The impact of vegetation structure on global photosynthesis

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.

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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).

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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.

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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. Science329(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 Environment119, 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 Development10(3), pp.1291-1320.

Sting Jet: the poisonous (and windy) tail of some of the most intense UK storms

Email: a.volonte@pgr.reading.ac.uk

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Figure 1: Windstorm Tini (12 Feb 2014) passes over the British Isles bringing extreme winds. A Sting Jet has been identified in the storm. Image courtesy of NASA Earth Observatory

It was the morning of 16th October when South East England got battered by the Great Storm of 1987. Extreme winds occurred, with gusts of 70 knots or more recorded continually for three or four consecutive hours and maximum gusts up to 100 knots. The damage was huge across the country with 15 million trees blown down and 18 fatalities.

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Figure 2: Surface wind gusts in the Great Storm of 1987. Image courtesy of UK Met Office.

The forecast issued on the evening of 15th October failed to identify the incoming hazard but forecasters were not to blame as the strongest winds were actually due to a phenomenon that had yet to be discovered at the time: the Sting Jet. A new topic of weather-related research had started: what was the cause of the exceptionally strong winds in the Great Storm?

It was in Reading at the beginning of 21st century that scientists came up with the first formal description of those winds, using observations and model simulations. Following the intuitions of Norwegian forecasters they used the term Sting Jet, the ‘sting at the end of the tail’. Using some imagination we can see the resemblance of the bent-back cloud head with a scorpion’s tail: strong winds coming out from its tip and descending towards the surface can then be seen as the poisonous sting at the end of the tail.

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Figure 3: Conceptual model of a sting-jet extratropical cyclone, from Clark et al, 2005. As the cloud head bends back and the cold front moves ahead we can see the Sting Jet exiting from the cloud tip and descending into the opening frontal fracture.  WJ: Warm conveyor belt. CJ: Cold conveyor belt. SJ: Sting jet.

In the last decade sting-jet research progressed steadily with observational, modelling and climatological studies confirming that the strong winds can occur relatively often, that they form in intense extratropical cyclones with a particular shape and are caused by an additional airstream that is neither related to the Cold nor to the Warm Conveyor Belt. The key questions are currently focused on the dynamics of Sting Jets: how do they form and accelerate?

Works recently published (and others about to come out, stay tuned!) claim that although the Sting Jet occurs in an area in which fairly strong winds would already be expected given the morphology of the storm, a further mechanism of acceleration is needed to take into account its full strength. In fact, it is the onset of mesoscale instabilities and the occurrence of evaporative cooling on the airstream that enhances its descent and acceleration, generating a focused intense jet (see references for more details). It is thus necessary a synergy between the general dynamics of the storm and the local processes in the cloud head in order to produce what we call the Sting Jet .

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Figure 4: Sting Jet (green) and Cold Conveyor Belt (blue) in the simulations of Windstorm Tini. The animation shows how the onset of the strongest winds is related to the descent of the Sting Jet. For further details on this animation and on the analysis of Windstorm Tini see here.

References:

http://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/case-studies/great-storm

Browning, K. A. (2004), The sting at the end of the tail: Damaging winds associated with extratropical cyclones. Q.J.R. Meteorol. Soc., 130: 375–399. doi:10.1256/qj.02.143

Clark, P. A., K. A. Browning, and C. Wang (2005), The sting at the end of the tail: Model diagnostics of fine-scale three-dimensional structure of the cloud head. Q.J.R. Meteorol. Soc., 131: 2263–2292. doi:10.1256/qj.04.36

Martínez-Alvarado, O., L.H. Baker, S.L. Gray, J. Methven, and R.S. Plant (2014), Distinguishing the Cold Conveyor Belt and Sting Jet Airstreams in an Intense Extratropical Cyclone. Mon. Wea. Rev., 142, 2571–2595, doi: 10.1175/MWR-D-13-00348.1.

