Tropical

How Important are Post-Tropical Cyclones to European Windstorm Risk?

Elliott SainsburyLeave a comment

Elliott Sainsbury, e.sainsbury@pgr.reading.ac.uk

To date, the 2020 North Atlantic hurricane season has been the most active on record, producing 20 named storms, 7 hurricanes, and a major hurricane which caused $9 billion in damages across the southern United States. With the potential for such destructive storms, it is understandable that a large amount of attention is paid to the North Atlantic basin at this time of year. Whilst hurricanes have been known to cause devastation in the tropics for centuries, until recently there was little appreciation for the destructive potential of these systems across Europe.

As tropical cyclones such as hurricanes move poleward – away from the tropics and into regions of lower sea surface temperatures and higher vertical wind shear, they undergo a process called extratropical transition (Klein et al., 2000): Over a period of time, the cyclones change from symmetric, warm cored systems into asymmetric cold core systems fuelled by horizontal temperature gradients, as opposed to latent heat fluxes (Evans et al., 2017). These systems, so-called post-tropical cyclones (PTCs), often reintensify in the mid-latitude Atlantic with consequences for land masses downstream – often Europe. This was highlighted in 2017, when ex-hurricane Ophelia impacted Ireland, bringing with it the strongest winds Ireland had seen in 50 years (Stewart, 2018). 3 people were killed, and 360,000 homes were without power.

In a recent paper, we quantify the risk associated with PTCs across Europe relative to mid-latitude cyclones (MLCs) for the first time – in terms of both the absolute risk (i.e. what fraction of high impact wind events across Europe are caused by PTCs?) and also the relative risk (for a given PTC, how likely is it to be associated with high-impact winds, and how does this compare to a given MLC?). By tracking all cyclones impacting a European domain (36-70N, 10W-30E) in the ERA5 reanalysis (1979-2017) using a feature tracking algorithm (Hodges, 1994, 1995, 1999), we identify the post-tropical cyclones using spatiotemporal matching (Hodges et al., 2017) with the observational record, IBTrACS (Knapp et al., 2010).

Figure 1: Distributions of the maximum intensity (maximum wind speed, minimum MSLP) attained by each PTC and MLC inside (a-c) the whole European domain (36-70N, 10W-30E), (d-f) the Northern Europe domain (48-70N, 10W-30E) and (g-i) the Southern Europe domain (36-48N, 10W-30E), using cyclones tracked through the ERA5 reanalysis all year round, 1979-2017. [Figure 2 in Sainsbury et al. 2020].

Figure 1 shows the distributions of maximum intensity for PTCs and MLCs across the entire European domain (top), Northern Europe (48-70°N, 10°W-30°E; middle) and Southern Europe (36-48°N, 10°W-30°E; bottom), using all cyclone tracks all year round. The distribution of PTC maximum intensities is higher (in terms of both wind speed and MSLP) than MLCs, particularly across Northern Europe. The difference between the maximum intensity distributions of PTCs and MLCs across Northern Europe is statistically significant (99%). PTCs are also present in the highest of intensity bins, indicating that the strongest PTCs have intensities comparable to strong wintertime MLCs.

Whilst Figure 1 shows that PTCs are stronger than MLCs even when considering MLCs forming all year round (including the often much stronger wintertime MLCs), it is also useful to compare the risks posed by PTCs relative to MLCs forming at the same time of the year – during the North Atlantic hurricane season (June 1st-November 30th).

Figure 2 shows the fraction of all storms, binned by their maximum intensity in their respective domains, which are PTCs. For storms with weak-moderate maximum winds (first three bins in the figure), <1% of such events are caused by PTCs (with the remaining 99% caused by MLCs). For stronger storms, particularly those of storm force (>25 ms-1 on the Beaufort scale), this percentage is much higher. Despite less than 1% of all storms impacting Northern Europe during hurricane season being PTCs, almost 9% of all storms with storm-force winds which impact the region are PTCs, suggesting that a disproportionate fraction of high-impact windstorms are PTCs. 8.2% of all Northern Europe impacting PTCs which form during hurricane season impact the region with storm-force winds. This fraction is only 0.8% for MLCs, suggesting that the fraction of PTCs impacting Northern Europe with storm-force winds is approximately 10 times greater than MLCs.

