Research Study: Re-simulating Hurricane Katrina

1. Introduction

Hurricane Katrina, in August 2005, was the costliest natural disaster in history to hit the United States, with an estimated $108 billion of damage to property, as well as 1833 deaths (FEMA, 2007). It had become a hurricane ( winds > 33m/s) just before making landfall in Florida on the 25th. As the hurricane passed over the warm waters of the Gulf of Mexico, it rapidly intensified. As Fig.1a shows, by 0000UTC on the 28th, maximum wind speeds were above 50m/s, hence a category 3 hurricane. The hurricane deepened further - shown by decreasing pressure in Fig.1b – and wind speeds also increased. A maximum wind speed of 78m/s was recorded at 18000UTC on the 29th, although this is not present in Fig.1a. A minimum hourly surface pressure of 941hPa was also simulated at this time (Fig.1b). This value is much higher than the 902hPa minimum observed (NOAA, 2005). These underestimations are discussed in Sec. 4.2.

Fig.1 a) Maximum and minimum wind speeds, b) Minimum surface pressure, in the vicinity of the hurricane on 28th and 29th August 2005.

2. Method

The WRF model was used to simulate the progress of Hurricane Katrina. Using ‘ncview’ to plot the water vapour mixing ratio (QVAPOR), the hurricane’s synoptic-scale structure could be interpreted (Fig.2). From this, the hurricane was considered to be strongest, whilst still approximately symmetrical, at 1000UTC on the 29th. This time is therefore used in the subsequent analysis. The model is initialised at 2005-08-28_00:00 UTC.

Coordinates were then taken in the zonal, west-to-east direction through the centre of the hurricane, which were used in cross sections of various quantities. Potential temperature contours were overlaid on each vertical cross section to allow for easy identity of the location of the eye column.
Plan views of cloud types were also plotted, using the assumptions:
• Stratiform cloud where w < 1 m/s, with liquid or mixed-phase cloud and precipitation hydrometeors.
• Cirriform cloud where w < 1 m/s, with ice-only cloud.
• Convective cloud where w > 1 m/s.

All scripts were then re-ran under a new parameterisation scheme (Sec.4.2). The coordinates used in the cross section plots were changed, in order to transect through the eye of the storm for comparison of the different schemes. Although this will capture the hurricane in a different stage of its development, simple comparisons of the hurricane’s structure can be made.

3. Results and analysis

3.1 Structure

Katrina had a classic hurricane structure. In the northern hemisphere, the flow around a surface low pressure is cyclonic (Fig.3a). Gradient wind balance exists, but this is disrupted by turbulent friction at low levels, causing the flow to be deflected towards the low pressure at the centre of the system. The low-level winds spiral towards the low-pressure centre, causing large horizontal convergence in the eye wall. By the conservation of mass, this leads to strong ascent in this region (Fig.3b), setting up a thermal direct circulation: the ascending air rises and flows outwards in the upper levels. Fig.3a-b can be used to interpret that the radial velocity would be large and positive where there is the inflow, decreases with height, and would become negative where there is the outflow.

The kink in the potential temperature contours (Fig.3c) indicates the upward advection of ascending air in the eye wall and the downward advection of descending air in the eye. As the air rises in the eye wall, it rapidly cools, allowing relative humidity to increase to, or close to, 100% (Fig.3d). This initiates the rapid growth of the cloud-liquid amount (Fig.4a), and deep convective cloud forms (Fig.4c), producing a very high total precipitation hydrometeor amount in the eye wall region (Fig.4b).
 

Fig.3 West-east vertical cross section (looking north) of a) horizontal velocity, b) vertical velocity, c) potential temperature, and d) relative humidity, valid at 2005-08-29_10:00UTC. Horizontal velocity is negative to the west of the eye and positive to the east of the eye – hence cyclonic flow.

Fig.4 West-east vertical cross section (looking north) of a) cloud-liquid amount, b) total precipitation hydrometeor amount, and c) a plan view of convective cloud, valid at 2005-08-29_10:00UTC.

      3.2 Dynamics

In the Northern Hemisphere, the Coriolis force causes low pressure systems to have cyclonic flow, which is seen in the lower levels on Hurricane Katrina (Fig.5a). However, latent heat release and warming builds high pressure aloft, generating a strong upper level pressure gradient. This causes the upper level outflow to become anti-cyclonic. This can be seen in Fig.5b by observing the clockwise outward-spiralling wind barbs. This process also results in the strongest winds being close to the surface and decreasing with height, which can be seen in Fig.3a and by comparing the magnitude of the wind barbs in Fig.5.

   

Fig.5 Plan view of wind barbs at a) 950hPa and b) 200hPa, valid at 2005-08-29_10:00UTC.

      3.3 Potential vorticity

Potential vorticity (PV) is the absolute circulation of an air parcel that is enclosed between two isentropic surfaces. It is dependent on the static stability and absolute vorticity. PV on the 320K isentropic level (~600hPa using Fig.3c), in the eye wall, has values greater than 6PVU (Fig.6a), due to cyclonic flow and very large absolute vorticity here. Away from the vicinity of hurricane centre, PV is positive but close to zero, suggesting weak cyclonic flow here at this level. This plot can be compared to the PV on the 360K isentropic level (~230hPa using Fig.3c). At this level, negative PV values exist (Fig.6b) in the hurricane’s vicinity, away from the hurricane eye and eye wall (where PV has decreased to ~4PVU). Negative PV is indicative of anticyclonic flow at this upper level. This supports the analysis of radial velocity in Sec. 3.1 and the plots of wind barbs in Fig.5.

 Fig.6 Plan view of potential vorticity on the a) 320K and b) 360K isentropic level, valid at 2005-08-29_10:00UTC.

4. WRF model errors

4.1 Sources of error

NWP models such as WRF are not perfect and contain many sources of error that can significantly affect the results model output. The chaotic nature of the atmosphere amplifies any sources of error, so it is important to limit any uncertainties to avoid error growth. Firstly, the model contains truncation errors, introduced by the discretisation of partial differential equations. Unresolved parameterisations also add error, which arise due to an insufficient understanding of the processes taking place. The increased resolution of models is not matched by increasing resolution of observations, and so surface representation errors remain. The most significant contributors to model error are believed to be in the physics parameterisations (WRF-RAB, 2006). In particular, the cloud microphysics contains significant sources of uncertainty for explicit prediction of convective cells. Secondly, uncertainty arises through imperfect initial conditions. Observational data contain errors and few measurements are taken over the Ocean.

      4.2 Kain-Fritsch convection/cumulus parameterisation scheme

In Sec. 1-3, the Morrison 2-moment microphysics scheme (mp_physics=10) and the Betts-Miller-Janjic (BMJ) convection parameterisation scheme (cu_physics=2) was used. Re-running the model under the Kain-Fritsch (KF) convection parameterisation scheme (cu_physics=1) outputted noticeable differences to the simulation of the hurricane. Firstly, the new scheme resulted in a faster tracking system and hence a different hurricane location at 2005-08-29_10:00UTC (Fig.7) - the hurricane is estimated to be 120km further north than under the BMJ scheme. Comparison with observational surface pressure charts suggests that the KF scheme better estimates the location of the hurricane. Fig.7 shows a lower central surface pressure under the KF scheme than under the BMJ scheme. This may help explain why the maximum wind speed and the minimum surface pressure plotted in Fig.1 are underestimations under the BMJ ‘adjustment’ scheme. As a result, PV is greater in the lower levels. In addition, the deep convective cells within the eye wall are in different relative locations (Fig.7).

Fig.7 Plan view of convective cloud valid at 2005-08-29_10:00UTC under a) the Betts-Miller-Janjic and b) the Kain-Fritsch, convection parameterisation schemes. Surface pressure contours are overlaid.

