The Circumglobal Teleconnection and its Links to Seasonal Forecast Skill for the European Summer

Email: j.beverley@pgr.reading.ac.uk

Recent extreme weather events such as the central European heatwave in 2003, flooding in the UK in 2007, and even the recent dry summer in the UK in 2018, have highlighted the need for more accurate long-range forecasts for the European summer. Recent research has led to improvements in European winter seasonal forecasts, however summer forecast skill remains relatively low. One potential source of predictability for Europe is the Indian summer monsoon, which can affect European weather via a global wave train known as the “Circumglobal Teleconnection” (CGT).

figure1
Figure 1: One-point correlation between 200 hPa geopotential height at the base point (35°-40°N, 60°-70°E) and 200 hPa geopotential height elsewhere in the ERA-Interim (1981–2014) reanalysis dataset, for August. The boxes indicate the regions defined as the “centres of action” of the CGT – these are North Pacific (NPAC), North America (NAM), Northwest Europe (NWEUR), Ding and Wang (D&W) and East Asia (EASIA).

The CGT was first identified by Ding and Wang (2005) as having a major role in modulating observed weather patterns in the Northern Hemisphere summer. Using a 200 hPa geopotential height index centred in west-central Asia (35°-40°N, 60°-70°E), they constructed a one-point correlation map of geopotential height with reference to this index (reproduced in Figure 1). From this, they identified a wavenumber-5 structure where the pressure variations over the Northeast Atlantic, East Asia, North Pacific and North America are all nearly in phase with the variations over west-central Asia (these are known as the “centres of action”). They also showed that the CGT is associated with significant temperature and precipitation anomalies in Europe, so accurate representation this mechanism in seasonal forecast models could provide an important source of subseasonal to seasonal forecast skill.

The model used here is a version of the European Centre for Medium-Range Weather Forecasts (ECMWF)’s coupled seasonal forecast model. Reforecasts are initialised on 1st May and are run for four months, so cover May-August, with start dates from 1981-2014. The skill of the model 200 hPa geopotential height is shown in Figure 2, defined as the correlation between the model ensemble mean and ERA-Interim. The model has good skill in May (to be expected given that the reforecasts are initialised in May) but in June, July and August areas of zero or negative correlation develop across much of the northern hemisphere extratropics. The areas of reduced skill align closely with the location of the centres of action of the CGT shown in Figure 1, suggesting that there is a link between the model skill and the model representation of the CGT.

figure2
Figure 2: Model ensemble mean skill for 200 hPa geopotential height as defined as the correlation between ERA-Interim and model ensemble mean for (a) May (b) June (c) July and (d) August

To determine how well the model represents the CGT, Figure 3 shows the correlation between the D&W region and the other centres of action of the CGT, as defined in Figure 1. Focussing on August (as August has the strongest CGT pattern) it can be seen that the model correlations, indicated by the box and whisker plots, are weaker than in observations (red diamond) for the D&W vs. North Pacific (NPAC), North America (NAM) and Northwest Europe (NWEUR) regions. This indicates that the model has a weak representation of the wavetrain associated with the CGT.

figure3
Figure 3: Distribution of correlation coefficients for the D&W Index correlated against the other centres of action of the CGT. The box plots represent the upper and lower quartiles, and the whiskers extend to the 5th and 95th percentiles. The black horizontal line represents the median value and the red diamond the observed correlation coefficient from ERA-Interim.

There are likely to be several reasons for the weak representation of the CGT in the model. One important factor is the presence of a northerly jet bias in the model across much of the Northern Hemisphere. This can be seen in Figure 4, which shows the model jet biases relative to ERA-Interim in the coloured contours, and the observed zonal wind in the black contours. The dipole structure of the biases which exists across much of the hemisphere, particularly in June, July and August, indicates that the model jet stream is located too far to the north. This means that Rossby waves forced in this region will have different wave propagation characteristics to reality – they may propagate at the incorrect speed, in the wrong direction or may not propagate at all, and this is likely to be an important factor in the weak representation of the CGT in the model.

figure4
Figure 4: Model 200 hPa zonal wind bias (filled contours, m/s), defined as the model ensemble mean minus ERA-Interim zonal wind, and ERA-I 200 hPa zonal wind (black contours) for (a) May (b) June (c) July and (d) August. The location of the centres of action of the CGT are marked with white crosses.