Figure 2: The fraction of cyclones impacting Europe which are PTCs as a function of their maximum 10m wind speed in their respective domain. Lower bound of wind speed is shown on the x axis, bin width = 3. Error bars show the 95% confidence interval. All cyclone tracks forming during the North Atlantic hurricane season are used. [Figure 4 in Sainsbury et al. 2020].

Here we have shown that PTCs, at their maximum intensity over Northern Europe, are stronger than MLCs. However, the question still remains as to why this is the case. Warm-seclusion storms post-extratropical transition have been shown to have the fastest rates of reintensification (Kofron et al., 2010) and typically have the lowest pressures upon impacting Europe (Dekker et al., 2018). Given the climatological track that PTCs often take over the warm waters of the Gulf stream, along with the contribution of both baroclinic instability and latent heat release for warm-seclusion development (Baatsen et al., 2015), one hypothesis may be that PTCs are more likely to develop into warm seclusion storms than the broader class of mid-latitude cyclones, potentially explaining the disproportionate impacts they cause across Europe. This will be the topic of future work.

Despite PTCs disproportionately impacting Europe with high intensities, they are a relatively small component of the total cyclone risk in the current climate. However, only small changes are expected in MLC number and intensity under RCP 4.5 (Zappa et al., 2013). Conversely, the number of hurricane-force (>32.6 ms-1) storms impacting Norway, the North Sea and the Gulf of Biscay has been projected to increases by a factor of 6.5, virtually all of which originate in the tropics (Haarsma et al., 2013). Whilst the absolute contribution of PTCs to hurricane season windstorm risk is currently low, PTCs may make an increasingly significant contribution to European windstorm risk in a future climate.

Interested to read more? Read our paper, published in Geophysical Research Letters.

Sainsbury, E. M., R. K. H. Schiemann, K. I. Hodges, L. C. Shaffrey, A. J. Baker, K. T. Bhatia, 2020: How Important Are Post‐Tropical Cyclones for European Windstorm Risk? Geophysical Research Letters, 47(18), e2020GL089853, https://doi.org/10.1029/2020GL089853

References

Baatsen, M., Haarsma, R. J., Van Delden, A. J., & de Vries, H. (2015). Severe Autumn storms in future Western Europe with a warmer Atlantic Ocean. Climate Dynamics, 45, 949–964. https://doi.org/10.1007/s00382-014-2329-8

Dekker, M. M., Haarsma, R. J., Vries, H. de, Baatsen, M., & Delden, A. J. va. (2018). Characteristics and development of European cyclones with tropical origin in reanalysis data. Climate Dynamics, 50(1), 445–455. https://doi.org/10.1007/s00382-017-3619-8

Evans, C., Wood, K. M., Aberson, S. D., Archambault, H. M., Milrad, S. M., Bosart, L. F., et al. (2017). The extratropical transition of tropical cyclones. Part I: Cyclone evolution and direct impacts. Monthly Weather Review, 145(11), 4317–4344. https://doi.org/10.1175/MWR-D-17-0027.1

Haarsma, R. J., Hazeleger, W., Severijns, C., De Vries, H., Sterl, A., Bintanja, R., 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

Hodges, K., Cobb, A., & Vidale, P. L. (2017). How well are tropical cyclones represented in reanalysis datasets? Journal of Climate, 30(14), 5243–5264. https://doi.org/10.1175/JCLI-D-16-0557.1

Hodges, K. I. (1994). A general method for tracking analysis and its application to meteorological data. Monthly Weather Review, 122(11), 2573–2586. https://doi.org/10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2

Hodges, K. I. (1995). Feature Tracking on the Unit Sphere. Monthly Weather Review, 123(12), 3458–3465. https://doi.org/10.1175/1520-0493(1995)123<3458:ftotus>2.0.co;2

Hodges, K. I. (1999). Adaptive constraints for feature tracking. Monthly Weather Review, 127(6), 1362–1373. https://doi.org/10.1175/1520-0493(1999)127<1362:acfft>2.0.co;2

Klein, P. M., Harr, P. A., & Elsberry, R. L. (2000). Extratropical transition of western North Pacific tropical cyclones: An overview and conceptual model of the transformation stage. Weather and Forecasting, 15(4), 373–395. https://doi.org/10.1175/1520-0434(2000)015<0373:ETOWNP>2.0.CO;2

Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., & Neumann, C. J. (2010). The international best track archive for climate stewardship (IBTrACS). Bulletin of the American Meteorological Society, 91(3), 363–376. https://doi.org/10.1175/2009BAMS2755.1

Kofron, D. E., Ritchie, E. A., & Tyo, J. S. (2010). Determination of a consistent time for the extratropical transition of tropical cyclones. Part I: Examination of existing methods for finding “ET Time.” Monthly Weather Review, 138(12), 4328–4343. https://doi.org/10.1175/2010MWR3180.1

Stewart, S. R. (2018). Tropical Cyclone Report: Hurricane Ophelia. National Hurricane Center, (February), 1–32. https://doi.org/AL142016

Zappa, G., Shaffrey, L. C., Hodges, K. I., Sansom, P. G., & Stephenson, D. B. (2013). A multimodel assessment of future projections of North Atlantic and European extratropical cyclones in the CMIP5 climate models. Journal of Climate, 26(16), 5846–5862. https://doi.org/10.1175/JCLI-D-12-00573.1

How much energy is available in a moist atmosphere?

Bethan Harris1 Comment

Email: b.l.harris@pgr.reading.ac.uk

It is often useful to know how much energy is available to generate motion in the atmosphere, for example in storm tracks or tropical cyclones. To this end, Lorenz (1955) developed the theory of Available Potential Energy (APE), which defines the part of the potential energy in the atmosphere that could be converted into kinetic energy.

To calculate the APE of the atmosphere, we first find the minimum total potential energy that could be obtained by adiabatic motion (no heat exchange between parcels of air). The atmospheric setup that gives this minimum is called the reference state. This is illustrated in Figure 1: in the atmosphere on the left, the denser air will move horizontally into the less dense air, but in the reference state on the right, the atmosphere is stable and no motion would occur. No further kinetic energy is expected to be generated once we reach the reference state, and so the APE of the atmosphere is its total potential energy minus the total potential energy of the reference state.

Figure 1: Construction of the APE reference state for a 2D atmosphere. The purple shading indicates the density of the air; darker colours mean denser air. In the actual state, the density stratification is not completely horizontal, which leads to the air motion shown by the orange arrows. The reference state has a stable, horizontal density stratification, so the air will not move without some disturbance.

If we think about an atmosphere that only varies in the vertical direction, it is easy to find the reference state if the atmosphere is dry. We assume that the atmosphere consists of a number of air parcels, and then all we have to do is place the parcels in order of increasing potential temperature with height. This ensures that density decreases upwards, so we have a stable atmosphere.

However, if we introduce water vapour into the atmosphere, the situation becomes more complicated. When water vapour condenses, latent heat is released, which increases the temperature of the air, decreasing its density. One moist air parcel can be denser than another at a certain height, but then less dense if they are lifted to a height where the first parcel condenses but the second one does not. The moist reference state therefore depends on the exact method used to sort the parcels by their density.

It is possible to find the rearrangement of the moist air parcels that gives the minimum possible total potential energy, using the Munkres (1957) sorting algorithm, but this takes a very long time for a large number of parcels. Lots of different sorting algorithms have therefore been developed that try to find an approximate moist reference state more quickly (the different types of algorithms are explained by Stansifer (2017) and Harris and Tailleux (2018)). However, these sorting algorithms do not try to analyse whether the parcel movements they are simulating could actually happen in the real atmosphere—for example, many work by lifting all parcels to a fixed level in the atmosphere, without considering whether the parcels could feasibly move there—and there has been little understanding of whether the reference states they find are accurate.

As part of my PhD, I have performed the first assessment of these sorting algorithms across a wide range of atmospheric data, using over 3000 soundings from both tropical island and mid-latitude continental locations (Harris and Tailleux, 2018). This showed that whilst some of the sorting algorithms can provide a good estimate of the minimum potential energy reference state, others are prone to computing a rearrangement that actually has a higher potential energy than the original atmosphere.

We also showed that a new algorithm, which does not rely on sorting procedures, can calculate APE with comparable accuracy to the sorting algorithms. This method finds a layer of near-surface buoyant parcels, and performs the rearrangement by lifting the layer upwards until it is no longer buoyant. The success of this method suggests that we do not need to rely on possibly unphysical sorting algorithms to calculate moist APE, but that we can move towards approaches that consider the physical processes generating motion in a moist atmosphere.