The vertical cross section plots in Sec. 3 were repeated with the new scheme and with corrected coordinates. Kerkhoven et al. (2006) found that the BMJ scheme had difficulty representing vertical velocities accurately, but this is difficult to assess in our analysis because we do not know what the ‘true’ profiles should look like. The most noticeable difference is the structure and magnitude of the downdrafts within the eye appear more realistic under the KF scheme (Fig.8). In the KF scheme, all cloud systems are represented through a 1D cloud model, which accounts for up-/down-drafts, en-/de-trainment, and other cloud processes, and so better simulates profile changes, such as the development of the eye’s downdraft column. Also noticeable is the weaker updraft existing to the east of the eye (Fig.8b). The different structure to the east of the eye is also present for other variables.
                       

Fig.8 West-east vertical cross section (looking north) of vertical velocity valid at 2005-08-29_10:00UTC under a) the Betts-Miller-Janjic and b) the Kain-Fritsch, convection parameterisation schemes.

5. References

• FEMA (2007), Federal Disaster Declarations, FEMA, Hyattsville, MD, available at: www.fema.gov/news/disasters.fema#sev1.

• Kerkhoven, E., Gan, T. Y., Shiiba, M., Reuter, G. and Tanaka, K. (2006), A comparison of cumulus parameterization schemes in a numerical weather prediction model for a monsoon rainfall event. Hydrol. Process., 20: 1961–1978. doi:10.1002/hyp.5967

• National Oceanic and Atmospheric Administration (NOAA), 2005a: Post Storm Data Acquisition, Aerial Wind Analysis and Damage Assessment, Hurricane Katrina, 11 pp. [Available online at: http://www.weather.gov/om/data/pdfs/KatrinaPSDA.pdf]

• WRF-RAB, 2006. RESEARCH-COMMUNITY PRIORITIES FOR WRF-SYSTEM DEVELOPMENT. Pre-pared by the WRF Research Applications Board, December 2006 Executive Summary.

Links between glacier hydrology and processes of glacier flow

Glacier hydrology and glacier flow are strongly interlinked. Glacier flow transfers ice from accumulation areas to ablation areas and as the melting of ice occurs in these ablation areas, the hydrological cycle is strongly affected by the processes of glacier motion. A greater amount of ice melt in ablation areas due to increases to glacier flow speed may lead to a changes in the water content within the glacier, hence impacting the glacier hydrology. Likewise, changes to the production, storage and transport of water within the glacier influences its motion, through a number of processes. These links are particularly important for warm or temperate glaciers because they generally contain much more water (Meier and Post, 1991).

Glacier motion occurs by strain within the ice or the bed (ice creep deformation), or by sliding at the interface between the ice and bed. It is driven by the force exerted by the ice and balanced by the drag at the glacier boundaries and by ice viscosity. Ice creep is strongly influenced by the intergranular water content within the ice. At low water contents, surface tension pulls surfaces together, increasing the effective pressure and causing a rise in frictional strength.

A greater impact on glacier flow by glacier hydrology is from water stored within and at the base of the glacier. Warm or temperate glaciers have strong diurnal and seasonal variations in their hydrology (Knight, 1999), which leads to varying quantities of water descending crevasses into the bed of the glacier. Wallace (1871) was the one of the first to note the affect these variations have on the rate of glacier motion. Pressure builds when water accumulates at the base of the glacier, which offsets the weight of the glacier. This causes the basal resistance between wet ice and the smooth surface to be very low and hence allows the glacier to slide forward at an increasing rate. Bartholomaus (2008) found that this mechanism occurs when englacial, as well as subglacial, water storage increases.

Figure 1 shows how changes in water storage are well correlated with glacier velocities. The study suggested that when water inputs exceed the ability of the existing conduits to transmit water, the conduits pressurize and drive water back into the extensive linked cavity system, which in turn promotes basal motion. The mechanism of basal sliding is suggested to account for up to 90% of the movement of thin ice on steep slopes and 20-50% of the movement in valley glaciers (Sharp, 1954).
The flow of meltwater associated with regelation sliding occurs through a thin film between the ice and its bed (Weertman, 1964; Hallet, 1979), but can also flow through a vein network within a basal ice layer (Lliboutry,1993), particularly in temperate glaciers.

Fig.1 Taken from Bartholomaus (2008) showing the rate of change of water storage and ice speed, for (a) diurnal, (b) seasonal and (c) outburst-flood timescales. At each timescale, sliding coincides with times of increasing water storage.

References

Barry, R.G. and Gan, T.Y. (2011). The global cryosphere: past, present and future. Cambridge University Press, Cambridge.

Bartholomaus, T. C., Anderson, R. S., & Anderson, S. P. 2008. Response of glacier basal motion to transient water storage. Nature Geoscience, 1, 33−37.

Benn, D.I. and Evans, D.J.A. (1998). Glaciers and Glaciation. London, Wiley.

Hallet, B. (1979). Subglacial regelation water film. Journal of Glaciology 23, 321-34.

Knight, P.G. (1999) Glaciers. London: Routledge. 261pp.

Lliboutry, L. (1993). Internal melting and ice accretion at the bottom of temperate glaciers. Journal of Glaciology 39, 50-64.

Meier, M.F. and Post, A. (1991) Glaciers: a Water Resources. United States Department of the Interior, US Geological Survey, Denver.

Sharp, R.P. (1954). Glacier flow: A review. Bull. Geol. Soc. Amer., 65: 821-38.

Wallace, A.R. (1871). The theory of glacier motion. Nature, 3:309-10.

Weertman, J. (1964). The theory of glacier sliding. Journal of Glaciology 5, 287-303.

What causes uncertainty in the cryospheric contributions to 21st century sea level change?

Ocean thermal expansion and glacier mass loss, caused by the global mean temperature increases, have had the largest contributions to global mean sea level (GMSL) (Hock et al. 2009). The melting of glaciers and ice caps (excluding the glaciers surrounding Greenland and Antarctica) contributed to sea-level rise by about 0.8 mm per year from 2001–2004 (Kaser et al., 2006) and the rate of sea level rise is increasing. However, observational data is temporally limited, and satellite/airborne measurements lack resolution (Church et al., 2013). This adds error to the computing of the past ice volume lost to melting, and hence there is significant uncertainty in its current contribution to GMSL change. As a result, future predictions of 21st century GMSL also contain large uncertainty (Fig.1).

The use of the new global inventory on nearly all glaciers in the world (Arendt et al. 2012), and hence eliminating the global upscaling of glaciers, improved the quantification of uncertainty in the projections of glacier contributions to sea level change (Radic et al., 2014), but a poor understanding of some important cryospheric processes remain. Ice sheet rapid dynamic response, including complex snow hydrology and drifting snow process, are implemented as first-order approximations and are difficult to implement on a global scale (Luthi, 2009). Kangerdlussuaq and Helheim glaciers in the south-east and Jakobshavn in the south west of Greenland (Nick et al., 2009; Kerr, 2009) have rapidly thinned and retreated in recent years, possibly due to the hydraulic acceleration of the ice sheet, but it is not understood whether marine ice sheet instability (MISI) and the infiltration of surface melt water provides a dynamical effect of the movement of the ice (van de Wal et al., 2008; Shepherd et al., 2009). MISI is not currently factored into GCMs (Favier et al., 2014).

The calibration and initialisation of GCMs is affected by poor observational data, as well as by non-perfect empirical parameters. For example, the temperature-index model may not represent reality at the scale of individual glaciers. In addition, under future climate conditions the parameters may change. Emissions and climate scenarios are used to help project future climate forcings, providing several different outcomes (Fig.1), and therefore not one single scenario can be assumed. Natural forcings, such as volcanic eruptions and changes in incoming solar radiation, also add uncertainty to glacier mass changes, particularly in the lower latitudes (Huss et al., 2009).

GCMs assume a direct, instantaneous GMSL equivalent from glacier mass loss. However, the effect of meltwater flow through aquifers and basins, and the changes to the storage of water on land, is unclear. Water storage changes will result from the building of dams, the mining of groundwater, and the isostatic adjustment of land surface and ocean floor due to changes in ice and water loading.

Figure 1. Projected glacier volume loss and corresponding GMSL equivalent over the 21st century (Radic et al. 2014).

References

Arendt, A. et al., 2012. Randolph Glacier Inventory [v2.0]: A dataset of globl glacier outlines. Global Land Ice Measurements from Space, Boulder CO, Digital Media, USA

Bamber, J., and R. Riva. 2010. The sea level fingerprint of recent ice mass fluxes. The Cryosphere 4: 621-627.

Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. Merri eld, G.A. Milne, R.S. Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer and A.S. Unnikrishnan, 2013: Sea Level Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.- K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Favier. L., G. Durand., S. L. Cornford., G. H. Gudmundsson., O. Gagliardini., F. Gillet-Chaulet., T. Zwinger., A. J. Payne & A. M. Le Brocq., 2014, Retreat of Pine Island Glacier controlled by marine icesheet instability. Nature Climate Change, 12 January, Volume 4, pp. 117-121.

Hock R, de Woul M, Radić V, Dyurgerov M (2009) Mountain glaciers and ice caps around Antarctica make a large sea-level rise contribution. Geophys Res Lett 36:L07501. 

Huss M, Funk M, Ohmura A (2009) Strong Alpine melt in the 1940s due to enhanced solar radiation. Geophys Res Lett 36:L23501

Intergovernmental Panel on Climate Change (IPCC). 2007. IPCC Fourth Assessment Report - Climate Change 2007: The Physical Science Basis Summary for Policymakers.

Intergovernmental Panel on Climate Change (IPCC). 2013. IPCC Fifth Assessment Report - Climate Change 2013: The Physical Science Basis Summary for Policymakers.

Kaser G, Cogley JG, Dyurgerov MB, Meier MF, Ohmura A (2006) Mass balance of glaciers and ice caps: consensus estimates for 1961–2004. Geophys Res Lett 33. 

 Lüthi MP (2009) Transient response of idealized glaciers to climate change. J Glaciol 55(193):918–930

Meier, M.F., M.B. Dyurgerov, U.K. Rick, S. O'Neel, W.T. Pfeffer, R.S. Anderson, S.P. Anderson, and A.F. Glazovsky. 2007. Glaciers dominate eustatic sea-level rise in the 21st century. Science 317: 1064-1067.

Radić, V., A. Bliss, A. C. Beedlow, R. Hock, E. Miles, J.G. Cogley. 2014. Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models. Climate Dynamics 42: 37-58

How satellites measure the volume of sea ice in the Arctic and recent changes in the Arctic sea ice volume

Climate change in the Arctic has been twice as fast as the global average (Blunden and Arndt 2012), causing general declines to the sea ice thickness, extent and concentration. It is important, though, to consider the volume of the sea ice, because this depends on both ice thickness and extent, hence suitably reflects changes to the exchange of fresh water between sea ice and the ocean. This is highlighted by the simulations of coupled global climate models, such as the Pan-Arctic Ice Ocean Modeling and Assimiation System (PIOMAS). The simulations display a 3.4% decline in Arctic sea ice volume per decade (Fig.1) (PIOMAS, Zhang and Rothrock,2003), whilst the decline in sea ice extent is predicted at 2.4% per decade (Gregory et al., 2002).

However, a continuous record of Arctic sea ice volume cannot currently be observed. One method for estimating sea ice volume changes uses satellite observations of sea ice thickness and concentration, and sea ice volume can then be extrapolated from this. Satellite altimetry is used to measure sea ice thickness. The satellite’s laser or radar pulse measures the height difference between the ocean surface and the ice surface - the freeboard.  Measurements of thickness are possible with the approximation that the freeboard is 1/9th of the sea ice thickness (Vihma, 2014). The weight of snow cover, invisible to the radar altimeters, is one contribution of uncertainty in this measurement (Schweiger et al., 2011). The CryoSat-2 radar altimeter, which launched in 2010, has provided new thickness and volume estimates of Arctic Ocean sea ice (Laxon et al., 2013), with coverage up to 89°N. The observations show the ice volume inside the Arctic Basin decreased between the period of previous satellite ICESat (2003–2008) and the CryoSat-2 period (2010–present), by a total of 4291km3 in the autumn months and by 1479km3 in the winter months (Vaughan et al., 2013).

The PIOMAS simulations supports this, showing decline of sea ice volume over all seasons (Zhang and Rothrock, 2003) since the satellite record began in 1979 (Fig.1). September 2016 sea ice volume (4500km3) was 60% below the 1979-2015 mean and the third lowest for September on record, behind 2012 and 2011. The largest decline has come at the end of the summer melt season (Serreze et al. 2007) and the change in September minimum sea ice extent is becoming steeper with time (Cosimo et al. 2008). The period with sea ice cover has become shorter over large areas (Stammerjohn et al. 2012) and Overland et al. (2011) estimates an ice-free Artic Ocean will occur around year 2050. Holland et al. (2008) suggests that the summer ice volume is also increasing in variability, due the increasingly thinner ice being more vulnerable to melting out during the summer under favourable atmospheric conditions.

Figure 1. Monthly sea ice volume anomaly from PIOMAS. The 1979-present trend is shown in blue. Shaded areas shows two standard deviations from the trend. 

From: http://psc.apl.washington.edu/research/projects/arctic-sea-ice-volume-anomaly/

References

Blunden J, Arndt DS (2012) State of the climate in 2011. Bull Am Meteorol Soc 93:S1–S264, Special supplement

Comiso JC, Parkinson CL, Gersten R, Stock L (2008) Accelerated decline in the Arctic sea ice cover. Geophys Res Lett 35:L01703

Gregory, J. M., P. A. Stott, D. J. Cresswell, N. A. Rayner, C. Gordon, and D. M. H. Sexton (2002), Recent and future changes in Arctic sea ice simulated by the HadCM3 AOGCM, Geophys. Res. Lett., 29(24).

Holland MM, Bitz CM, Tremblay B, Bailey DA (2008) The role of natural versus forced change in future rapid summer Arctic ice loss. In: DeWeaver ET, Bitz CM, Tremblay L-B (eds) Arctic sea ice decline: observations, projections, mechanisms, and implications. Geophys Monogr Ser, 180. AGU, Washington, DC, pp 133–150

Laxon S. W., K. A. Giles, A. L. Ridout, D. J. Wingham, R. Willatt, R. Cullen, R. Kwok, A. Schweiger, J. Zhang, C. Haas, S. Hendricks, R. Krishfield, N. Kurtz, S. Farrell and M. Davidson (2013), CryoSat-2 estimates of Arctic sea ice thickness and volume, Geophys. Res. Lett., 40, 732–737, doi:10.1002/grl.50193

Overland JE, Wang M, Walsh JE, Christensen JH, Kattsov VM, Champan WL (2011a) Climate model projections for the Arctic. In: AMAP (2011) Snow, Water, Ice and Permafrost in the Arctic (SWIPA): Climate Change and the Cryosphere. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway. xii + 538 pp

Schweiger, A., R. Lindsay, J. L. Zhang, M. Steele, H. Stern, and R. Kwok (2011), Uncertainty in modeled Arctic sea ice volume, J. Geophys. Res. Oceans, 116, C00D06, doi:10.1029/2011JC007084. 

Serreze, M.C., Holland, M.M., Stroeve, J., 2007. Perspectives on the Arctic’s Shrinking Sea-Ice Cover. Science 16 Mar 2007: 1533-1536.

Stammerjohn S, Massom R, Rind D, Martinson D (2012) Regions of rapid sea ice change: an interhemispheric seasonal comparison. Geophys Res Lett 39:L06501

Vaughan, D., et al. (2013) Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Vihma, T. Surv Geophys (2014) 35: 1175. doi:10.1007/s10712-014-9284-0

Zhang, J.L. and D.A. Rothrock, “Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates“, Mon. Weather Rev., 131, 845-861, 2003

Predicting the Evolution of Imja Glacier, Nepal

Rising global mean temperature has led to the retreat of the majority of the world’s glaciers (Lemke et al., 2007). Glaciers in the Mount Everest (Sagamartha) region of Nepal are receding at an average rate of 10–59 m a–1 (Bajracharya and Mool, 2009). The Imja glacier, located just southeast of Mount Everest (fig.1), in the Khumbu Range of Eastern Nepal’s Himalaya, retreated at 41 meters/year from 1961-2000 and 74 meters/year from 2001-2006 (Bajracharya and Mool, 2009).