Hart, N.G., S.L. Gray, and P.A. Clark, 0: Sting-jet windstorms over the North Atlantic: Climatology and contribution to extreme wind risk. J. Climate, 0, doi: 10.1175/JCLI-D-16-0791.1.

Volonté, A., P.A. Clark, S.L. Gray. The role of Mesoscale Instabilities in the Sting-Jet dynamics in Windstorm Tini. Poster presented at European Geosciences Union – General Assembly 2017, Dynamical Meteorology (General session)

Tales from the Alice Holt forest: carbon fluxes, data assimilation and fieldwork

Email: ewan.pinnington@gmail.com

Forests play an important role in the global carbon cycle, removing large amounts of CO2 from the atmosphere and thus helping to mitigate the effect of human-induced climate change. The state of the global carbon cycle in the IPCC AR5 suggests that the land surface is the most uncertain component of the global carbon cycle. The response of ecosystem carbon uptake to land use change and disturbance (e.g. fire, felling, insect outbreak) is a large component of this uncertainty. Additionally, there is much disagreement on whether forests and terrestrial ecosystems will continue to remove the same proportion of CO2 from the atmosphere under future climate regimes. It is therefore important to improve our understanding of ecosystem carbon cycle processes in the context of a changing climate.

Here we focus on the effect on ecosystem carbon dynamics of disturbance from selective felling (thinning) at the Alice Holt research forest in Hampshire, UK. Thinning is a management practice used to improve ecosystem services or the quality of a final tree crop and is globally widespread. At Alice Holt a program of thinning was carried out in 2014 where one side of the forest was thinned and the other side left unmanaged. During thinning approximately 46% of trees were removed from the area of interest.

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Figure 1: At the top of Alice Holt flux tower.

Using the technique of eddy-covariance at flux tower sites we can produce direct measurements of the carbon fluxes in a forest ecosystem. The flux tower at Alice Holt has been producing measurements since 1999 (Wilkinson et al., 2012), a view from the flux tower is shown in Figure 1. These measurements represent the Net Ecosystem Exchange of CO2 (NEE). The NEE is composed of both photosynthesis and respiration fluxes. The total amount of carbon removed from the atmosphere through photosynthesis is termed the Gross Primary Productivity (GPP). The Total Ecosystem Respiration (TER) is made up of autotrophic respiration (Ra) from plants and heterotrophic respiration (Rh) from soil microbes and other organisms incapable of photosynthesis. We then have, NEE = -GPP + TER, so that a negative NEE value represents removal of carbon from the atmosphere and a positive NEE value represents an input of carbon to the atmosphere. A schematic of these fluxes is shown in Figure 2.

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Figure 2: Fluxes of carbon around a forest ecosystem.

The flux tower at Alice Holt is on the boundary between the thinned and unthinned forest. This allows us to partition the NEE observations between the two areas of forest using a flux footprint model (Wilkinson et al., 2016). We also conducted an extensive fieldwork campaign in 2015 to estimate the difference in structure between the thinned and unthinned forest. However, these observations are not enough alone to understand the effect of disturbance. We therefore also use mathematical models describing the carbon balance of our ecosystem, here we use the DALEC2 model of ecosystem carbon balance (Bloom and Williams, 2015). In order to find the best estimate for our system we use the mathematical technique of data assimilation in order to combine all our available observations with our prior model predictions. More infomation on the novel data assimilation techniques developed can be found in Pinnington et al., 2016. These techniques allow us to find two distinct parameter sets for the DALEC2 model corresponding to the thinned and unthinned forest. We can then inspect the model output for both areas of forest and attempt to further understand the effect of selective felling on ecosystem carbon dynamics.

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Figure 3: Model predicted cumulative fluxes for 2015 after data assimilatiom. Solid line: NEE, dotted line: TER, dashed line: GPP. Orange: model prediction for thinned forest, blue: model prediction for unthinned forest. Shaded region: model uncertainty after assimilation (± 1 standard deviation).