References

Harris, B. L. and R. Tailleux, 2018: Assessment of algorithms for computing moist available potential energy. Q. J. R. Meteorol. Soc., 144, 1501–1510, https://doi.org/10.1002/qj.3297

Lorenz, E. N., 1955: Available potential energy and the maintenance of the general circulation. Tellus, 7, 157–167, https://doi.org/10.3402/tellusa.v7i2.8796

Munkres, J., 1957: Algorithms for the Assignment and Transportation Problems. J. Soc. Ind. Appl. Math., 5, 32–38, https://doi.org/10.1137/0105003

Stansifer, E. M., P. A. O’Gorman, and J. I. Holt, 2017: Accurate computation of moist available potential energy with the Munkres algorithm. Q. J. R. Meteorol. Soc., 143, 288–292, https://doi.org/10.1002/qj.2921

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

The Social MetworkLeave a comment

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.

 

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

The Social MetworkLeave a comment

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.

 

Trouble in paradise: Climate change, extreme weather and wildlife conservation on a tropical island.

Social Metwork GuestLeave a comment

Joseph Taylor, NERC SCEARNIO DTP student. Zoological Society of London.

Email: J.Taylor5@pgr.reading.ac.uk

Projecting the impacts of climate change on biodiversity is important for informing

Mauritius Kestrel by Joe Taylor
Male Mauritius kestrel (Falco punctatus) in the Bambous Mountains, eastern Mauritius. Photo by Joe Taylor.

mitigation and adaptation strategies. There are many studies that project climate change impacts on biodiversity; however, changes in the occurrence of extreme weather events are often omitted, usually because of insufficient understanding of their ecological impacts. Yet, changes in the frequency and intensity of extreme weather events may pose a greater threat to ecosystems than changes in average weather regimes (Jentsch and Beierkuhnlein 2008). Island species are expected to be particularly vulnerable to climate change pressures, owing to their inherently limited distribution, population size and genetic diversity, and because of existing impacts from human activities, including habitat destruction and the introduction of non-native species (e.g. Fordham and Brook 2010).

Mauritius is an icon both of species extinction and the successful recovery of threatened species. However, the achievements made through dedicated conservation work and the investment of substantial resources may be jeopardised by future climate change. Conservation programmes in Mauritius have involved the collection of extensive data on individual animals, creating detailed longitudinal datasets. These provide the opportunity to conduct in-depth analyses into the factors that drive population trends.

My study focuses on the demographic impacts of weather conditions, including extreme events, on three globally threatened bird species that are endemic to Mauritius. I extended previous research into weather impacts on the Mauritius kestrel (Falco punctatus), and applied similar methods to the echo parakeet (Psittacula eques) and Mauritius fody (Foudia rubra). The kestrel and parakeet were both nearly lost entirely in the 1970s and 1980s respectively, having suffered severe population bottlenecks, but all three species have benefitted from successful recovery programmes. I analysed breeding success using generalised linear mixed models and analysed survival probability using capture-mark-recapture models. Established weather indices were adapted for use in this study, including indices to quantify extreme rainfall, droughts and tropical cyclone activity. Trends in weather indices at key conservation sites were also analysed.

The results for the Mauritius kestrel add to a body of evidence showing that precipitation is an important limiting factor in its demography and population dynamics. The focal population in the Bambous Mountains of eastern Mauritius occupies an area in which rainfall is increasing. This trend could have implications for the population, as my analyses provide evidence that heavy rainfall during the brood phase of nests reduces breeding success, and that prolonged spells of rain in the cyclone season negatively impact the survival of juveniles. This probably occurs through reductions in hunting efficiency, time available for hunting and prey availability, so that kestrels are unable to capture enough prey to sustain themselves and feed their young (Nicoll et al. 2003, Senapathi et al. 2011). Exposure to heavy and prolonged rainfall could also be a direct cause of mortality through hypothermia, especially for chicks if nests are flooded (Senapathi et al. 2011). Future management of this species may need to incorporate strategies to mitigate the impacts of increasing rainfall.

References:

Fordham, D. A. and Brook, B. W. (2010) Why tropical island endemics are acutely susceptible to global change. Biodiversity and Conservation 19(2): 329‒342.

