapid intensification the forecasts of the storm had limited accuracy beyond 12 hours before landfall; nowhere near enough time to trigger the release of financing. I spoke about this more on “Science in Action” for the BBC World Service (https://www.bbc.co.uk/sounds/play/w3ct1l4s, from 9 minutes in). I was invited to join a panel discussion at a workshop led by the Start Network in January to discuss how to address the challenges that rapid intensification gives us within our decision-making, commenting on research presented by the University of Philippines and PAGASA on rapid intensification. (The Reading link continues, with PhD researcher BA Racoma also joining the workshop, and with one of the presenters having Ed Hawkins’s climate stripes in the background).

Earlier this year we saw the devastating impact of a series of tropical cyclones in the Southern Indian Ocean (Ana, Batsirai, Emnati, Gombe), affecting Madagascar, Mozambique and Malawi. Along with the wider team of scientists from ECMWF, University of Bristol and HR Wallingford, we (University of Reading) produced flood forecast bulletins for FCDO to provide onto humanitarian partners. These bulletins provide support to humanitarians operating on the ground, and inform the release of funds by the government to support these operations (e.g. https://www.gov.uk/government/news/uk-provides-emergency-support-to-madagascar-after-cyclone-batsirai). With colleagues at RCCC we provided early awareness to the National Red Cross Societies that we support of the potential for impacts, providing interpretation of the forecast uncertainties and likely areas worse hit.

Figure 2: Global Flood Awareness System (GloFAS) forecasts for Tropical Cyclone Batsirai, February 2022. Colour saturation represents the probability of a 1 in 5 year return period flood.

The learning across all of these cyclones is that for early warning and humanitarian decision-making there is a massive need to improve the provision of multi-hazard early warnings – too often the forecast information coming in is fragmented, with one source for wind, another for river floods, another for storm-surge flooding and so on. To make robust decisions we need to combine the hazard forecasts to provide comprehensive assessments of exposure and vulnerability, giving us an overall assessment of risk that will help to prioritise resources, determine early actions and provide appropriate impact-based warnings. This of course needs to be led by the national meteorological, hydrological and disaster management authorities.

Posted in Climate, Flooding, Tropical cyclones, Weather forecasting | Leave a comment

Co-Producing New Sub-Seasonal Weather Forecasts in Africa

Posted on 23 May, 2022 by danaallen

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?

References

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

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: https://doi.org/10.1175/WAF-D-20-0054.1

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)   https://doi.org/10.1016/j.cliser.2021.100246

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

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

Posted on 16 May, 2022 by danaallen

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)

References:

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 https://www.gov.uk/government/publications/atmospheric-implications-of-increased-hydrogen-use

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

Fieldwork Without The Footprint

Posted on 9 May, 2022 by danaallen

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.

References:

Bates, J, 2020. Reimagining fieldwork during and beyond the pandemic. Feminist Perspectives blog, Kings College London. At: https://www.kcl.ac.uk/reimagining-fieldwork-during-and-beyond-the-pandemic

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: https://items.ssrc.org/covid-19-and-the-social-sciences/social-research-and-insecurity/the-covid-19-opportunity-creating-more-ethical-and-sustainable-research-practices/

Forrester, N., 2020. How to manage when your fieldwork is cancelled. Nature, Career Feature: doi: https://doi.org/10.1038/d41586-020-03368-0

 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. https://doi.org/10.1111/anti.12502

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?

Posted on 2 May, 2022 by danaallen

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.

Notes:

*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!

References:

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

Posted on 19 April, 2022 by danaallen

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.

References:

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

Posted on 28 March, 2022 by danaallen

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.

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