Other potential factors involved are a poor representation of the link between monsoon precipitation and the geopotential height in west-central Asia (which was shown by Ding and Wang (2007) to be important in the maintenance of the CGT) and errors in the forcing of Rossby waves associated with the monsoon. For a more detailed explanation of these, see my paper in Climate Dynamics (Beverley et al. 2018). It seems likely that the pattern of reduced skill in Figure 2, with negative correlations located at the centres of action of the CGT, including over Europe, is related to the poor representation of the CGT in the model. This raises the question of whether an improvement in the model’s representation of the CGT would lead to an improvement in forecast skill for the European summer. To address this question, sensitivity experiments have been carried out, in which the observed circulation is imposed in several centres of action along the CGT pathway to explore the impact on forecast skill for European summer weather.

References

Beverley, J. D., S. J. Woolnough, L. H. Baker, S. J. Johnson and A. Weisheimer, 2018: The northern hemisphere circumglobal teleconnection in a seasonal forecast model and its relationship to European summer forecast skill. Clim. Dyn. https://doi.org/10.1007/s00382-018-4371-4

Ding, Q., and B. Wang, 2005: Circumglobal teleconnection in the northern hemisphere summer. J. Clim. 18, 3483–3505.  https://doi.org/10.1175/JCLI3473.1

Ding, Q., and B. Wang, 2007: Intraseasonal teleconnection between the summer Eurasian wave train and the Indian monsoon. J. Clim. 20, 3751-3767. https://doi.org/10.1175/JCLI4221.1

Modelling windstorm losses in a climate model

Extratropical cyclones cause vast amounts of damage across Europe throughout the winter seasons. The damage from these cyclones mainly comes from the associated severe winds. The most intense cyclones have gusts of over 200 kilometres per hour, resulting in substantial damage to property and forestry, for example, the Great Storm of 1987 uprooted approximately 15 million trees in one night. The average loss from these storms is over $2 billion per year (Schwierz et al. 2010) and is second only to Atlantic Hurricanes globally in terms of insured losses from natural hazards. However, the most severe cyclones such as Lothar (26/12/1999) and Kyrill (18/1/2007) can cause losses in excess of $10 billion (Munich Re, 2016). One property of extratropical cyclones is that they have a tendency to cluster (to arrive in groups – see example in Figure 1), and in such cases these impacts can be greatly increased. For example Windstorm Lothar was followed just one day later by Windstorm Martin and the two storms combined caused losses of over $15 billion. The large-scale atmospheric dynamics associated with clustering events have been discussed in a previous blog post and also in the scientific literature (Pinto et al., 2014; Priestley et al. 2017).

Picture1
Figure 1. Composite visible satellite image from 11 February 2014 of 4 extratropical cyclones over the North Atlantic (circled) (NASA).

A large part of my PhD has involved investigating exactly how important the clustering of cyclones is on losses across Europe during the winter. In order to do this, I have used 918 years of high resolution coupled climate model data from HiGEM (Shaffrey et al., 2017) which provides a huge amount of winter seasons and cyclone events for analysis.

In order to understand how clustering affects losses, I first of all need to know how much loss/damage is associated with each individual cyclone. This is done using a measure called the Storm Severity Index (SSI – Leckebusch et al., 2008), which is a proxy for losses that is based on the 10-metre wind field of the cyclone events. The SSI is a good proxy for windstorm loss. Firstly, it scales the wind speed in any particular location by the 98th percentile of the wind speed climatology in that location. This scaling ensures that only the most severe winds at any one point are considered, as different locations have different perspectives on what would be classed as ‘damaging’. This exceedance above the 98th percentile is then raised to the power of 3 due to damage from wind being a highly non-linear function. Finally, we apply a population density weighting to our calculations. This weighting is required because a hypothetical gust of 40 m/s across London will cause considerably more damage than the same gust across far northern Scandinavia, and the population density is a good approximation for the density of insured property. An example of the SSI that has been calculated for Windstorm Lothar is shown in Figure 2.

 

figure_2_blog_2018_new
Figure 2. (a) Wind footprint of Windstorm Lothar (25-27/12/1999) – 10 metre wind speed in coloured contours (m/s). Black line is the track of Lothar with points every 6 hours (black dots). (b) The SSI field of Windstorm Lothar. All data from ERA-Interim.

 

From Figure 2b you can see how most of the damage from Windstorm Lothar was concentrated across central/northern France and also across southern Germany. This is because the winds here were most extreme relative to what is the climatology. Even though the winds are highest across the North Atlantic Ocean, the lack of insured property, and a much high climatological winter mean wind speed, means that we do not observe losses/damage from Windstorm Lothar in these locations.

figure_3_blog_2018_new
Figure 3. The average SSI for 918 years of HiGEM data.