Jentsch, A. and Beierkuhnlein, C. (2008) Research frontiers in climate change: Effects of extreme meteorological events on ecosystems. Comptes Rendus Geoscience 340: 621‒628.

Nicoll, M. A. C., Jones, C. G. and Norris, K. (2003) Declining survival rates in a reintroduced population of the Mauritius kestrel: evidence for non-linear density dependence and environmental stochasticity. Journal of Animal Ecology 72: 917‒926.

Senapathi, D., Nicoll, M. A. C., Teplitsky, C., Jones, C. G. and Norris, K. (2011) Climate change and the risks associated with delayed breeding in a tropical wild bird population. Proceedings of the Royal Society B 278: 3184‒3190.

Tropical Circulation viewed as a heat engine

Jan KamienieckiLeave a comment

Climate scientists have a lot of insight into the factors driving weather systems in the mid-latitudes, where the rotation of the earth is an important influence. The tropics are less well served, and this can be a problem for global climate models which don’t capture many of the phenomena observed in the tropics that well.

What we do know about the tropics however is that despite significant contrasts in sea surface temperatures (Fig. 1) there is very little horizontal temperature variation in the atmosphere (Fig. 2) – because the Coriolis force (due to the Earth’s rotation) that enables this gradient in more temperate climates is not present. We believe that the large-scale circulation acts to minimise the effect these surface contrasts have higher up. This suggests a model for vertical wind which cools the air over warmer surfaces and warms it where the surface is cool, called the Weak Temperature Gradient (WTG) Approximation, that is frequently used in studying the climate in the tropics.

GrSEMtest1_SST_map2-page-001
Fig.1 Sea surface temperatures (K) at 0Z on 1/1/2000 (ERA-Interim)

GrSEMtest1_T_map2-page-001
Fig.2 Temperatures at 500 hPa (K) at 0Z on 1/1/2000 (ERA-Interim)

 

 

 

 

 

Thermodynamic ideas have been around for some 200 years. Carnot, a Frenchman worried about Britain’s industrial might underpinning its military potential(!), studied the efficiency of heat engines and showed that the maximum mechanical work generated by an engine is determined by the ratio of the temperatures at which energy enters and leaves the system. It is possible to treat climate systems as heat engines – for example Kerry Emanuel has used Carnot’s idea to estimate the pressure in the eye of a hurricane. I have been building on a recent development of these ideas by Olivier Pauluis at New York University who shows how to divide up the maximum work output of a climate heat engine into the generation of wind, the lifting of moisture and a lost component, which he calls the Gibbs penalty, which is the energetic cost of keeping the atmosphere moist. Typically, 50% of the maximum work output is gobbled up by the Gibbs penalty, 30% is the moisture lifting term and only 20% is used to generate wind.

For my PhD, I have been applying Pauluis’ ideas to a modelled system consisting of two connected tropical regions (one over a cooler surface than the other), which are connected by a circulation given by the weak temperature gradient approximation. I look at how this circulation affects the components of work done by the system. Overall there is no impact – in other words the WTG does not distort the thermodynamics of the underlying system – which is reassuring for those who use it. What is perhaps more interesting however, is that even though the WTG circulation is very weak compared to the winds that we observe in the two columns, it does as much work as is done by the cooler column – in other words its thermodynamic importance is huge. This suggests that further avenues of study may help us better express what drives the climate in the tropics.

The impact of vegetation structure on global photosynthesis

Renato BraghiereLeave a comment

Email: R.Braghiere@pgr.reading.ac.uk

Twitter: @renatobraghiere

The partitioning of shortwave radiation by vegetation into absorbed, reflected, and transmitted terms is important for most biogeophysical processes including photosynthesis. The most commonly used radiative transfer scheme in climate models does not explicitly account for vegetation architectural effects on shortwave radiation partitioning, and even though detailed 3D radiative transfer schemes have been developed, they are often too computationally expensive and require a large number of parameters.

Using a simple parameterisation, we modified a 1D radiative transfer scheme to simulate the radiative balance consistently with 3D representations. Canopy structure is typically treated via a so called “clumping” factor which acts to reduce the effective leaf area index (LAI) and hence fAPAR (fraction of absorbed photosynthetically radiation, 400-700 nm). Consequently from a production efficiency standpoint it seems intuitive that any consideration of clumping can only lead to reduce GPP (Gross Primary Productivity).  We show, to the contrary, that the dominant effect of clumping in more complex models should be to increase photosynthesis on global scales.

difference_gpp_clump_default_jules
Figure 1. Difference in GPP estimated by JULES including clumping and default JULES GL4.0. Global difference is 5.5 PgC.