Figure 1. Location of the Imja glacier. Its glacial lake, Lake Imja, can be seen in the bottom-left of the image. Taken from Google Earth Pro.

Its heavy recession resulted in the formation of a glacial lake at the foot of the glacier in the 1960’s. Since then, the Lake Imja has expanded from 0.03 km2 to 1.35 km2 at a rate of 0.026 km2a-1, developing into one of the largest glacial lakes in the Himalayas. Glacial lakes can be very dangerous, as they can trigger an outburst flood.

Glacier recession also causes sea level to rise, and can help accelerate climate change through a number of glacier-climate feedback processes (Lemke et al., 2007). Glacial change also impacts river flows and landscape evolution.

It is therefore important to understand the speed of glacier retreat and to predict their evolution. In this study, mass balance and thickness of the Imja glacier is predicted, in order to estimate its likely longevity. 

Methods

Glacier mass balance tells us about the change in mass of a glacier over a specified time period, due to an imbalance between accumulation and ablation. The geodetic approach is used to calculate the mass balance of the Imja glacier, which computes the difference between digital elevation models (DEMs) from satellite imagery several years apart, producing a glacier surface elevation change. The sensors contain three cameras – one facing forwards, one directly downwards and one backwards. Stereoscopy is then used to compute the elevation for every image pixel. One DEM was derived from satellite imagery acquired by the Advanced Land Observing Satellite (ALOS) Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) in April 2009. The other DEM is from satellite imagery acquired by the Shuttle Radar Topography Mission (SRTM) in February 2000. The glacier volume change is calculated from the difference between the two DEMs, which is converted to a mass change. A mass balance in metres water equivalent (m.w.e.) of negative 1 m.w.e. means that every year a 1 m depth of water, across the whole glacier surface, is lost.

Along with an estimate of the glacier’s maximum thickness, and by making several assumptions, the mass balance can be used to estimate the lifetime of the Imja glacier, before it completely disappears:
Glacier longevity = Current maximum thickness ÷ Glacier mass loss

The maximum glacier thickness is used because this gives us the longevity of the thickest part of the glacier, which is likely to last the longest amount of time before the glacier completely disappears. The maximum ice thickness is estimated using the perfect plasticity approach (Nye, 1951), which relates the thickness and surface slope to a yield stress.

Results and Discussion

The geodetic approach produced a summed elevation change of -2.90x105 m between February 2000 and April 2009 for the Imja glacier. A negative elevation change for Imja Glacier was also found by Thakuri et al. (2016) (fig.2) and by King et al. (2016) (fig.3).

Our summed elevation change equates to a glacier mass balance of -1.31 m.w.e.a-1. This essentially means that every year a 1 m depth of water, across the whole glacier surface, is lost. This agrees with Bolch et al. (2011), Nuimura et al. (2012) and Gardelle et al. (2013), who showed the Imja Glacier experiencing a mean of –1.45 m.w.e.a-1 during 2002–2007, of –0.93 m.w.e.a-1 during 2000–2008, and of –0.70 m.w.e.a-1 during 1999–2011.

Figure 2. Glacier elevation change of Imja Glacier for 2001–14, with the area mean (inset box). Mean elevation change is plotted as a function of elevation in the panel on the right. Taken from Thakuri et al. (2016). 

Figure 3. Glacier surface elevation change over the study area between 2000 and 2014/15. Also shown is a summary of off-glacier terrain differences. Areas of no data show the ASTER GDEM underlay. Taken from King et al. (2016).

With a mass balance of -1.31 ma-1 and a maximum glacier thickness of 329 m, a simple calculation suggests that the Imja Glacier will completely disappear in 251 years (year-2260), if it were to continue to lose mass at the same rate.

Summed elevation change (m)	-2.90x105
Volume change (m3)	-2.61x108
Mass change (tonnes)	-2.35x108
Mass change per year (tonnes per year)	-2.56x107
Mass balance  (metres water equivalent)	-1.31
Maximum thickness (m)	329
Glacier lifetime (years)	251

Uncertainties in the Evolution Forecast

The process used to calculate the lifetime of the Imja Glacier was greatly simplified and contains some technical uncertainties. However, large uncertainties arise from glaciological and climatological factors influencing the future dynamics and evolution of the glacier, which are not considered in our estimation of the glacier lifetime.

Firstly, global mean temperatures are set to continue to rise. This will continue to cause greater ablation of glaciers, increasing the rate of glacier retreat. Differing emission and climate scenarios means future global temperatures are unknown, so the effect of rising temperature on glacier mass loss contains are large amount of uncertainty. Natural forcings, such as volcanic eruptions and changes in incoming solar radiation, also add uncertainty to glacier mass changes, particularly in the lower latitudes (Huss et al., 2009).

As the Imja Glacier losses mass, the dynamical processes that control its mass balance are likely to change. Differences in the rate of thinning across the glacier can result in changes to the glacier slope, which can lead to changes in the glacier speed.
Glacial lakes enhance glacier melt and favours mass loss through calving, so the continued growth of the Imja Lake may increase the retreat rate of the glacier. Basnett et al. (2013) found that debris covered glaciers in the Sikkim Himalaya with proglacial lakes have greater retreat than glaciers without proglacial lakes

Changes to the amount of debris cover as the glacier retreats will also affect the mass balance over time. If the debris cover is thin, it tends to enhance glacier melting, but if the debris cover is thick enough, it tends to reduce melting by insulating the glacier. Takeuchi et al. (2000) found that in the Khumbu Glacier, debris cover less than 5 cm increases ablation, whilst debris cover greater than 5 cm reduces ablation. Debris cover is likely to increase with increased melting due to warmer temperatures, so this is likely to increase the glacier mass loss with time.
Other changes to accumulation and ablation zones of the glacier may also have an impact on its mass loss, such as changes to the frequency and/or impact of avalanches.

Overall, it is likely that the glacier mass loss will increase with time, resulting in our lifetime estimation for the Imja Glacier of 251 years to be an overestimate. Thakuri et al. (2016) found that the loss rate of the Imja glacier has increased over time, from 0.04 km2a-1 for 1962-1992, to 0.11 km2a-1 for 1992-2013. This is confirmed by Bolch et al. (2011) and by Nuimura et al. (2012).

References

BAJRACHARYA, S. R. & MOOL, P. 2009. Glaciers, glacial lakes and glacial lake outburst floods in the Mount Everest region, Nepal. Annals of Glaciology, 50, 81-86.
BASNETT, S., KULKARNI, A. V. & BOLCH, T. 2013. The influence of debris cover and glacial lakes on the recession of glaciers in Sikkim Himalaya, India. Journal of Glaciology, 59, 1035-1046.
BOLCH, T., PIECZONKA, T. & BENN, D. 2011. Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) derived from stereo imagery. The Cryosphere, 5, 349-358.
HUSS, M., FUNK, M. & OHMURA, A. 2009. Strong Alpine glacier melt in the 1940s due to enhanced solar radiation. Geophysical Research Letters, 36.
LEMKE, P., REN, J., ALLEY, R. B., ALLISON, I., CARRASCO, J., FLATO, G., FUJII, Y., KASER, G., MOTE, P. & THOMAS, R. H. 2007. Observations: changes in snow, ice and frozen ground.
LI, H., NG, F., LI, Z., QIN, D. & CHENG, G. 2012. An extended “perfect‐plasticity” method for estimating ice thickness along the flow line of mountain glaciers. Journal of Geophysical Research: Earth Surface, 117.
NUIMURA, T., FUJITA, K., YAMAGUCHI, S. & SHARMA, R. R. 2012. Elevation changes of glaciers revealed by multitemporal digital elevation models calibrated by GPS survey in the Khumbu region, Nepal Himalaya, 1992–2008. Journal of Glaciology, 58, 648-656.
NYE, J. The flow of glaciers and ice-sheets as a problem in plasticity.  Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1951. The Royal Society, 554-572.
King, O., Quincey, D.J., Carrivick, J.L. and Rowan, A.V., 2016. Spatial variability in mass change of glaciers in the Everest region, central Himalaya, between 2000 and 2015. The Cryosphere Discussions, pp.1-35.
RAPER, S. C. & BRAITHWAITE, R. J. 2006. Low sea level rise projections from mountain glaciers and icecaps under global warming. Nature, 439, 311-313.
TAKEUCHI, Y., KAYASTHA, R. B. & NAKAWO, M. 2000. Characteristics of ablation and heat balance in debris-free and debris-covered areas on Khumbu Glacier, Nepal Himalayas, in the pre-monsoon season. IAHS PUBLICATION, 53-62.
WANG, D. & KÄÄB, A. 2015. Modeling glacier elevation change from DEM time series. Remote Sensing, 7, 10117-10142.