 

I can apply the SSI to all of the individual cyclone events in HiGEM and therefore can construct a climatology of where windstorm losses occur. Figure 3 shows the average loss across all 918 years of HiGEM. You can see that the losses are concentrated in a band from southern UK towards Poland in an easterly direction. This mainly covers the countries of Great Britain, Belgium, The Netherlands, France, Germany, and Denmark.

This blog post introduces my methodology of calculating and investigating the losses associated with the winter season extratropical cyclones. Work in Priestley et al. (2018) uses this methodology to investigate the role of clustering on winter windstorm losses.

This work has been funded by the SCENARIO NERC DTP and also co-sponsored by Aon Benfield.

 

Email: m.d.k.priestley@pgr.reading.ac.uk

 

References

Leckebusch, G. C., Renggli, D., and Ulbrich, U. 2008. Development and application of an objective storm severity measure for the Northeast Atlantic region. Meteorologische Zeitschrift. https://doi.org/10.1127/0941-2948/2008/0323.

Munich Re. 2016. Loss events in Europe 1980 – 2015. 10 costliest winter storms ordered by overall losses. https://www.munichre.com/touch/naturalhazards/en/natcatservice/significant-natural-catastrophes/index.html

Pinto, J. G., Gómara, I., Masato, G., Dacre, H. F., Woollings, T., and Caballero, R. 2014. Large-scale dynamics associated with clustering of extratropical cyclones affecting Western Europe. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1002/2014JD022305.

Priestley, M. D. K., Dacre, H. F., Shaffrey, L. C., Hodges, K. I., and Pinto, J. G. 2018. The role of European windstorm clustering for extreme seasonal losses as determined from a high resolution climate model, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2018-165, in review.

Priestley, M. D. K., Pinto, J. G., Dacre, H. F., and Shaffrey, L. C. 2017. Rossby wave breaking, the upper level jet, and serial clustering of extratropical cyclones in western Europe. Geophysical Research Letters. https://doi.org/10.1002/2016GL071277.

Schwierz, C., Köllner-Heck, P., Zenklusen Mutter, E. et al. 2010. Modelling European winter wind storm losses in current and future climate. Climatic Change. https://doi.org/10.1007/s10584-009-9712-1.

Shaffrey, L. C., Hodson, D., Robson, J., Stevens, D., Hawkins, E., Polo, I., Stevens, I., Sutton, R. T., Lister, G., Iwi, A., et al. 2017. Decadal predictions with the HiGEM high resolution global coupled climate model: description and basic evaluation, Climate Dynamics, https://doi.org/10.1007/s00382-016-3075-x.

The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing

Email: j.f.talib@pgr.reading.ac.uk

Talib, J., S.J. Woolnough, N.P. Klingaman, and C.E. Holloway, 2018: The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing. J. Climate, 31, 6821–6838, https://doi.org/10.1175/JCLI-D-17-0794.1

Rainfall in the tropics is commonly associated with the Intertropical Convergence Zone (ITCZ), a discontinuous line of convergence collocated at the ascending branch of the Hadley circulation, where strong moist convection leads to high rainfall. What controls the location and intensity of the ITCZ remains a fundamental question in climate science.

ensemble_precip_neat_thesis
Figure 1: Annual-mean, zonal-mean tropical precipitation (mm day-1) from Global Precipitation Climatology Project (GPCP, observations, solid black line) and CMIP5 (current coupled models) output. Dashed line indicates CMIP5 ensemble mean.

In current and previous generations of climate models, the ITCZ is too intense in the Southern Hemisphere, resulting in two annual-mean, zonal-mean tropical precipitation maxima, one in each hemisphere (Figure 1).  Even if we take the same atmospheric models and couple them to a world with only an ocean surface (aquaplanets) with prescribed sea surface temperatues (SSTs), different models simulate different ITCZs (Blackburn et al., 2013).

Within a climate model parameterisations are used to replace processes that are too small-scale or complex to be physically represented in the model. Parameterisation schemes are used to simulate a variety of processes including processes within the boundary layer, radiative fluxes and atmospheric chemistry. However my work, along with a plethora of others, shows that the representation of the ITCZ is sensitive to the convective parameterisation scheme (Figure 2a). The convective parameterisation scheme simulates the life cycle of clouds within a model grid-box.