The Joint UK Land Environment Simulator (JULES) has recently been modified to include clumping information on a per-plant functional type (PFT) basis (Williams et al., 2017). Here we further modify JULES to read in clumping for each PFT in each grid cell independently. We used a global clumping map derived from MODIS data (He et al., 2012) and ran JULES 4.6 for the year 2008 both with and without clumping using the GL4.0 configuration forced with the WATCH-Forcing-Data-ERA-Interim data set (Weedon et al., 2014). We compare our results against the MTE (Model Tree Ensemble) GPP global data set (Beer et al., 2010).

erro_bar_boxes_v2
Figure 2. Regionally averaged GPP compared to the MTE GPP data set. In all areas except Africa there is an overall improvement.

Fig. 1 shows an almost ubiquitous increase in GPP globally when clumping is included in JULES. In general this improves agreement against the MTE data set (Fig. 2). Spatially the only significant areas where the performance is degraded are some tropical grasslands and savannas (not shown). This is likely due to other model problems, in particular the limited number of PFTs used to represent all vegetation globally. The explanation for the increase in GPP and its spatial pattern is shown in Fig 3. JULES uses a multi-layered canopy scheme coupled to the Farquhar photosynthesis scheme (Farquhar et al., 1980). Changing fAPAR (by including clumping in this case) has largest impacts where GPP is light limited, and this is especially true in tropical forests.

gpp_vertical_anomaly_zonal_mean_Opt5_gridbox
Figure 3. Difference in longitudinally averaged GPP as a function of depth in the canopy. Clumping allows greater light penetration to lower canopy layers in which photosynthesis is light limited.

 

References

Beer, C. et al. 2010. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329(5993), pp.834-838.

Farquhar, G.D. et al. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 149, 78–90.

He, L. et al. 2012. Global clumping index map derived from the MODIS BRDF product. Remote Sensing of Environment, 119, pp.118-130.

Weedon, G. P. et al. 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514.

Williams, K. et al. 2017. Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska. Geoscientific Model Development, 10(3), pp.1291-1320.

The onset and end of wet seasons over Africa

Caroline Dunning2 Comments

Email: c.m.dunning@pgr.reading.ac.uk

For many Africans, the timing of the wet season is of crucial importance, especially for those reliant upon subsistence agriculture, who depend on the seasonal rains for crop irrigation. In addition, the wet season recharges lakes, rivers and water storage tanks which constitute the domestic water supply in some areas. The timing of the wet season also affects the availability of energy from hydroelectric schemes, and has impacts upon the prevalence of certain disease carrying vectors, such as mosquitoes.

Climate change is already threatening many vulnerable populations, and changes in the timing or intensity of the wet season, or increasing uncertainty in the timing of the onset, may lead to significant socio-economic impacts. But before we consider future projections or past changes in the seasonality, we need to go back a few steps.

The first step is to find a method for determining when the wet season starts and ends (its ‘onset’ and ‘cessation’). In order to look at large-scale shifts in the timing of the wet season and relate this to wider-scale drivers, this method needs to be applicable across the entirety of continental Africa. Most previous methods for determining the onset focus on the national to regional scale, and are dependent on the exceedance of a certain threshold e.g. the first week with at least 20mm of rainfall, with one rainfall event of more than 10mm, and no dry spell of more than 10 days after the rain event for the next month. While such definitions work well at a national scale they are not applicable at a continental scale where rainfall amounts vary substantially. A threshold suitable for the dry countries at the fringes of the Sahara would not be suitable in the wetter East African highlands.

In addition to a vast range of rainfall amounts, the African continent also spans multiple climatic regimes. The seasonal cycle of precipitation over continental Africa is largely driven by the seasonal progression of the ITCZ and associated rain belts, which follows the maximum incoming solar radiation. In the boreal summer, when the thermal equator sits between the equator and the Tropic of Cancer, the ITCZ sits north of the equator and West Africa and the Sahel experience a wet season. During the boreal autumn the ITCZ moves south, and southern Africa experiences a wet season during the austral summer, followed by the northward return of the ITCZ during the boreal spring. As a consequence of this, central African regions and the Horn of Africa experience two wet seasons per year – one as the ITCZ travels north, and a second as the ITCZ travels south. A method for determining the onset and cessation at the continental scale thus needs to account for regions with multiple wet seasons per year.