Rapid Cyclogenesis of the US Super-Snowstorm, 10-16 March 1993

Introduction

The 1993 Storm of the Century broke low pressure records and brought large amounts of snowfall and rainfall, as well as high winds, tornadoes and thunderstorms, across the East coast of America. Surface winds speeds reached 144mph in Mount Washington and 60 inches of snowfall was recorded over Mount LeConte. The cost of damage from these severe weather conditions is estimated around $6-10 billion and the storm was responsible for more than 300 deaths.

This case study examines some of the dynamical mechanisms that led to the storm’s explosive cyclogenesis, aided by figures 1-4, developed using GEMPAK.

Fig.1. Analysis at 1200 UTC 12 March 1993. (a) Mean sea level pressure is labelled in hPa and contoured every 4hPa. (b) The 300hPa geopotential height (solid orange lines) is labelled in metres and contoured every 120m. The 300hPa wind speeds is shaded at intervals of 15ms-1. The jet streaks are identified as “A” and “B”. The 300hPa divergence (solid blue lines) is labelled in 10-5s-1 at intervals of 2x10-5s-1.  (c) The 300-700hPa thickness (solid pink lines) is labelled in metres and contoured every 60m. The 500hPa absolute vorticity is shaded at intervals of 10x10-5s-1. The 500hPa positive vorticity advection by the thermal wind (solid purple lines) is labelled in 10-8mkg-1 at intervals of 3x10-5mkg-1.  (d) The 500hPa geopotential height (solid orange lines) is labelled in metres and contoured every 60m. Omega is shaded at intervals of 10x10-3cPa s-1, with the shading corresponding to upward vertical motion.

Fig.2. As in Fig.1 but for 0000 UTC 13 March 1993.

Fig.3. As in Fig.1 but for 1200 UTC 13 March 1993.

Fig.4. As in Fig.1 but for 0000 UTC 14 March 1993.

Possible storm deepening mechanisms

a) Effect of upper-level jet streak-induced circulations

Throughout the time period of 1200 UTC 12 March to 0000 UTC 14 March, the location of the low pressure system, in comparison to the location of the upper-level jet streaks A and B (noted on figures 1-4), was favourable for cyclogenesis.

At 1200 UTC 12 March, the low pressure system is located in the left jet exit region of jet streak B, particularly for the north-western side of the low pressure system. Our knowledge of the direction of the ageostrophic wind vector in the exit region of a jet streak, in association with the rapid deceleration of the geostrophic winds, tells us that there will be upper-level divergence of the air in the left jet exit region. The upper-level divergence provides a forcing for upward vertical motion throughout the column, by the mass continuity equation. Negative omega at 500hPa, a variable representative of upward vertical motion throughout the tropospheric column, is plotted in Fig.1d. The location of the large values of negative omega off the southeast coast of Texas relates well with the large upper-level divergence present in Fig.1b. In turn, the low pressure system experiences a reduction in its surface pressure and hence indicates that jet streak B is providing a cyclogenetic forcing at this time. The effect of this forcing can be observed on the plot of MSLP 12 hours later at 0000 UTC 13 March (Fig.2a). The circulation of the low pressure system has strengthened, as seen by the tightening of the isobars. The circulation has strengthened most notably in the northwest quadrant of the system, which corresponds to the location of the strongest forcing for ascent at 1200 UTC 12 March, so it is reasonable that this is where the low pressure system notably deepened.

At 0000 UTC 13 March, the cyclone is now located in the right jet entrance region of jet streak A (Fig.2b). This is another favourable position for upper-level divergence of the ageostrophic winds, due to the rapid acceleration of the geostrophic winds in the entrance region of the jet streak. Upper-level divergence suggests there is low-level convergence and hence upward vertical motion. Comparison of Fig.2d and Fig.2b shows that the strongest ascent at 500hPa is directly in the region of strongest upper-level divergence at 300hPa. This sets up a thermally direct ageostrophic circulation in the entrance region. The reduction of mass in the column that results from the ascent is favourable for continued deepening of the cyclone. The significantly greater strength and stubbiness of jet streak A at 0000 UTC 13 March compared to jet streak B at 1200 UTC 12 March - and hence much more rapid changes in speed in the respective jet entrance and exit regions - provides much larger values of upper-level divergence close to the cyclone at this time compared to the divergence values present 12 hours before. As a result, the upward vertical motion is much stronger at this later time, and so the deepening that occurs in the 12 hours after this is much more explosive. This explosive deepening causes the MSLP to drop from 992 hPa to 976 hPa at 1200 UTC 13 March (Fig.3a). The isobars are now much tighter around the cyclone – the cyclone has a much greater intensity.

Jet streak A remains extremely stubby through 1200 UTC 13 March, with great acceleration of the geostrophic winds in its entrance region, and so a thermally direct ageostrophic circulation remains. The upper-level divergence of the ageostrophic winds in the right entrance region of jet streak A is over a much greater area (Fig.3b) at this time and the surface cyclone centre is located just to the south of the right jet entrance region. However, jet streak B has now combined with jet streak A to a certain extent, and so the effect of jet streak A is not the only upper-level feature to be considered. The surface cyclone is also located in close proximity to the left exit region of jet streak B, another favourable position for upper-level ageostrophic divergence. Furthermore, the surface cyclone is downstream of an upper-level jet stream trough. This is yet another desirable location for upper-level divergence. The positioning of the surface cyclone among these three different factors results in the total upper-level divergence remaining very large over the surface cyclone. Once again, this upper-level divergence corresponds to location of large negative omega (Fig.3d). The removal of mass via upward vertical motion causes a strong deepening of the cyclone, as observed 12 hours later at 0000 UTC 14 March (Fig.4a) by the further tightening of the isobars.

b) Effect of midlevel short-wave troughs

Investigation the vorticity advection by the thermal wind around the location of the low pressure system can give insight into the rapid cyclogenesis. Regions of positive vorticity advection (PVA) by the thermal wind are directly associated with upward vertical motion, as seen using the Trenberth form of the quasigeostrophic omega equation.

At 1200 UTC 12 March, the low pressure system is located at the base of a mid-level short-wave trough, where a maximum in absolute vorticity is positioned (Fig.1c). The PVA downstream of the thermal wind is over the eastern side of the low pressure system, and so the PVA may be providing some degree of cyclogenetic forcing for the low pressure system. By comparing the location of the PVA by the thermal wind with the area of large, negative omega in Fig.1d, it is evident that they are co-located. Therefore, the PVA is likely providing a forcing for ascent at this time. Ascent is favourable for cyclonic deepening, and so this region of PVA by the thermal wind most probably caused some of the storm’s development in the following 12 hours, to 992hPa at 0000 UTC 13 March.

At 0000 UTC 13 March, there is a region of large PVA by the thermal wind over the northern side of the low pressure centre (Fig.2c), over the south coast of New Orleans. 12 hours later the surface cyclone has deepened from 992 hPa to 976 hPa (Fig.3a), as expected for strong ascent. Most notably, the circulation in the northern quadrant of the cyclone has strengthened, as seen by the tightening of the isobars 1200 UTC 13 March. This part of the cyclone is where the greatest forcing for ascent was located at 0000 UTC 13 March, so it is reasonable that this is where the cyclone notably deepened.
At 1200 UTC 13 March, there is now an area of strong PVA by the thermal wind located over the southern side of the cyclone centre and there is a corresponding region of negative omega to the south of the surface cyclone in Fig.3d.

c) Other cyclogenetic effects

Latent heat release may have played an important role in the deepening of the cyclone. It is likely that the warming produced by condensation processes will have provided a large forcing for cyclogenesis. The convectively unstable pre-storm environment would also have had a large role in the development of the storm. These effects are not analysed here.