Our method of showing that the simulated ITCZ is sensitive to the convective parameterisation scheme is by altering the convective mixing rate in prescribed-SST aquaplanet simulations. The convective mixing rate determines the amount of mixing a convective parcel has with the environmental air, therefore the greater the convective mixing rate, the quicker a convective parcel will become similar to the environmental air, given fixed convective parcel properties.

AEIprecipCREon
Figure 2: Zonal-mean, time-mean (a) precipitation rates (mm day-1}$) and (b) AEI (W m-2) in simulations where the convective mixing rate is varied.

In our study, the structure of the simulated ITCZ is sensitive to the convective mixing rate. Low convective mixing rates simulate a double ITCZ (two precipitation maxima, orange and red lines in Figure 2a), and high convective mixing rates simulate a single ITCZ (blue and black lines).

We then associate these ITCZ structures to the atmospheric energy input (AEI). The AEI is the amount of energy left in the atmosphere once considering the top of the atmosphere and surface energy budgets. We conclude, similar to Bischoff and Schneider, 2016, that when the AEI is positive (negative) at the equator, a single (double) ITCZ is simulated (Figure 2b). When the AEI is negative at the equator, energy is needed to be transported towards the equator for equilibrium. From a mean circulation perspective, this take place in a double ITCZ scenario (Figure 3). A positive AEI at the equator, is associated with poleward energy transport and a single ITCZ.

blog_figure_ITCZ_simulation
Figure 3: Schematic of a single (left) and double ITCZ (right). Blue arrows denote energy transport. In a single ITCZ scenario more energy is transported in the upper branches of the Hadley circulation, resulting in a net-poleward energy transport. In a double ITCZ scenario, more energy is transport equatorward than poleward at low latitudes, leading to an equatorward energy transport.

In our paper, we use this association between the AEI and ITCZ to hypothesize that without the cloud radiative effect (CRE), atmospheric heating due to cloud-radiation interactions, a double ITCZ will be simulated. We also hypothesize that prescribing the CRE will reduce the sensitivity of the ITCZ to convective mixing, as simulated AEI changes are predominately due to CRE changes.

In the rest of the paper we perform simulations with the CRE removed and prescribed to explore further the role of the CRE in the sensitivity of the ITCZ. We conclude that when removing the CRE a double ITCZ becomes more favourable and in both sets of simulations the ITCZ is less sensitive to convective mixing. The remaining sensitivity is associated with latent heat flux alterations.

My future work following this publication explores the role of coupling in the sensitivity of the ITCZ to the convective parameterisation scheme. Prescribing the SSTs implies an arbitary ocean heat transport, however in the real world the ocean heat transport is sensitive to the atmospheric circulation. Does this sensitivity between the ocean heat transport and atmospheric circulation affect the sensitivity of the ITCZ to convective mixing?

Thanks to my funders, SCENARIO NERC DTP, and supervisors for their support for this project.

References:

Blackburn, M. et al., (2013). The Aqua-planet Experiment (APE): Control SST simulation. J. Meteo. Soc. Japan. Ser. II, 91, 17–56.

Bischoff, T. and Schneider, T. (2016). The Equatorial Energy Balance, ITCZ Position, and Double-ITCZ Bifurcations. J. Climate., 29(8), 2997–3013, and Corrigendum, 29(19), 7167–7167.

 

Hierarchies of Models

With thanks to Inna Polichtchouk.

General circulation models (GCMs) of varying complexity are used in atmospheric and oceanic sciences to study different atmospheric processes and to simulate response of climate to climate change and other forcings.

However, Held (2005) warned the climate community that the gap between understanding and simulating atmospheric and oceanic processes is becoming wider. He stressed the use of model hierarchies for improved understanding of the atmosphere and oceans (Fig. 1). Often at the bottom of the hierarchy lie the well-understood, idealized, one- or two-layer models.  In the middle of the hierarchy lie multi-layer models, which omit certain processes such as land-ocean-atmosphere interactions or moist physics. And finally, at the top of the hierarchy lie fully coupled atmosphere-ocean general circulation models that are used for climate projections. Such model hierarchies are already well developed in other sciences (Held 2005), such as molecular biology, where studying less complex animals (e.g. mice) infers something about the more complex humans (through evolution).