In Figure 3 we show the cumulative fluxes for both the thinned and unthinned forest after disturbance in 2015. We would probably assume that removing 46% of the trees from the thinned section would reduce the amount of carbon uptake in comparison to the unthinned section. However, we can see that both forests removed a total of approximately 425 g C m-2 in 2015, despite the thinned forest having 46% of its trees removed in the previous year. From our best modelled predictions this unchanged carbon uptake is possible due to significant reductions in TER. So, even though the thinned forest has lower GPP, its net carbon uptake is similar to the unthinned forest. Our model suggests that GPP is a main driver for TER, therefore removing a large amount of trees has significantly reduced ecosystem respiration. This result is supported by other ecological studies (Heinemeyer et al., 2012, Högberg et al., 2001, Janssens et al., 2001). This has implications for future predictions of land surface carbon uptake and whether forests will continue to sequester atmospheric CO2 at similar rates, or if they will be limited by increased GPP leading to increased respiration.

References

Wilkinson, M. et al., 2012: Inter-annual variation of carbon uptake by a plantation oak woodland in south-eastern England. Biogeosciences, 9 (12), 5373–5389.

Wilkinson, M., et al., 2016: Effects of management thinning on CO2 exchange by a plantation oak woodland in south-eastern England. Biogeosciences, 13 (8), 2367–2378, doi: 10.5194/bg-13-2367-2016.

Bloom, A. A. and M. Williams, 2015: Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological “common sense” in a model data fusion framework. Biogeosciences, 12 (5), 1299–1315, doi: 10.5194/bg-12-1299-2015.

Pinnington, E. M., et al., 2016: Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using four-dimensional variational data assimilation. Agricultural and Forest Meteorology, 228229, 299 – 314, doi: http://dx.doi.org/10.1016/j.agrformet.2016.07.006.

Heinemeyer, A., et al., 2012: Exploring the “overflow tap” theory: linking forest soil co2 fluxes and individual mycorrhizo- sphere components to photosynthesis. Biogeosciences, 9 (1), 79–95.

Högberg, P., et al., 2001: Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature, 411 (6839), 789–792.

Janssens, I. A., et al., 2001: Productivity overshadows temperature in determining soil and ecosystem respiration across european forests. Global Change Biology, 7 (3), 269–278, doi: 10.1046/j.1365-2486.2001.00412.x.

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Mountains and the Atmospheric Circulation within Models

Email: a.vanniekerk@pgr.reading.ac.uk

Mountains come in many shapes and sizes and as a result their dynamic impact on the atmospheric circulation spans a continuous range of physical and temporal scales. For example, large-scale orographic features, such as the Himalayas and the Rockies, deflect the atmospheric flow and, as a result of the Earth’s rotation, generate waves downstream that can remain fixed in space for long periods of time. These are known as stationary waves (see Nigam and DeWeaver (2002) for overview). They have an impact not only on the regional hydro-climate but also on the location and strength of the mid-latitude westerlies. On smaller physical scales, orography can generate gravity waves that act to transport momentum from the surface to the upper parts of the atmosphere (see Teixeira 2014), playing a role in the mixing of chemical species within the stratosphere.

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Figure 1: The model resolved orography at different horizontal resolutions. From a low (climate model) resolution to a high (seasonal forecasting) resolution. Note how smooth the orography is at climate model resolution.

Figure 1 shows an example of the resolved orography at different horizontal resolutions over the Himalayas. The representation of orography within models is complicated by the fact that, unlike other parameterized processes, such as clouds and convection, that are typically totally unresolved by the model, its effects are partly resolved by the dynamics of the model and the rest is accounted for by parameterization schemes.However, many parameters within these schemes are not well constrained by observations, if at all. The World Meteorological Organisation (WMO) Working Group on Numerical Experimentation (WGNE) performed an inter-model comparison focusing on the treatment of unresolved drag processes within models (Zadra et al. 2013). They found that while modelling groups generally had the same total amount of drag from various different processes, their partitioning was vastly different, as a result of the uncertainty in their formulation.