Conclusions

The amplification and evolution of the 1993 superstorm was partly driven by two midlevel short-wave troughs merging and then interacting with a thermally direct ageostropic circulation in the entrance region of an upper level jet streak.

Research Study: The influence of Synoptic Meteorology on UK Air Quality

The weather often plays an important role during episodes of poor air quality, allowing local surface pollution emissions to accumulate, but UK air quality is also affected by meteorological processes over much greater length scales. Observations and modelling can be used to study these influences, helping to improve air quality forecasts and understanding.

Background

Air pollution has detrimental impacts on human health. Exposure to air pollution increases the risk of disease from a stroke, heart disease, lung cancer and respiratory diseases, including asthma. Poor air quality, particularly from nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM), contributes to an approximate 40,000 premature deaths per year in the UK1, as well as costing UK society £16 billion annually2.

Nitrogen oxide (NO) and nitrogen dioxide (NO2), known together as nitrogen oxides (NOx), are produced from natural and anthropogenic sources, particularly by power generation, motor vehicles, fuel and solvent use, and other industrial activities.

Ozone is not emitted directly, but is formed in the atmosphere through photochemical reactions involving NOx, volatile organic compounds (VOCs) and ultraviolet radiation (sunlight).
Particulate matter (PM), or aerosols, are a suspension of tiny particles in air, each with a diameter of 0.001 – 100 µm. PM2.5 and PM10 are defined as having an aerodynamic diameter of less than 2.5 µm and 10 µm, respectively. The main species of PM are sulphates, nitrates, organics, dust, soot and sea salt.

Wind-driven atmospheric transport can allow these pollutants to move large distances away from its location of formation, which is essentially governed by the position of synoptic-scale (large-scale) pressure systems. Wind also strongly affects the vertical mixing of air. High pressure, anticyclonic
conditions are often associated with calm weather, which reduces pollution dispersion.

Methods

The interactions between meteorology and air quality is relatively well understood theoretically, but few studies have used the UK's air quality measurements network and air quality forecast models to monitor the effects of meteorology on UK air quality.

Understanding the effect of meteorology on pollution accumulation and dispersion/removal is important for improving public health, particularly through the improvement of national air quality forecasts. The AQUM is also used to inform policy decisions on emissions controls (i.e. by DEFRA), and for research into the wider effects of poor air quality.

The Met Office’s AQUM (Air Quality in the Unified Model) is operationally used to produce regional air quality forecasts, allowing air quality warnings to be delivered to the public. The model considers the emissions of pollutants, the transport and dispersion of pollutants by winds, the chemical reactions amongst reactive gases and aerosols, and the removal processes, such as wet and dry deposition.

The skill of AQUM forecasts is tested by comparison against surface observations. The Automatic Urban and Rural Network (AURN) provides measurements of pollutant concentrations on an hourly basis at approximately 200 sites across the UK. Observations from MODIS on NASA’s TERRA and AQUA satellites monitor the ambient aerosol optical depth (AOD) globally.

Data for the period 2006-2010 was used in this study and two poor air quality episodes were analysed: 18th-20th July 2006 and 26th-28th March 2007.

Results

Comparison of observations and modelling showed the ability of the Met Office’s regional air quality model (AQUM) to reproduce surface pollution concentrations, making it a practical tool for investigating the influence of synoptic meteorology on UK air quality. Some bias does exist however, with an NO2 negative bias of around 30 µgm-3 in cities (neutral away from cities) and an ozone positive bias of around 10 µgm-3 (figure 1).

Figure 1. Difference between gridded midday AQUM output and gridded midday AURN data (AQUM – AURN), during July 2006 for (a) NO2 and (b) ozone, and during March 2007 for (c) NO2 and (d) ozone.

The influence of the UK synoptic weather regime on poor UK air quality episodes was indicated by the model to be significant.

Poor air quality episode: 18th-20th July 2006

During 18th-20th July 2006, low winds and recirculating air flow over the UK and northwest Europe allowed pollution to accumulate close to UK emission sources6, particularly close to the city hotspots in southern Scotland, central England, Bristol and London (figure 2).

High pressure was present over central Europe and the UK and synoptic set up caused there to be an easterly flow over the UK and allowed heatwave conditions to build across large parts of Europe and UK during this episode.

High NO2 levels remained close to sources due to its short summer lifetime, reaching 40 µgm-3 in the city hotspots. Being a summertime episode and with high levels of ozone precursors, ozone concentrations were also high (>130 µgm-3).

Figure 2. Gridded midday AQUM output for the 18th-20th July 2006 episode, showing the absolute values for (a) NO2 and (b) ozone, and the difference from the 5-year mean for (c) NO2 and (d) ozone, with surface wind vectors overlaid.

PM2.5 had concentrations of around 25 µgm-3 across the UK. PM2.5 was greatest over the north coast of Northern Ireland (> 50 µgm-3), over the Irish sea (~40 µgm-3), and over the English Channel (>50 µgm-3). PM2.5 was slightly higher than the average over the city hotspots noted with NO2, with PM2.5 concentrations of ~30 µgm-3. PM2.5 concentrations increased over the whole UK, by 15 µgm-3 on average. The greatest difference compared to the mean was on the north coast of Northern Ireland (~45 µgm-3 greater).

Poor air quality episode: 26th-28th March 2007

During 26th-28th March 2007, a well-defined easterly synoptic regime existed across most of Europe. The air mass arriving in the UK, from the ground to 3km up (above the boundary layer), passed long distances over Europe. This allowed pollution to build as air flowed over the continent, especially over highly-industrialised areas such as the Benelux region, and caused the long-range transport of pollution towards the UK (figure 3), including NOx and PM.

Figure 3. Gridded midday AQUM output for the 26th-28th March 2007 episode, showing the absolute values for (a) NO2 and (b) ozone, and the difference from the 5-year mean for (c) NO2 and (d) ozone, with surface wind vectors overlaid.

With high levels of NO present over the UK, the breakdown of ozone dominated over ozone formation, so ozone concentrations were lower than its average for this time of year. This was particularly the case to the west of UK city hotspots, due to the easterly winds causing streaks of ozone negative anomalies to form, collocated with NOx positive anomaly streaks.

There appears to be a sharp north-south gradient in ozone concentrations, cutting through central Northern Ireland, with higher concentrations to its west (>80 µgm-3) and lower concentrations to its east (<80 µgm-3).

The north-south sharp concentration gradient is also present in the PM2.5 output, but does not extend as far north as for ozone; instead cutting across southern Scotland and down the east coast of England. Unlike for ozone, there are lower PM2.5 concentrations to the west and north of the divide (~15 µgm-3) and higher PM2.5 concentrations to the east of the divide (~40 µgm-3). PM2.5 is greater than the mean, by about 35 µgm-3 to the east of the divide and by about 10 µgm-3 to the west of the divide.

The dividing boundary between higher and lower ozone in is likely due to the presence of a front separating two different air masses. Synoptic analysis charts show this north-south front moving eastwards towards the UK. The air mass behind (to the west of) the front progress over the North Atlantic, so has very low pollution levels. Hence, PM2.5 is low behind the front, and less NO2 means less is available to photochemically breakdown to form ozone. However, there is no sharp change in NO2 along this frontal line, but this may be due to NO2 much shorter lifetime.

WHO guideline threshold pollution values

The World Health Organization (WHO) air quality guidelines provide safe limits for the key air pollutants that pose health risk, based on expert evaluation of current scientific evidence. The bias in the AQUM must be considered when comparing the pollution concentrations to the WHO limits. The WHO guideline values are for mean concentrations over a number of hours, depending on the lifetime of the pollutant; this must be taken into account when comparing the WHO thresholds to the three-day episode midday means used in this study.