Model_hierarchies_Shaw_etal2016
Figure 1: Model hierarchy of midlatitude atmosphere (as used for studying storm tracks). The simplest models are on the left and the most complex models are on the right. Bottom panels show eddy kinetic energy (EKE, contours) and precipitation (shading) with increase in model hierarchy (left-to-right): No precipitation in a dry core model (left), zonally homogeneous EKE and precipitation in an aquaplanet model (middle), and zonally varying EKE and precipitation in the most complex model (right). Source: Shaw et al. (2016), Fig. B2.

Model hierarchies have now become an important research tool to further our understanding of the climate system [see, e.g., Polvani et al. (2017), Jeevanjee et al. (2017), Vallis et al. (2018)]. This approach allows us to delineate most important processes responsible for circulation response to climate change (e.g., mid-latitude storm track shift, widening of tropical belt etc.), to perform hypothesis testing, and to assess robustness of results in different configurations.

In my PhD, I have extensively used the model hierarchies concept to understand mid-latitude tropospheric dynamics (Fig. 1). One-layer barotropic and two-layer quasi-geostrophic models are often used as a first step to understand large-scale dynamics and to establish the importance of barotropic and baroclinic processes (also discussed in my previous blog post). Subsequently, more realistic “dry” non-linear multi-layer models with simple treatment for boundary layer and radiation [the so-called “Held & Suarez” setup, first introduced in Held and Suarez (1994)] can be used to study zonally homogeneous mid-latitude dynamics without complicating the setup with physical parametrisations (e.g. moist processes), or the full range of ocean-land-ice-atmosphere interactions. For example, I have successfully used the Held & Suarez setup to test the robustness of the annular mode variability (see my previous blog post) to different model climatologies (Boljka et al., 2018). I found that baroclinic annular mode timescale and its link to the barotropic annular mode is sensitive to model climatology. This can have an impact on climate variability in a changing climate.

Additional complexity can be introduced to the multi-layer dry models by adding moist processes and physical parametrisations in the so-called “aquaplanet” setup [e.g. Neale and Hoskins (2000)]. The aquaplanet setup allows us to elucidate the role of moist processes and parametrisations on zonally homogeneous dynamics. For example, mid-latitude cyclones tend to be stronger in moist atmospheres.

To study effects of zonal asymmetries on the mid-latitude dynamics, localized heating or topography can be further introduced to the aquaplanet and Held & Suarez setup to force large-scale stationary waves, reproducing the south-west to north-east tilts in the Northern Hemisphere storm tracks (bottom left panel in Fig. 1). This setup has helped me elucidate the differences between the zonally homogeneous and zonally inhomogeneous atmospheres, where the planetary scale (stationary) waves and their interplay with the synoptic eddies (cyclones) become increasingly important for the mid-latitude storm track dynamics and variability on different temporal and spatial scales.

Even further complexity can be achieved by coupling atmospheric models to the dynamic ocean and/or land and ice models (coupled atmosphere-ocean or atmosphere only GCMs, in Fig. 1), all of which bring the model closer to reality. However, interpreting results from such complex models is very difficult without having first studied the hierarchy of models as too many processes are acting simultaneously in such fully coupled models.  Further insights can also be gained by improving the theoretical (mathematical) understanding of the atmospheric processes by using a similar hierarchical approach [see e.g. Boljka and Shepherd (2018)].

References:

Boljka, L. and T.G. Shepherd, 2018: A multiscale asymptotic theory of extratropical wave–mean flow interaction. J. Atmos. Sci., 75, 1833–1852, https://doi.org/10.1175/JAS-D-17-0307.1 .

Boljka, L., T.G. Shepherd, and M. Blackburn, 2018: On the boupling between barotropic and baroclinic modes of extratropical atmospheric variability. J. Atmos. Sci., 75, 1853–1871, https://doi.org/10.1175/JAS-D-17-0370.1 .

Held, I. M., 2005: The gap between simulation and understanding in climate modeling. Bull. Am. Meteorol. Soc., 86, 1609 – 1614.

Held, I. M. and M. J. Suarez, 1994: A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. Bull. Amer. Meteor. Soc., 75, 1825–1830.

Jeevanjee, N., Hassanzadeh, P., Hill, S., Sheshadri, A., 2017: A perspective on climate model hierarchies. JAMES9, 1760-1771.

Neale, R. B., and B. J. Hoskins, 2000: A standard test for AGCMs including their physical parametrizations: I: the proposal. Atmosph. Sci. Lett., 1, 101–107.

Polvani, L. M., A. C. Clement, B. Medeiros, J. J. Benedict, and I. R. Simpson (2017), When less is more: Opening the door to simpler climate models. EOS, 98.