Climate models with typically low horizontal resolutions, resolve less of the Earth’s orography and are therefore more dependent on parameterization schemes. They also have large model biases in their climatological circulations when compared with observations, as well as exhibiting a similarly large spread about these biases. What is more, their projected circulation response to climate change is highly uncertain. It is therefore worth investigating the processes that contribute towards the spread in their climatological circulations and circulation response to climate change. The representation of orographic processes seem vital for the accurate simulation of the atmospheric circulation and yet, as discussed above, we find that there is a lot of uncertainty in their treatment within models that may be contributing to model uncertainty. These uncertainties in the orographic treatment come from two main sources:

  1. Model Resolution: Models with different horizontal resolutions will have different resolved orography.
  2. Parameterization Formulation: Orographic drag parameterization formulation varies between models.

The issue of model resolution was investigated in our recent study, van Niekerk et al. (2016). We showed that, in the Met Office Unified Model (MetUM) at climate model resolutions, the decrease in parameterized orographic drag that occurs with increasing horizontal resolution was not balanced by an increase in resolved orographic drag. The inability of the model to maintain an equivalent total (resolved plus parameterized) orographic drag across resolutions resulted in an increase in systematic model biases at lower resolutions identifiable over short timescales. This shows not only that the modelled circulation is non-robust to changes in resolution but also that the parameterization scheme is not performing in the same way as the resolved orography. We have highlighted the impact of parameterized and resolved orographic drag on model fidelity and demonstrated that there is still a lot of uncertainty in the way we treat unresolved orography within models. This further motivates the need to constrain the theory and parameters within orographic drag parameterization schemes.

References

Nigam, S., and E. DeWeaver, 2002: Stationary Waves (Orographic and Thermally Forced). Academic Press, Elsevier Science, London, 2121–2137 pp., doi:10.1016/B978-0-12-382225-3. 00381-9.

Teixeira MAC, 2014: The physics of orographic gravity wave drag. Front. Phys. 2:43. doi:10.3389/fphy.2014.00043 http://journal.frontiersin.org/article/10.3389/fphy.2014.00043/full

Zadra, A., and Coauthors, 2013: WGNE Drag Project. URL:http://collaboration.cmc.ec.gc.ca/science/rpn/drag_project/

van Niekerk, A., T. G. Shepherd, S. B. Vosper, and S. Webster, 2016: Sensitivity of resolved and parametrized surface drag to changes in resolution and parametrization. Q. J. R. Meteorol. Soc., 142 (699), 2300–2313, doi:10.1002/qj.2821. 

 

Air Pollution – The Cleaner Side of Climate Change?

Email: c.p.webber@pgr.reading.ac.uk

Air pollution is a major global problem, with the World Health Organisation recently linking 1 in 8 global deaths to this invisible problem. I say invisible, what air pollution may seem is an almost invisible problem. My PhD looks at some of the largest air pollutants, particulate matter PM10, which is still only 1/5th the width of a human hair in diameter!

My project looks at whether winter (December – February) UK PM10 concentration ([PM10]) exceedance events will change in frequency or composition in a future climate. To answer this question, a state of the art climate model is required. This model simulates the atmosphere only and is an iteration of the Met-Office HADGEM3 model. The climate simulation models a future 2050 under the RCP8.5 emissions scenario, the highest greenhouse-gas emission scenario considered in IPCC-AR5 (Riahi et al., 2011).

In an attempt to model PM10 in the climate model (a complex feat, currently tasked to the coupled UKCA model), we have idealised the problem, making the results much easier to understand. We have emitted chemically inert tracers in the model, which represent the key sources of PM10 throughout mainland Europe and the UK. The source regions identified were: West Poland, Po Valley, BENELUX and the UK. While the modelled tracers were shown to replicate observed PM10 well, albeit with inevitable sources of lost variability, they were primarily used to identify synoptic flow regimes influencing the UK. The motivation of this work is to determine whether the flow regimes that influence the UK during UK PM10 episodes, change in a future climate.

