ENDGame: A new dynamical core
for seamless atmospheric prediction
July 2014
Contents
1 Executive summary
2
2 Introduction
3
3 ENDGame description
7
4 Improvements to physics, resolution, data assimilation and satellite data usage
9
4.1 Improvements to the physical parametrizations . . . . . . . . . . . . . . . . . . . . .
9
4.2 Resolution upgrade to the deterministic global NWP model and data assimilation . .
9
4.3 Additional package of satellite changes . . . . . . . . . . . . . . . . . . . . . . . . .
10
5 Performance benefits
12
5.1 Improved representation of extra-tropical and tropical storms . . . . . . . . . . . . .
12
5.2 Improved representation of tropical equatorial waves and the Madden–Julian Oscillation 17
5.3 Improvements to the model representation of gravity waves . . . . . . . . . . . . . .
19
5.4 Improved forecasts of aviation “jet level” winds . . . . . . . . . . . . . . . . . . . . .
19
6 Conclusions and future direction
23
Appendix A List of improvements to the model’s physics
24
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1
Executive summary
At the Met Office we are continually developing our weather and climate models to improve
their accuracy and provide new capability. Usually these upgrades are the combination of
a number of small improvements made at short but regular intervals that contribute to the
development of the models. This year, however, we have made a major change by adopting
the new “ENDGame” dynamical core. This is the culmination of over 10 years work and is
an important step in our ongoing model development plans. In this report we describe the
motivation for ENDGame and the benefits that it brings.
Modern computer models of the atmosphere include many complex physical processes that each
have local influences and feed back into the general circulation. At the heart of these models,
however, is the solution of the dynamical equations of motion (Newton’s laws applied to a gas). For
this reason, the model component that solves these equations is called the “dynamical core”. The
dynamical core used in the Met Office Unified Model prior to July 2014 is known as “New Dynamics”
and was first introduced in 2002. The introduction of New Dynamics was a major step forward as
it solved an unprecedentedly accurate set of equations. This allowed us to pursue our seamless
modelling approach by using the same dynamical core for very high-resolution weather forecasts
as for centuries-long climate projections. To solve the New Dynamics equations both stably and
quickly, we needed to apply artificial damping, which removes detail from the forecasts. In addition,
its scalability (the increased speed achieved by using more computer processors) was approaching
its limit, which impeded our ability to run higher resolution models on our supercomputers.
To improve these aspects of the model, whilst maintaining the benefits of New Dynamics, our
Dynamics Research team and Professor John Thuburn from the University of Exeter developed
“ENDGame” (Even Newer Dynamics for General atmospheric modelling of the environment). This
development took nearly 10 years and in the past two years staff from across the Met Office
have tested and evaluated its impact and prepared our operational systems for its implementation. ENDGame is now being used for global weather and climate prediction work and we will move
our regional and seasonal prediction systems to use ENDGame over the next year.
In global models, ENDGame’s improved accuracy and reduced damping produces more detail in individual synoptic features such as cyclones, fronts, troughs and jet stream winds.
In the tropics, a combination of ENDGame, increased resolution and improvements to the
model’s physics provides unprecedented improvements to our predictions of tropical cyclone intensity and position. ENDGame’s improved scalability has allowed us to increase
the resolution of our global weather forecasts which provides additional improvements to
forecast accuracy. It will also allow us to further increase the model’s resolution when our
supercomputing capability increases.
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2
Introduction
At its simplest the Earth’s atmosphere can be considered to be a rotating body of gas subjected to
heating and cooling. The heating and cooling arises from the absorption, emission and subsequent
reabsorption of solar energy. In particular the heating and cooling varies in both time and space.
This variation causes changes in pressure which drive the winds. Newton’s second law of motion
together with the first law of thermodynamics and conservation of mass (the governing equations)
determine how the gas responds to the heating and cooling (see Tech Box 1). This simple picture is
complicated by the presence of the liquid and frozen phases of water and associated latent heats,
as well as surface emissions and chemical reactions.
Tech Box 1: Dynamical core (dry) equations
Newton’s second law of motion:
Dv
+ 2Ω × v + cp θ∇Π − g = Sv
Dt
The first law of thermodynamics:
Dθ
= Sθ
Dt
Conservation of mass:
Dρ
+ ρ∇ · v = 0
Dt
Equation of state for a perfect gas:
Π(1−κ)/κ =
R
θρ
p0
v is the three-dimensional wind vector; Ω is the Earth’s rotation vector; cp is the specific heat
capacity of air at constant pressure; θ is potential temperature; Π is Exner pressure; g is
the gravitational acceleration; Sv represents the effects of the physical parametrizations on
v; S θ represents the effects of the physical parametrizations on θ; ρ is the density of dry air;
κ = R/cp where R is the gas constant; and p0 is a reference pressure value. D/Dt indicates
the rate of change following an air parcel.