Shaw, T. A., M. Baldwin, E. A. Barnes, R. Caballero, C. I. Garfinkel, Y-T. Hwang, C. Li, P. A. O’Gorman, G. Riviere, I R. Simpson, and A. Voigt, 2016: Storm track processes and the opposing influences of climate change. Nature Geoscience, 9, 656–664.

Vallis, G. K., Colyer, G., Geen, R., Gerber, E., Jucker, M., Maher, P., Paterson, A., Pietschnig, M., Penn, J., and Thomson, S. I., 2018: Isca, v1.0: a framework for the global modelling of the atmospheres of Earth and other planets at varying levels of complexity. Geosci. Model Dev., 11, 843-859.

Presenting in Ponte Vedra, Florida – 33rd Conference on Hurricanes and Tropical Meteorology

Email: j.f.talib@pgr.reading.ac.uk

You’ve watched many speak before you. You’ve practised your presentation repeatedly. You’ve spent hours, days, months, and sometimes years, understanding your scientific work. Yet, no matter the audience’s size or specialism, the nerves always creep in before a presentation. It’s especially no different at your first international conference!

IMG_20180420_133234

Between the 16th and 20th April 2018, me, Jonathan Beverley and Bethan Harris were fortunate enough to attend and present at the American Meteorological Society 33rd Conference on Hurricanes and Tropical Meteorology in Ponte Vedra, Florida. For each of us, our first international conference!

Being a regular user of Instagram through the conference, especially the Instagram Story function, I was regularly asked by my friends back home, “what actually happens at a scientific conference”? Very simple really – scientists from around the world, from different departments, universities, and countries, come to share their work, in the hope of progressing the scientific field, to learn from one another, and network with future collaborators. For myself, it was an opportunity to present recently submitted work and to discuss with fellow researchers on the important questions that should be asked during the rest of my PhD. One outcome of my talk for example, was a two-hour discussion with a graduate student from Caltech, which not only improved my own work, but also helped me understand other research in global circulation.

Recordings of the presentations given by University of Reading PhD students can be found at:

Alongside presenting my own work, I had the opportunity to listen and learn from other scientific researchers. The conference had oral and poster presentations from a variety of tropical meteorology subject areas including hurricanes, global circulation, sub-seasonal forecasting, monsoons and Madden-Julian Oscillation. One of the things that I most enjoy at conferences is to hear from leading academics give an overview of certain topic or issue. For example, Kerry Emanuel spoke on the inferences that can be made from simple models of tropical convection. Through applying four key principles of tropical meteorology including the weak temperature gradient approximation and conservation of free-tropospheric moist static energy, we can understand tropical meteorology processes including the Intertropical Convergence Zone, Walker circulation and observed temperature and humidity profiles.

Of course, if you’re going to fly to the other side of the pond, you must take advantage of being in the USA. We saw a SPACEX rocket launch, (just at a distance of 150 miles away,) experienced travelling through a squall line, visited the launch sites of NASA’s first space programs, and explored the sunny streets of Miami. It was a great privilege to have the opportunity to present and attend the AMS 33rd Conference on Hurricanes and Tropical Meteorology, and I am hugely thankful to NERC SCENARIO DTP and the Department of Meteorology for funding my work and travel.

 

Baroclinic and Barotropic Annular Modes of Variability

Email: l.boljka@pgr.reading.ac.uk

Modes of variability are climatological features that have global effects on regional climate and weather. They are identified through spatial structures and the timeseries associated with them (so-called EOF/PC analysis, which finds the largest variability of a given atmospheric field). Examples of modes of variability include El Niño Southern Oscillation, Madden-Julian Oscillation, North Atlantic Oscillation, Annular modes, etc. The latter are named after the “annulus” (a region bounded by two concentric circles) as they occur in the Earth’s midlatitudes (a band of atmosphere bounded by the polar and tropical regions, Fig. 1), and are the most important modes of midlatitude variability, generally representing 20-30% of the variability in a field.

Southern_Hemi_Antarctica
Figure 1: Southern Hemisphere midlatitudes (red concentric circles) as annulus, region where annular modes have the largest impacts. Source.