As we are unable to accurately replicate observed UK [PM10] within the model, we need to generate a proxy for UK [PM10] episodes. We chose to identify the synoptic meteorological conditions (synoptic scale ~ 1000 km) that result in UK air pollution episodes. We find that the phenomenon of atmospheric blocking in the winter months, in the Northeast Atlantic/ European region, provide the perfect conditions for PM10 accumulation in the UK. In the Northern Hemisphere winter, Rossby Wave Breaking (RWB) is the predominant precursor to atmospheric blocking (Woollings et al., 2008). RWB is the meridional overturning of air masses in the upper troposphere, so that warm/cold air is advected towards the pole/equator. The diagnostic chosen to detect RWB on is potential temperature (θ) on the potential vorticity = 2 Potential vorticity units surface, otherwise termed the dynamical tropopause. The advantages of using this diagnostic for detecting RWB have been outlined in this study’s first publication; Webber et al., (2016). Figure 1 illustrates this mechanism and the metric used to diagnose RWB, BI, introduced by Pelly and Hoskins (2003).

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Fig. 1 – A schematic of Rossby Wave Breaking, breaking in a clockwise (anticyclonic) direction. The black contour represents a θ contour on the 2PVU surface, otherwise termed the dynamical tropopause. The colour shading represents θ anomalies, with red/ blue being warm/cold θ anomalies. The metric used to identify RWB is shown as the BI metric and is the mean θ in the 15 degrees latitude to the north subtracted by that to the south of the centre of overturning (black dot).

In Fig. 1 warm air is transported to the north of cold air to the south. This mechanism generates an anticyclone to the north of the centre of overturning (black circle in Fig 1) and a cyclone to the south. If the anticyclone to north becomes quasi-stationary, a blocking anticyclone is formed, which has been shown to generate conditions favourable for the accumulation of PM10.

To determine whether there exists a change in RWB frequency, due to climate change (a climate increment), the difference in RWB frequency between two simulations must be taken. The first of these is a free-running present day simulation, which provides us with the models representation of a present day atmosphere. The second is a future time-slice simulation, representative of the year 2050. Figure 2 shows the difference between the two simulations, with positive values representing an increase in RWB frequency in a future climate. The black contoured region corresponds to the region where the occurrence of RWB significantly increases UK [PM10].

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Fig 2. Climate increment in RWB frequency, with red/blue shading representing an increase/ decrease in RWB frequency in a future climate. The thick black contour represents the region where the occurrence of RWB significantly raises mean UK [PM10].
RWB frequency anomalies within the black contoured region are of most importance within this study. Predominantly the RWB frequency anomaly, within the black contour, can be described as a negative frequency anomaly. However, there also exist heterogeneous RWB frequency anomalies within the contoured region. What is shown is that there is a tendency for RWB to occur further north and eastward in a future climate. These shifts in the regions of RWB occurrence influence a shift in the resulting flow regimes that influence the UK.

Climate shifts in flow regimes were analysed, however only for the most prominent subset of RWB events. RWB can be subset into cyclonic and anti-cyclonic RWB (CRWB and ACRWB respectively) and both have quite different impacts on UK [PM10] (Webber et al., 2016).  ACRWB events are the most prominent RWB subset within the Northeast Atlantic/ European region (Weijenborg et al., 2012). Figure 1 represents ACRWB, with overturning occurring in a clockwise direction about the centre of overturning and these events were analysed for climate shifts in resultant flow regimes.

The analysis of climate flow regime shifts, provides the most interesting result of this study. We find that there exists a significant (p<0.05) increase in near European BENELUX tracer transport into the UK and a significant reduction of UK tracer accumulation, following ACRWB events. What we therefore see is that while in the future we see a reduction in the number of RWB and ACRWB events in a region most influential to UK [PM10], there also exists a robust shift in the resulting flow regime. Following ACRWB, there exists an increased tendency for the transport of European PM10 and decreased locally sourced [PM10] in the UK. Increased European transport may result in increased long-range transport of smaller and potentially more toxic (Gehring et al., 2013) PM2.5 particles from Europe.

