Atmospheric blocking: why is it so hard to predict?

Atmospheric blocks are nearly stationary large-scale flow features that effectively block the prevailing westerly winds and redirect mobile cyclones. They are typically characterised by a synoptic-scale, quasi-stationary high pressure system in the midlatitudes that can remain over a region for several weeks. Blocking events can cause extreme weather: heat waves in summer and cold spells in winter, and the impacts associated with these events can escalate due to a block’s persistence. Because of this, it is important that we can forecast blocking accurately. However, atmospheric blocking has been shown to be the cause of some of the poorest forecasts in recent years. Looking at all occasions when the ECMWF model experienced a period of very low forecast skill, Rodwell et al. (2013) found that the average flow pattern for which these forecasts verified was an easily-distinguishable atmospheric blocking pattern (Figure 1). But why are blocks so hard to forecast?

Fig_1_blogjacob
Figure 1:  Average verifying 500 hPa geopotential height (Z500) field for occasions when the ECMWF model experienced very low skill. From Rodwell et al. (2013).

There are several reasons why forecasting blocking is a challenge. Firstly, there is no universally accepted definition of what constitutes a block. Several different flow configurations that could be referred to as blocks are shown in Figure 2. The variety in flow patterns used to define blocking brings with it a variety of mechanisms that are dynamically important for blocks developing in a forecast (Woollings et al. 2018). Firstly, many phenomena must be well represented in a model for it to forecast all blocking events accurately. Secondly, there is no complete dynamical theory for block onset and maintenance- we do not know if a process key for blocking dynamics is missing from the equation set solved by numerical weather prediction models and is contributing to the forecast error. Finally, many of the known mechanisms associated with block onset and maintenance are also know sources of model uncertainty. For example, diabatic processes within extratropical cyclones have been shown to contribute substantially to blocking events (Pfahl et al. 2015), the parameterisation of which has been shown to affect medium-range forecasts of ridge building events (Martínez-Alvarado et al. 2015).

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Figure 2: Different flow patterns, shown using Z500 (contours), that have been defined as blocks. From Woollings et al. (2018).

We do, however, know some ways to improve the representation of blocking: increase the horizontal resolution of the model (Schiemann et al. 2017); improve the parameterisation of subgrid physical processes (Jung et al. 2010); remove underlying model biases (Scaife et al. 2010); and in my PhD we found that improvements to a model’s dynamical core (the part of the model used to solved the governing equations) can also improve the medium-range forecast of blocking. In Figure 3, the frequency of blocking that occurred during two northern hemisphere winters is shown for the ERA-Interim reanalysis and three operational weather forecast centres (the ECMWF, Met Office (UKMO) and the Korean Meteorological Administration (KMA)). Both KMA and UKMO use the Met Office Unified Model – however, before the winter of 2014/15 the UKMO updated the model to use a new dynamical core whilst KMA continued to use the original. This means that for the 2013/14 the UKMO and KMA forecasts are from the same model with the same dynamical core whilst for the 2014/15 winter the UKMO and KMA forecasts are from the same model but with different dynamical cores. The clear improvement in forecast from the UKMO in 2014/15 can hence be attributed to the new dynamical core. For a full analysis of this improvement see Martínez-Alvarado et al. (2018).

Fig_3_blogjacob
Figure 3: The frequency of blocking during winter in the northern hemisphere in ERA-Interim (grey shading) and in seven-day forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Met Office (UKMO) and the Korean Meteorological Administration (KMA). Box plots show the spread in the ensemble forecast from each centre.

In the remainder of my PhD I aim to investigate the link between errors in forecasts of blocking with the representation of upstream cyclones. I am particularly interested to see if the parameterisation of diabatic processes (a known source of model uncertainty) could be causing the downstream error in Rossby wave amplification and blocking.

Email: j.maddison@pgr.reading.ac.uk.

References:

Rodwell, M. J., and Coauthors, 2013: Characteristics of occasional poor medium-range weather  forecasts for Europe. Bulletin of the American Meteorological Society, 94 (9), 1393–1405.

Woollings, T., and Coauthors, 2018: Blocking and its response to climate change. Current Climate Change Reports, 4 (3), 287–300.

Pfahl, S., C. Schwierz, M. Croci-Maspoli, C. Grams, and H. Wernli, 2015: Importance of latent  heat release in ascending air streams for atmospheric blocking. Nature Geoscience, 8 (8), 610– 614.

Mart´ınez-Alvarado, O., E. Madonna, S. Gray, and H. Joos, 2015: A route to systematic error in forecasts of Rossby waves. Quart. J. Roy. Meteor. Soc., 142, 196–210.

Mart´ınez-Alvarado, O., and R. Plant, 2014: Parametrized diabatic processes in numerical simulations of an extratropical cyclone. Quart. J. Roy. Meteor. Soc., 140 (682), 1742–1755.