We know two types of annular modes: baroclinic (based on eddy kinetic energy, a proxy for eddy activity and an indicator of storm-track intensity) and barotropic (based on zonal mean zonal wind, representing the north-south shifts of the jet stream) (Fig. 2). The latter are usually referred to as Southern (SAM or Antarctic Oscillation) or Northern (NAM or Arctic Oscillation) Annular Mode (depending on the hemisphere), have generally quasi-barotropic (uniform) vertical structure, and impact the temperature variations, sea-ice distribution, and storm paths in both hemispheres with timescales of about 10 days. The former are referred to as BAM (baroclinic annular mode) and exhibit strong vertical structure associated with strong vertical wind shear (baroclinicity), and their impacts are yet to be determined (e.g. Thompson and Barnes 2014, Marshall et al. 2017). These two modes of variability are linked to the key processes of the midlatitude tropospheric dynamics that are involved in the growth (baroclinic processes) and decay (barotropic processes) of midlatitude storms. The growth stage of the midlatitude storms is conventionally associated with increase in eddy kinetic energy (EKE) and the decay stage with decrease in EKE.

ThompsonWoodworth_Fig2a_SAM_2f_BAM(1)
Figure 2: Barotropic annular mode (right), based on zonal wind (contours), associated with eddy momentum flux (shading); Baroclinic annular mode (left), based on eddy kinetic energy (contours), associated with eddy heat flux (shading). Source: Thompson and Woodworth (2014).

However, recent observational studies (e.g. Thompson and Woodworth 2014) have suggested decoupling of baroclinic and barotropic components of atmospheric variability in the Southern Hemisphere (i.e. no correlation between the BAM and SAM) and a simpler formulation of the EKE budget that only depends on eddy heat fluxes and BAM (Thompson et al. 2017). Using cross-spectrum analysis, we empirically test the validity of the suggested relationship between EKE and heat flux at different timescales (Boljka et al. 2018). Two different relationships are identified in Fig. 3: 1) a regime where EKE and eddy heat flux relationship holds well (periods longer than 10 days; intermediate timescale); and 2) a regime where this relationship breaks down (periods shorter than 10 days; synoptic timescale). For the relationship to hold (by construction), the imaginary part of the cross-spectrum must follow the angular frequency line and the real part must be constant. This is only true at the intermediate timescales. Hence, the suggested decoupling of baroclinic and barotropic components found in Thompson and Woodworth (2014) only works at intermediate timescales. This is consistent with our theoretical model (Boljka and Shepherd 2018), which predicts decoupling under synoptic temporal and spatial averaging. At synoptic timescales, processes such as barotropic momentum fluxes (closely related to the latitudinal shifts in the jet stream) contribute to the variability in EKE. This is consistent with the dynamics of storms that occur on timescales shorter than 10 days (e.g. Simmons and Hoskins 1978). This is further discussed in Boljka et al. (2018).

EKE_hflux_cross_spectrum_blog
Figure 3: Imaginary (black solid line) and Real (grey solid line) parts of cross-spectrum between EKE and eddy heat flux. Black dashed line shows the angular frequency (if the tested relationship holds, the imaginary part of cross-spectrum follows this line), the red line distinguishes between the two frequency regimes discussed in text. Source: Boljka et al. (2018).

References

Boljka, L., and T. G. Shepherd, 2018: A multiscale asymptotic theory of extratropical wave, mean-flow interaction. J. Atmos. Sci., in press.

Boljka, L., T. G. Shepherd, and M. Blackburn, 2018: On the coupling between barotropic and baroclinic modes of extratropical atmospheric variability. J. Atmos. Sci., in review.

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How does plasma from the solar wind enter Earth’s magnetosphere?

Earth’s radiation belts are a hazardous environment for the satellites underpinning our everyday life. The behaviour of these high-energy particles, trapped by Earth’s magnetic field, is partly determined by the existence of plasma waves. These waves provide the mechanisms by which energy and momentum are transferred and particle populations physically moved around, and it’s some of these waves that I study in my PhD.

However, I’ve noticed that whenever I talk about my work, I rarely talk about where this plasma comes from. In schools it’s often taught that space is a vacuum, and while it is closer to a vacuum than anything we can make on Earth, there are enough particles to make it a dangerous environment. A significant amount of particles do escape from Earth’s ionosphere into the magnetosphere but in this post I’ll focus on material entering from the solar wind. This constant outflow of hot particles from the Sun is a plasma, a fluid where enough of the particles are ionised that the behaviour of the fluid is then dominated by electric and magnetic fields. Since the charged particles in a plasma interact with each other, with external electric and magnetic fields, and also generate more fields by moving and interacting, this makes for some weird and wonderful behaviour.