In our paper (available here) we propose such a method, based on the method of Liebmann et al (2012). The method has three steps:

  • Firstly, determine the number of seasons experienced per year at the location (or grid point) of interest. This is achieved using harmonic analysis – the amplitude of the first and second harmonic were computed, using the entire timeseries and their ratio compared. If the ratio was greater than 1.0, i.e. the amplitude of the second harmonic was greater than the amplitude of the first harmonic then the grid point was defined as having two wet seasons per year (biannual), if the ratio was less than one then it was defined as having an annual regime. Figure 1 shows the ratio for one African rainfall dataset (TARCATv2). Three regions are identified as biannual regions; the Horn of Africa, an equatorial strip extending from Gabon to Uganda and a small region on the southern West African coastline.

    blog_fig1
    Figure 1: Location of regions with one and two seasons per year, determined using harmonic analysis. Yellow indicates two seasons per year, while pink/purple indicates one season per year. Computed from TARCATv2 data.
  • Secondly the period of the year when the wet season occurs was determined. This was achieved by looking for minima and maxima in the climatological cumulative daily rainfall anomaly to identify one or two seasons.
  • The third and final stage is to calculate the onset and cessation dates for each year. This is done by looking for the minima and maxima in the cumulative daily rainfall anomaly, calculated for each season.

Figure 2 shows the seasonal progression of the onset and cessation, with the patterns observed in agreement with those expected from the driving physical mechanisms, and continuous progression across the annual/biannual boundaries. Over West Africa and the Sahel, Figure 2a-b shows zonally-contiguous progression patterns with onset following the onset of the long rains and moving north, and cessation moving southward, preceding the end of the short rains. Over southern Africa Figure 2c-d shows the onset over southern Africa starting in the north-west and south-east, following the onset of the short rains, reaching the East African coast last, and cessation starting at the Zimbabwe, Mozambique, South Africa border and spreading out radially into the cessation of the long rains.

As well as testing the method for compatibility with known physical drivers of African rainfall, agreement across multiple satellite-based rainfall estimates was also examined. In general, good agreement was found across the datasets, particularly for regions with an annual regime and over the biannual region of East Africa.

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Figure 2: Southward and northward progression of the onset and cessation across the annual/biannual boundaries, computed using GPCP daily rainfall data 1998-2013.

The advantage of having a method that works at the continental scale is the ability to look at the impact of large-scale oscillations on wider-scale variability. One application of this method was to investigate the impact of El Niño upon both the annual rains and short rains (Figure 3). In Figure 3 we see the well-documented dipole in rainfall anomaly, with higher rainfall totals over 0–15°S and the Horn of Africa in El Niño years and the opposite between 15°S and 30°S.  This anomaly is stronger when we use this method compared with using standard meteorological seasons. We can also see that while the lower rainfall to the south is colocated with later onset dates and a consequentially shorter season, the higher rainfall over the Horn of Africa is associated with later cessation of the short rains, with only small differences in onset date.

blog_fig3
Figure 3: a-c) Composite of onset, cessation and wet season rainfall in El Niño years for annual rains and short rains, minus the mean over 1982-2013, computed using CHIRPS data d) Oct-Feb rainfall anomaly in  years (CHIRPS).

In addition to using this method for research purposes, its application within an operational setting is also being explored. Hopefully, the method will be included within the Rainwatch platform, which will be able to provide users with a probabilistic estimate of whether or not the season has started, based on the rainfall experienced so far that year, and historical rainfall data.

For more details, please see the paper detailing this work:

Dunning, C.M., E Black, and R.P. Allan (2016) The onset and cessation of seasonal rainfall over Africa, Journal of Geophysical Research: Atmospheres, 121 11,405-11,424, doi: 10.1002/2016JD025428

References:

Liebmann, B., I. Bladé, G. N. Kiladis, L. M. Carvalho, G. B. Senay, D. Allured, S. Leroux, and C. Funk (2012), Seasonality of African precipitation from 1996 to 2009, J. Clim., 25(12), 4304–4322.