Scaife, A. A., T. Woollings, J. Knight, G. Martin, and T. Hinton, 2010: Atmospheric blocking and mean biases in climate models. Journal of Climate, 23 (23), 6143–6152.

Schiemann, R., and Coauthors, 2017: The resolution sensitivity of northern hemisphere blocking in four 25-km atmospheric global circulation models. Journal of Climate, 30 (1), 337–358.

Jung, T., and Coauthors, 2010: The ECMWF model climate: Recent progress through improved physical parametrizations. Quart. J. Roy. Meteor. Soc., 136 (650), 1145–1160.

Communicating uncertainties associated with anthropogenic climate change

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

This week Prof. Ed Hawkins from the Department of Meteorology and NCAS-Climate gave a University of Reading public lecture discussing the science of climate change. A plethora of research was presented, all highlighting that humans are changing our climate. As scientists we can study the greenhouse effect in scientific labs, observe increasing temperatures across the majority of the planet, or simulate the impact of human actions on the Earth’s climate through using climate models.

simulating_temperature_rise
Figure 1. Global-mean surface temperature in observations (solid black line), and climate model simulations with (red shading) and without (blue shading) human actions. Shown during Prof. Ed Hawkins’ University of Reading Public Lecture.

Fig. 1, presented in Ed Hawkins’ lecture, shows the global mean temperature rise associated with human activities. Two sets of climate simulations have been performed to produce this plot. The first set, shown in blue, are simulations controlled solely by natural forcings, i.e. variations in radiation from the sun and volcanic eruptions. The second, shown in red, are simulations which include both natural forcing and forcing associated with greenhouse gas emissions from human activities. The shading indicates the spread amongst climate models, whilst the observed global-mean temperature is shown by the solid black line. From this plot it is evident that all climate models attribute the rising temperatures over the 20th and 21st century to human activity. Climate simulations without greenhouse gas emissions from human activity indicate a much smaller rise, if any, in global-mean temperature.

However, whilst there is much agreement amongst climate scientists and climate models that our planet is warming due to human activity, understanding the local impact of anthropogenic climate change contains its uncertainties.

For example, my PhD research aims to understand what controls the location and intensity of the Intertropical Convergence Zone. The Intertropical Convergence Zone is a discontinuous, zonal precipitation band in the tropics that migrates meridionally over the seasonal cycle (see Fig. 2). The Intertropical Convergence Zone is associated with wet and dry seasons over Africa, the development of the South Asian Monsoon and the life-cycle of tropical cyclones. However, currently our climate models struggle to simulate characteristics of the Intertropical Convergence Zone. This, alongside other issues, results in climate models differing in the response of tropical precipitation to anthropogenic climate change.

animation
Figure 2. Animation showing the seasonal cycle of the observed monthly-mean precipitation rates between 1979-2014.

Figure 3 is a plot taken from a report written by the Intergovernmental Panel on Climate Change (Climate Change 2013: The Physical Science Basis). Both maps show the projected change from climate model simulations in Northern Hemisphere winter precipitation between the years 2016 to 2035 (left) and 2081 to 2100 (right) relative to 1986 to 2005 under a scenario where minimal action is taken to limit greenhouse gas emissions (RCP8.5) . Whilst the projected changes in precipitation are an interesting topic in their own right, I’d like to draw your attention to the lines and dots annotated on each map. The lines indicate where the majority of climate models agree on a small change. The map on the left indicates that most climate models agree on small changes in precipitation over the majority of the globe over the next two decades. Dots, meanwhile, indicate where climate models agree on a substantial change in Northern Hemisphere winter precipitation. The plot on the right indicates that across the tropics there are substantial areas where models disagree on changes in tropical precipitation due to anthropogenic climate change. Over the majority of Africa, South America and the Maritime Continent, models disagree on the future of precipitation due to climate change.

IPCC_plot
Figure 3. Changes in Northern Hemisphere Winter Precipitation between 2016 to 2035 (left) and 2081 to 2100 (right) relative to 1986 to 2005 under a scenario with minimal reduction in anthropogenic greenhouse gas emission. Taken from IPCC – Climate Change 2013: The Physical Science Basis.

How should scientists present these uncertainties?