magnetosphere_diagram
Figure 1: The area of space dominated by Earth’s magnetic field (the magnetosphere) is shaped by the constant flow of the solar wind (a plasma predominantly composed of protons, electrons and alpha particles). Plasma inside the magnetosphere collects in specific areas; the radiation belts are particularly of interest as particles there pose a danger to satellites. Credit: NASA/Goddard/Aaron Kaas

When explaining my work to family or friends, I often describe Earth’s magnetic field as a shield to the solar wind. Because the solar wind is well ionised, it is highly conductive, and this means that approximately, the magnetic field is “frozen in” to the plasma. If the magnetic field changes, the plasma follows this change. Similarly, if the plasma flows somewhere, the magnetic field is dragged along with it. (This is known as Alfvén’s frozen in theorem – the amount of plasma in a volume parallel to the magnetic field line remains constant). And this is why the magnetosphere acts as shield to all this energy streaming out of the Sun – while the magnetic field embedded in the solar wind is topologically distinct from the magnetic field of the Earth, there is no plasma transfer across magnetic field lines, and it streams past our planet (although this dynamic pressure still compresses the plasma of the magnetosphere, giving it that typical asymmetric shape in Figure 1).

Of course, the question still remains of how the solar wind plasma enters the Earth’s magnetic field if such a shielding effect exists. You may have noticed in Figure 1 that there are gaps in the shield that the Earth’s dipole magnetic field presents to the solar wind; these are called the cusps, and at these locations the magnetic field connects to the solar wind. Here, plasma can travel along magnetic field lines and impact us on Earth.

But there’s also a more interesting phenomenon occurring – on a small enough scale (i.e. the very thin boundaries between two magnetic domains) the assumptions behind the frozen-in theorem break down, and then we start to see one of the processes that make the magnetosphere such a complex, fascinating and dynamic system to study. Say we have two regions of plasma with opposing orientation of the magnetic field. Then in a middle area these opposing field lines will suddenly snap to a new configuration, allowing them to peel off and away from this tightly packed central region. Figure 2 illustrates this process – you can see that after pushing red and blue field lines together, they suddenly jump to a new configuration. As well as changing the topology of the magnetic field, the plasma at the centre is energised and accelerated, shooting off along the magnetic field lines. Of course even this is a simplification; the whole process is somewhat more messy in reality and I for one don’t really understand how the field can suddenly “snap” to a new configuration.

reconnection
Figure 2: Magnetic reconnection. Two magnetic domains of opposing orientation can undergo a process where the field line configuration suddenly resets. Instead of two distinct magnetic domains, some field lines are suddenly connected to both, and shoot outwards and away, as does the energised plasma.

In the Earth’s magnetosphere there are two main regions where this process is important (Figure 3). Firstly, at the nose of the magnetosphere. The dynamic pressure of the solar wind is compressing the solar wind plasma against the magnetospheric plasma, and when the interplanetary magnetic field is orientated downwards (i.e. opposite to the Earth’s dipole – about half the time) this reconnection can happen. At this point field lines that were solely connected to the Earth or in the solar wind are now connected to both, and plasma can flow along them.

magnetosphere_reconnection_sites
Figure 3: There are two main areas where reconnection happens in Earth’s magnetosphere. Opposing field lines can reconnect, allowing a continual dynamic cycle (the Dungey cycle) of field lines around the magnetosphere. Plasma can travel along these magnetic field lines freely. Credits: NASA/MMS (image) and NASA/Goddard Space Flight Center- Conceptual Image Lab (video)

Then, as the solar wind continues to rush outwards from the Sun, it drags these field lines along with it, past the Earth and into the tail of the magnetosphere. Eventually the build-up of these field lines reaches a critical point in the tail, and boom! Reconnection happens once more. You get a blast of energised plasma shooting along the magnetic field (this gives us the aurora) and the topology has rearranged to separate the magnetic fields of the Earth and solar wind; once more, they are distinct. These dipole field lines move around to the front of the Earth again, to begin this dramatic cycle once more.

Working out when and how these kind of processes take place is still an active area of research, let alone understanding exactly what we expect this new plasma to do when it arrives. If it doesn’t give us a beautiful show of the aurora, will it bounce around the radiation belts, trapped in the stronger magnetic fields near the Earth? Or if it’s not so high energy as that, will it settle in the cooler plasmasphere, to rotate with the Earth and be shaped as the magnetic field is distorted by solar wind variations? Right now I look out my window at a peaceful sunny day and find it incredible that such complicated and dynamic processes are continually happening so (relatively) nearby. It certainly makes space physics an interesting area of research.