Although NO2 concentrations were well within the WHO 1-hour NO2 mean threshold value of 200 µgm-3 during 18th-20th July 2006, the elevated NO2 levels are likely to have had some impact human health. Being a summertime episode and with high levels of ozone precursors, ozone concentrations were very high, reaching 130 µgm-3 in the south of England. The WHO ozone guideline value of 100 µgm-3 (8-hour mean) was therefore greatly exceeded, hence having a significant impact on human lung function. The WHO PM2.5 threshold value of 25 µgm-3 (24-hour mean) was also exceeded during this event.

NO2 concentrations were greater than 30 µgm-3 in the city hotspots during 26th-28th March 2007 - below the WHO NO2 threshold value of 200 µgm-3 (1-hour mean). The WHO ozone threshold (100 µgm-3 8-hour mean) was also not exceeded during this episode, reaching 65 µgm-3 for large parts of the UK. However, PM2.5 rose above the WHO PM2.5 threshold value of 25 µgm-3 (24-hour mean), to over 30 µgm-3 in large parts of England and Wales. Thus PM, in particular, is likely to have had a serious impact on human health during this poor air quality episode.

Conclusions

This study concludes that the processes of pollution accumulation and long-range transport have a strong influence on UK air quality under favourable synoptic meteorology. It highlights that background air quality is not just a local scale issue and is affected by European pollution sources under easterly flow regimes.

During 18th-20th July 2006, low winds and recirculating air flow over the UK and northwest Europe allowed pollution to accumulate close to UK emission sources, particularly close to large cities.
During 26th-28th March 2007, a well-defined easterly synoptic regime existed across most of Europe. This allowed pollution to build as air flowed over the continent, especially over highly-industrialised areas such as the Benelux region, and caused the long-range transport of pollution towards the UK, including NOx and PM.

WHO safe limit thresholds of NOx, ozone and PM were greatly exceeded in these two case studies.
The conclusions of this study will aid the interpretations of air quality forecasts and the effects of different weather regimes on UK air quality; for example, helping UK authorities prepare for, and help mitigate, the health impacts of poor air quality.

Limitations and future work

Analysis was limited by systematic bias of the AQUM model, causing an overestimate of the absolute values of ozone and an underestimate of the absolute values of NO2. This affects the assessment of the individual impacts of the pollutants. The multiple factors affecting air quality results in it being extremely difficult to separate effects of synoptic meteorological processes on air quality from other factors, including the effect of local emissions and local meteorology, vertical atmospheric mixing and pollution diurnal cycles.

This study has multiple branches for future work. Most importantly, it would be interesting to determine why the NO2 positive anomaly streaks and ozone negative anomaly streaks seen over the Irish sea (figure 2 & 3), westwards of the city hotspots of Bristol, Liverpool and Glasgow, are not present downstream of other big cities.

Key references

1. Holgate, S.T. 2017. ‘Every breath we take: the lifelong impact of air pollution’ – a call for action. Clinical Medicine. 17(1), pp.8–12.
2. DEFRA. 2010. Valuing the Overall Impacts of Air Pollution. [Online]. [Accessed 02 February 2017]. Available from: archive.defra.gov.uk.
3. Jones, A.M., Harrison, R.M., Baker, J., 2010. The wind speed dependence of the concentrations of airborne particulate matter and NOx. Atmospheric Environment. 44, pp.1682–1690.
4. Pope, R.J., Butt, E.W., Chipperfield, M.P., Doherty, R.M., Fenech, S., Schmidt, A., Arnold, S.R. and Savage, N.H. 2016. The impact of synoptic weather on UK surface ozone and implications for premature mortality. Environmental Research Letters. 11(12), p.124004.
5. Savage, N.H., Agnew, P., Davis, L.S., Ordónez, C., Thorpe, R., Johnson, C.E., O’Connor, F.M. and Dalvi, M. 2013. Air quality modelling using the Met Office unified model (AQUM OS24-26): model description and initial evaluation. Geosci.Model Dev. 6, pp.353–72.
6. Prior, J., and Beswick, M. 2007. The record-breaking heat and sunshine of July 2006. Weather. 62, pp.174–182.
7. World Health Organization, 2006. Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide. World Health Organization.
8. Camalier, L., Cox, W. and Dolwick, P. 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmos. Environ. 41, pp.7127–37.

Can Renewable Energy and Machine Learning Enable an Energy Independent India?

These are high-times for Artificial Intelligence (AI), specifically its Machine Learning (ML) form. Both are increasingly coming into the public consciousness, as they are applied to an array of sectors. Applications in the Power sector, though less visible than say physical robotics, are the most fundamental, especially in an Indian context, as its evolution will also transform many other sectors.

As a tropical country we are endowed with abundant sunshine and wind, but still import much of our energy in the form of oil and coal. The current government is pushing to transition us to a ‘Solar Nation’, in no small part to reduce the national import bill. Achieving this could accelerate the Make in India campaign, because national energy independence will mean the cheapest power possible. The vision is for a 100% renewables-powered grid, giving 100% reliability of supply, enabling prices to be a fraction of what they are today.

Technically India already has ample power, with approximately 300GW of installed capacity, and only 150GW of peak demand. However, this is not a practical reality. Rather, it reflects the challenges of managing supply and demand. Our per capita consumption remains low because most people still have only a limited access, in terms of connectivity and availability. Further, though abundant, Solar and Wind are inherently variable, and will exacerbate grid-volatility as integration increases. Even with the small penetration of renewable generation today, the intermittency has caused a significant rise in balancing costs, which in the future could be huge.

Added into the mix, the future will see increasing Distributed Energy - both localised green generation, and storage. Though in some instances this will take pressure off the central grid, for example by reducing the load on transmission infrastructure, overall it will increase volatility. Today generation and consumption locations are essentially fixed, we at least know where they occur, even if not quite what volumes and when. But we have little idea of the future profile, especially with Electric-Mobility coming into the frame.

It is already difficult for humans to manage this complexity, but these challenges aren't dire portents for the sector. What we need is a modern approach to address key deficiencies, by analysing disparate real-time data for optimal decision-making. This must be cost-effective, and of all available avenues software will always be much cheaper and more efficient than physical buildout.

This is where AI & ML excel, with by far the greatest benefit-to-cost ratio. Crunching the exponentially growing volumes of systems data required to balance gird-volatility, forecast future trends, optimise existing assets and plan future infrastructure. They are already handling these issues in the real-world, from forecasting of solar generation to prices on the power markets, and even detection of power-theft.

In a renewable and distributed energy future, AI & ML can manage key aspects of the energy system, with human-oversight replacing human-intervention for the highest decision levels. It thus has the capability to help us realise the vision of an energy-independent India, with affordable power for all.


Credit to Niladri Roy for co-writing this article.

Human vs Machine Learning

In previous articles I've written quite a bit about Artificial Intelligence and Machine Learning. This article hopes to explain these terms in fine detail and compares them to the workings of the human brain.

Learning Machines - Not So New

Though the terms Artificial Intelligence (AI) and now Machine Learning (ML), have become widely vaunted for an array of areas in industry and life, they conceptually have a very long history, being prevalent in prior decades and even centuries. We can actually go back several millennia, to the ancient world, for the first instances. 

There have been examples of ‘intelligent’ machines peppered throughout ancient mythology, and even an allegedly real one in ancient China. Given there is still limited understanding of what AI and ML actually are, examples from myth can help with differentiating and understanding both concepts, and ultimately explore ML in more detail.

The Automatons of Hellenistic Greek mythology, were metal statues crafted by Hephaestus, the smith-god of the Olympian deities. The best of them could think and feel just as humans do, like the Kourai Khryseai - ‘golden maidens’. This is the most common perception today of AI (just without the moving limbs, or heart and charm).

But more usually, the Automatons were only able to think, feel and ‘learn’ within the boundaries of what they were designed for. Like those created to assist Hephaestus in his weapons forge up on Mount Olympus. Or Talos, the giant who patrolled the shores of Crete to defend against attacking fleets. Or the Keledones, which were also golden maidens, but only able to sing and learn new songs and music. All were excellent at doing and learning about their respective tasks (or datasets as it were), but not much else. So the Automatons are pretty apt early examples of what AI, and particularly ML actually are.