I must confess that I am nowhere near an expert in communicating uncertainties, however I hope some of my thoughts will encourage a discussion amongst scientists and users of climate data. Here are some of the ideas I’ve picked up on during my PhD and thoughts associated with them:

  • Climate model average – Take the average amongst climate model simulations. With this method though you take the risk of smoothing out large positive and negative trends. The climate model average is also not a “true” projection of changes due to anthropogenic climate change.
  • Every climate model outcome – Show the range of climate model projections to the user. Here you face the risk of presenting the user with too much climate data. The user may also trust certain model outputs which suit their own agenda.
  • Storylines – This idea was first shown to me in a paper by Zappa, G. and Shepherd, T. G., (2017). You present a series of storylines in which you highlight the key processes that are associated with variability in the regional weather pattern of interest. Each change in the set of processes leads to a different climate model projection. However, once again, the user of the climate model data has to reach their own conclusion on which projection to take action on.
  • Probabilities with climate projections – Typically with short- and medium-range weather forecasts probabilities are used to support the user. These probabilities are generated by re-performing the simulations, each with either different initial conditions or a slight change in model physics, to see the percentage of simulations that agree on model output. However, with climate model simulations, it is slightly more difficult to associate probabilities with projections. How do you generate the probabilities? Climate models have similarities in the methods which they use to represent the physics of our atmosphere and therefore you don’t want the probabilities associated with each climate projection due to similarity amongst climate model set-up. You could base the probabilities on how well the climate model simulates the past, however just because a model simulates the past correctly, doesn’t mean it will correctly simulate the forcing in the future.

There is much more that can be said about communicating uncertainty among climate model projections – a challenge which will continue for several decades. As climate scientists we can sometimes fall into the trap on concentrating on uncertainties. We need to keep on presenting the work that we are confident about, to ensure that the right action is taken to mitigate against anthropogenic climate change.

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.

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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.

Oceans in Weather and Climate Course 2018

email: r.frew@pgr.reading.ac.uk

Between the 11th-16th March myself and four other PhDs and post docs attended the Ocean in Weather and Climate (OiWC) course at the Met Office, Exeter. This NERC advanced training course was aimed at PhDs, postdocs and beyond. It provided a great opportunity to spend a week meeting other Oceanography researchers at varying stages of their career, and to expand your understanding of the oceans role in climate beyond the scope of your own work.

The week kicked off with an ice breaker where we had do some ‘Scientific speed dating’, chatting to other participants about: Where are you from? What do you work on? What is your main hobby? What is the biggest question in your field of research? This set the tone for a very interactive week full of interesting discussions between all attendees and speakers alike. Course participants were accommodated at The Globe Inn situated in Topsham, a cute village-sized town full of pastel-coloured houses, cosy pubs, art galleries, and beautiful riverside walks to stretch your legs in the evenings.

The days consisted of four 1.5 hour sessions, split up by caffeine and biscuit breaks to recharge before the next session.

Topics covered in the lecture-style talks included…

  • Dynamical Theory
  • Modelling the Ocean
  • Observations
  • Ocean-atmosphere coupling
  • Air-sea fluxes
  • High Resolution Ocean modelling in coupled forecast systems
  • The Meridional Overturning Circulation
  • The Southern Ocean in climate and climatic change
  • Climate variability on diurnal, seasonal, annual, inter-annual, decadal timescales
  • Climate extremes
  • Climate sensitivity, heat uptake and sea level.
OceanResolutionFigure
A recurring figure of the week…. taken from Helene Hewitt’s talk on high resolution ocean modelling showing ocean surface currents from HadGEM3-based global coupled models at different resolutions (eddy resolving, eddy permitting and eddy parameterised).

 

All the talks were very interesting and were followed by some stimulating discussion. Each session provided an overview of each topic and an indication of the current research questions in each area at the moment.

In the post lunch session, there were group practical sessions. These explored observational ARGO float data and model output. The practicals, written in iPython notebooks, were designed to let us play with some data, giving us a series of questions to trigger group discussions to deepen understanding of topics covered that morning.

The course also included some ‘softer’ evening talks, giving research career advice in a more informal manner. Most evenings were spent exploring the lovely riverside walks and restaurants/pubs of Topsham. The final evening was spent all together at the Cosy Club in Exeter, rounding off a very interesting and enjoyable week!

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.

Marshall, G. J., D. W. J. Thompson, and M. R. van den Broeke, 2017: The signature of Southern Hemisphere atmospheric circulation patterns in Antarctic precipitation. Geophys. Res. Lett., 44, 11,580–11,589.

Simmons, A. J., and B. J. Hoskins, 1978: The life cycles of some nonlinear baroclinic waves. J. Atmos. Sci., 35, 414–432.

Thompson, D. W. J., and E. A. Barnes, 2014: Periodic variability in the large-scale Southern Hemisphere atmospheric circulation. Science, 343, 641–645.

Thompson, D. W. J., B. R. Crow, and E. A. Barnes, 2017: Intraseasonal periodicity in the Southern Hemisphere circulation on regional spatial scales. J. Atmos. Sci., 74, 865–877.

Thompson, D. W. J., and J. D. Woodworth, 2014: Barotropic and baroclinic annular variability in the Southern Hemisphere. J. Atmos. Sci., 71, 1480–1493.