References

Gehring, U., Gruzieva, O., Agius, R. M., Beelen, R., Custovic, A., Cyrys, J., Eeftens, M., Flexeder, C., Fuertes, E., Heinrich, J., Hoffmann, B., deJongste, J. C., Kerkhof, M., Klümper, C., Korek, M., Mölter, A., Schultz, E. S., Simpson, A.,Sugiri, D., Svartengren, M., von Berg, A., Wijga, A. H., Pershagen, G. and Brunekreef B.: Air Pollution Exposure and Lung Function in Children: The ESCAPE Project. Children’s Health Prespect, 121,
1357-1364, doi:10.1289/ehp.1306770 , 2013.

Pelly, J. L and Hoskins, B. J.: A New Perspective on Blocking. J. Atmos. Sci, 50, 743-755, doi: http://dx.doi.org/10.1175/1520- 0469(2003)060<0743:ANPOB>2.0.CO;2, 2003.

Riahi, K., Rao S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N. and Rafaj, P.: RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change, 109, no. 1-2, 33-57, doi: 10.1007/s10584-011-0149-y, 2011.

Webber, C. P., Dacre, H. F., Collins, W. J., and Masato, G.: The Dynamical Impact of Rossby Wave Breaking upon UK PM10 Concentration. Atmos. Chem. and Phys. Discuss, doi; 10.5194/acp-2016-571, 2016.

Weijenborg, C., de Vries, H. and Haarsma, R. J.: On the direction of Rossby wave breaking in blocking. Climate Dynamics, 39, 2823- 2831, doi: 10.1007/s00382-012-1332-1, 2012.

Woollings, T. J., Hoskins, B. J., Blackburn, M. and Berrisford, P.: A new Rossby wave-breaking interpretation of the North Atlantic Oscillation. J. Atmos. Sci, 65, 609-626, doi: http://dx.doi.org/10.1175/2007JAS2347.1, 2008.

 

 

Understanding the urban environment and its effect on indoor air.

Email: h.l.gough@pgr.reading.ac.uk

Recent estimates by the United Nations (2009) state that 50 to 70 % of the world’s population now live in urban areas with over 70 % of our time being spent indoors, whether that’s at work, at home or commuting.

We’ve all experienced a poor indoor environment, whether it’s the stuffy office that makes you sleepy, or the air conditioning unit that causes the one person under it to freeze. Poor environments make you unproductive and research is beginning to suggest that they can make you ill. The thing is, the microclimate around one person is complex enough, but then you have to consider the air flow of the room, the ventilation of the building and the effect of the urban environment on the building.

So what tends to happen is that buildings and urban areas are simplified down into basic shapes with all the fine details neglected and this is either modelled at a smaller scale in a wind tunnel or by using CFD (computer fluid dynamics). However, how do we know whether these models are representative of the real-world?

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This is Straw city, which was built in Silsoe U.K during 2014. You can just see the car behind the array (purple circle), these cubes of straw are 6 m tall, or roughly the height of an average house. Straw city is the stepping stone between the scale models and the real world, and was an urban experiment in a rural environment. We measured inside the array, outside of the array and within the blue building so we could see the link between internal and external flow: which meant the use of drones and smoke machines! The focus of the experiment was on the link between ventilation and the external conditions.

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Smoke releases, drone flying, thermal imaging and tracer gas release: some of the more fun aspects of the fieldwork

After 6 months of data collection, we took the straw cubes away and just monitored the blue cube on its own and the effect of the array can clearly be seen in this plot, where pink is the array, and blue is the isolated cube. So this is showing the pressure coefficient (Cp),  and can be thought of as a way of comparing one building to another in completely different conditions. You can see that the wind direction has an effect and that the array reduces the pressure felt by the cube by 60-90 %. Pressure is linked to the natural ventilation of a building: less pressure means less flow through the opening.

 

Alongside the big straw city, we also went to the Enflo lab at the University of Surrey to run some wind tunnel experiments of our own, which allowed us to expand the array.