What Are Artificial Intelligence and Machine Learning?

In simple terms, Artificial Intelligence is the broad notion of machines being able to perform intellectual tasks in a manner similar to human cognition. This is usually imagined to be the much conjectured ‘technological singularity’, where human cognitive capabilities are surpassed, possibly resulting in drastic and dire existentialist threats to humanity (so say messers Hawking, Musk, Gates, and co anyway). This concept is also known as ‘Artificial General Intelligence’ (AGI), which though of course a future possibility - whether malevolent, benevolent, or indifferent - is far removed from current reality. One reason being that human intelligence is not singular in nature, nor is the physical brain yet fully understood.

Figure 1. Types of human intelligence.

Machine Learning is a subset of AI, based on giving machines access to datasets, then letting them ‘learn’ from these. Specific problems are solved without the need for explicit programming, by feeding in data and training the system to derive information out of them. Once trained, any new data given to a machine will yield desired outputs - solutions to the initial problem. Starting from this basic and well-defined premise, better insights can be gained as to how a machine learns.

Current (mis)Perceptions

There is very varied understanding of AI and ML. Both are frequently misperceived as interchangeable, adding to the confusion. AI is indeed a rather nebulous term, but ML is a well-defined subset of it with tangible applications. It’s really ML that is making great strides in an ever more diverse range of fields.

However, because of the misperceived interchangeability, customers – in the energy sector and more widely - expect ‘AI’ to deliver everything now. Customers are often unaware of the numerous remaining incremental steps that are necessary to achieve their respective visions. As with all technology, and indeed life, development takes time, an often frustrating universal truth.

Supervised, Unsupervised, and Reinforcement Learning

Machine learning is commonly split into three main types: supervised, unsupervised, and reinforcement learning. A simple example of supervised-learning is having labels for sorting colours, or animals, whereby the machine is supervised (by a human) in how it categorises these. In unsupervised learning, the machine comes up with its own labels and logical groupings. So just like the mythological Automatons, a supervised machine can adapt and learn within the parameters it is given. However, it can only expand its range, by reprogramming through external intervention - from Hephaestus or a human respectively.

Also in unsupervised learning, whereas a human may use groupings such as age for classification, a machine may form entirely different classifications from the same dataset that are otherwise unapparent to a human. In reinforcement learning, the algorithm tries to find the best way to earn the greatest reward. These now lead to to a more dynamic and interesting space, where the lines get blurred a little.

Deep Learning

Deep Learning is a subset of Machine Learning, which is closer to human learning - give a machine massive amounts of data and let it come up with something by itself. This can be both supervised and unsupervised, so some real projects dedicated to deep learning from Google Brain’s DeepMind, can illustrate these.  

Neural networks are designed with the purpose of modelling or mimicking the human brain, better than previous approaches. One such network was given access to unlabelled YouTube images, and nothing else, to see what it came up with. Eventually, the machine learned to identify….cats! Machine image recognition is quite analogous to the human visual cortex and neurological processing, so has been well replicated. Though humans can still pick a face out in a crowd more easily, machine image recognition has made great strides recently with the advent of convolutional neural networks, which are specifically designed for shift-invariant object recognition in images.

Figure 2. Convolutional neural network for image recognition.

Of more interest and complexity is AlphaGo. The game of Go was invented in China over 2,500 years ago, and is believed to be the oldest board game still being played today. It has simple rules but has long been regarded as a difficult challenge within the field of AI and ML. Some of the complexity is attributed to the sheer number of possible game variations, which are on a scale similar to the number of atoms in the universe. Building a system that masters this game requires considerably more components that mimic human thought processes than other games, such as chess.

The AlphaGo neural network was given no rules, just historical games and datasets. The network was able to incrementally learn the rules, or ‘policies’, through self-simulated games, and what the best moves were to defeat a human. Millions of games were simulated, with high scores used to justify, or gradually reinforce its learned behaviour. So in very similar manner to a human, it post-rationalised, learning tactics to get high scores, which reinforced its behaviour.

Previously, it was generally believed that a decade or more would need to pass before a machine could defeat a professional player. Yet in 2016, a 5-match human vs machine contest, between one of the best professional human players and AlphaGo, saw the machine unexpectedly triumph 4-1. This was through finding ways of winning which ran contrary to professional convention, and has actually redefined the way the game will be played in the future by humans.

While the original AlphaGo needed the expertise of humans to get to such a level, the latest evolution of the program, AlphaGo Zero, has been able to consistently demonstrate superhuman performances well exceeding its predecessor. All without any human inputs or historical datasets, by knowing only the rules of the game and by playing games with itself. This is definitely a significant achievement in both ML, and wider AI.

Explicability, Predictability, and Volatility 

Despite these strides, there is still no way to understand why machines make any of these decisions. Nor can we really get any insight either, given the inherent black box nature of these algorithms. This lack of explicability is significant from a human perspective. Humans still always look for explicability and predictability - formulas for ‘what’s going on behind the scenes’. Classical ML techniques which were derived in the 80’s, are all statistical in nature – support-vector-machines (SVM), tree-based, or knowledge-based algorithms. In other words, they can be grasped by (capable) human minds. It is very challenging for humans to accept anything otherwise. Even with all the volatility and uncertainty around us in daily life, humans are neurological deeply hardwired to search for patterns and predictability.

Yet almost dichotomously, the human mind also functions in a very similar ‘inexplicable’ way to these black-box machines. What is called ‘gut’ feeling or instinct, is simply the brain making superspeed assessments based on a lifetime of data points and bypassing conscious consideration. We can’t explain it, but we do trust it without hesitation, even when we can’t fathom the pattern. So why not the same for machines? Machines can and have been proven to see patterns that humans can’t – patterns that sit outside of classical information theories and conventional explicability.

As the share of renewable generation on the grid increases, so will grid-volatility. So ‘inexplicable’ ML that can process patterns at super-speeds far beyond what humans are capable of, is exactly what will be needed. ML is an essential tool to manage extreme grid volatility, through forecasting of weather and demand based on past behaviour and data points. ML doesn’t need to be explicable to be useful, and its applications in forecasting within the context of energy specifically, will be delved into more deeply in the follow up article. This will include the 5-to-10-year incremental journey from reality today to where customers think capability should be.

 Figure 3. The Turing Test

The Big Question

With ‘creativity’ and learning in mind, can machines mimic that so cherished of human traits, the one held up as the highest of virtues and only possible with a ‘soul’? Well let’s flip the question around - how unique is human creativity really? When analysed, much of what humans produce is surprisingly repetitive. Take Art, even with its seemingly endless variety, it all still falls within set bounds - for a machine with enough processing power. There are already examples of computers painting and drawing pictures, which are indistinguishable from those by a human hand and heart. Then there’s music, which has wide and ubiquitous commonalities across genres and cultures.

Human creativity then maybe isn’t that special, and of course “all art is quite useless”, as Oscar Wilde famously said. However, there is another dimension that should be separated out. The preceding examples are all about production, rather than interpretation, and understanding ‘why’ humans react to art is still beyond machines (and it should be noted, many humans too). Another aspect of human creativity is the ability to do research, design and innovation - the highest level of insight, generated from a combination of multiple cognitive faculties, or modules. This is the ultimate ability of the human brain, which is what AGI aims to achieve. Currently, there is narrow AI within bounded modules, but no sense yet of how to plug them together. Though raw computational power has already far surpassed human cognitive capacity, it still doesn’t enable multidimensional integration of these modules. Development is certainly far from anything that could pass the Turing Test. This experiment was hypothesised by arguably the Godfather of ML, Alan Turing, to assess a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from a human.

Conclusion

Hence true machine creativity is still far from possible yet - a neural network will not learn to paint then suddenly decide to become a sculptor unprompted, nor write a new company energy policy. So, is human learning better than machine learning? The ideal scenario is to combine both - ML’s ability to crunch enormous volumes of data, with the still uniquely human ability of creativity, to achieve the highest levels of decision making.

Credit to my colleagues Niladri Roy, Rohan Nongpiur, Sho Akama and Aseem Khanduja for co-writing this article.