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Photos of the wind tunnel arrays. Left is the biggest array modelled, centre is the Silsoe array, top right is the wind tunnel and roughness elements. Bottom right is the model of the storage shed at the full-scale site and centre is the logging system used.

So we have a data set that encompasses all wind directions and speeds, all atmospheric stabilities, different temperature differences and different weather conditions. It’s a big data set and will take a while to work through, especially with comparisons to the wind tunnel model and CFD model created by the University of Leeds. We will also compare the results to the existing guidelines out there and to other similar data sets.

I could ramble on for hours about the work, having spent far too long in a muddy field in all weathers but for more information please email me or come along to my departmental seminar on the 8th November.

This PhD project is jointly funded by the University of Reading and the EPSRC and is part of the Refresh project: www.refresh-project.org.uk

The impact of Climate Variability on the GB power system.

Email: h.bloomfield@pgr.reading.ac.uk

Bloomfield et al., 2016. Quantifying the increasing sensitivity of power systems to climate variability. View published paper.

Within the power system of Great Britain (GB), there is a rapidly increasing amount of generation from renewables, such as wind and solar power which are weather-dependent. An increased proportion of weather-dependent generation will require increased understanding of the impact of climate variability on the power system.

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Figure 1: Predicted installed capacity from the National Grid Gone Green Scenario. Source: National Grid Future Energy Scenarios (2015).

Current research on the impact of climate variability on the GB power system is ongoing by climate scientists and power system modellers. The focus of the climate research is on the weather-driven components of the power system, such as the impact of climate variability on wind power generation. These studies tend to include limited knowledge of the whole system impacts of climate variability. The research by power system modellers focuses on the accurate representation of the GB power system. A limited amount of weather data may be used in this type of study (usually 1-10 years) due to the complexity of the power system models.

The aim of this project is to bridge the gap between these two groups of research, by understanding the impact of climate variability on the whole GB power system.In this project, multi-decadal records from the MERRA reanalysis* are combined with a simple representation of the GB power system, of which the weather-dependent components are electricity demand and wind power production. Multiple scenarios are analysed for GB power systems, including 0GW, 15GW, 30GW, and 45GW of installed wind power capacity in the system.

This study characterises the impact of inter-annual climate variability on multiple aspects of the GB power system (including coal, gas and nuclear generation) using a load duration curve framework. A load duration curve can be thought of as a cumulative frequency distribution of power system load. Load can be either power system demand (i.e. the NO-WIND scenario) or demand minus wind power (ie. the LOW, MED and HIGH scenarios).

The introduction of additional wind-power capacity greatly increases the year-year variability in operating opportunity for conventional generators, this is particularly evident for baseload plant (i.e. nuclear power plants). The impact of inter-annual climate variations across the power system due to present-day level of wind-farm installation has approximately doubled the exposure of the GB power sector to inter-annual climate variability. This is shown in Figure 2 as the spread between the red and blue curves (from the LOW scenario) is double that of the black curves (the NO-WIND scenario).

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Figure 2: Load duration curves for the NO-WIND and LOW scenario in black and grey respectively. The two most extreme years from the LOW scenario are 1990 and 2010, plotted in red and blue respectively. Vertical dashed lines show the percentage of time that baseload-plant (91%) and peaking plant (7%) are required to operate

This work has shown that as the amount of installed wind power capacity on the power system is increased, the total amount of energy required from other generators (coal, gas, nuclear) is reduced. Wind therefore contributes to decarbonising the power system, however the reduction is particularly pronounced for plants which are operating as baseload rather than peaking plant (i.e. oil fired generation) where an increase in required production is seen.

This study adds to the literature which suggests that the power system modelling community should begin to take a more robust approach to its treatment of weather and climate data by incorporating a wider range of climate variability.

For more information contact the author for a copy of the paper with details of this work: Quantifying the increasing sensitivity of power system to climate variability (submitted to ERL).

* A reanalysis data set is a scientific method for developing a record of how weather and climate are changing over time. In it, observations are combined with a numerical model to generate a synthesised estimate of the state of the climate system.