In principle, the governing equations tell us everything about the motion of the gas, from planetary
scales down towards the molecular scale. In practice however, we have to solve the equations on
a computer. To do this we digitise the equations by averaging them over a relatively large box (a
grid box). The size of this box is called the resolution of the model. Similar to the definition of a
television screen, increasing the resolution of the model gives sharper more realistic features and
finer, smaller scale processes are resolved. It is the dynamical core of the Unified Model (UM)
that is responsible for solving Newton’s laws on these grid boxes. The dynamical core is therefore
responsible for the larger scale features (be that fronts and high pressure systems in a global model,
or for example sea breezes in a high resolution, limited area model). The effects of the “missing”
processes, those that are either diabatic or occur at scales that cannot be resolved by the grid boxes,
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are provided by the physical parametrizations; these determine the details close to the resolution of
the model, such as convective clouds and details of the rainfall.
No matter how accurate a forecast is, its value is strongly dependent on the timeliness of its delivery
— the perfect forecast today for yesterday’s weather is of no use to anyone! To meet the demands
of our customers, the operational forecast system has to process a vast range and number of
observations to provide the starting point for the model and then go on to produce a forecast for the
entire globe for 7 days ahead, all within approximately 1 hour.
This is a significant challenge and to achieve it the equations used in almost all dynamical cores are
only approximations to the full equations. An important and distinguishing aspect of the evolution
of UM dynamical cores has been the gradual removal of these approximations. This has led to us
using ever more accurate equations but perhaps more importantly it means that the same dynamical core can be used across a wide range of scales, from the large planetary scales of 1000s of
kilometres down to small scale weather events at a kilometre or even less. This approach has been
fundamental to the Met Office seamless modelling strategy.
A significant step in delivering that strategy was the development of the current dynamical core
of the UM, known as New Dynamics (Davies et al.1 ). It became operational in 2002 and was the
first operational model to solve the virtually unapproximated equations — the deep-atmosphere,
nonhydrostatic equations (see Tech Box 2). To do so whilst respecting the strict time constraints
for delivery of the forecasts was a remarkable feat. This was achieved by implementing state-ofthe-art numerical methods but applied innovatively well beyond their usual scope. These numerical
methods are referred to as “semi-implicit semi-Lagrangian” (see Tech Box 3).
New Dynamics has served us well over more than a decade: not only have we continued to improve the skill of our large scale forecasts at the rate of 1 day lead time per decade (so for example
today’s 3 day forecast is as accurate as the 2 day forecast was 10 years ago) but we have seen
the introduction of a very high resolution (1 12 km) model over the UK which provides unprecedented
levels of detail to our forecasters. Our seasonal forecast (GloSea) demonstrates skill in predicting
large-scale circulation patterns in the tropics (e.g. associated with the El Niño Southern Oscillation) and extra-tropics (North Atlantic Oscillation — Scaife et al.2 ) and our climate predictions have
demonstrated high levels of skill compared to other coupled models across a range of metrics (e.g.
Martin et al.3 ). However, to achieve what it did, with the knowledge and understanding available
at the time of its design, certain aspects of the numerical schemes used in New Dynamics are unstable (which means that the computer program sometimes fails to successfully complete). This
1 T Davies et al. “A new dynamical core for the Met Office’s global and regional modelling of the atmosphere”. In: Quarterly
Journal of the Royal Meteorological Society 131 (2005), pp. 1759–1782.
2 A. A. Scaife et al. “Skillful long-range prediction of European and North American winters”. In: Geophysical Research
Letters 41 (2014), pp. 2514–2519.
3 G M Martin et al. “Analysis and Reduction of Systematic Errors through a Seamless Approach to Modeling Weather and
Climate”. In: Journal of Climate 23 (2010), pp. 5933–5957.
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Tech Box 2: Approximate vs unapproximated equations
Hydrostatic vs nonhydrostatic: the hydrostatic approximation assumes that the pressure
at any location is determined by the weight of air above that point. It therefore ignores the
effects of the acceleration of vertical motion. This is a very accurate assumption for resolved
scales greater than 10 km but becomes increasingly less accurate at smaller scales. Both
New Dynamics and ENDGame are nonhydrostatic models and so do not suffer from being
limited to large scale applications.
Shallow vs deep: the shallow approximation exploits the fact that the depth of the troposphere (the region of the atmosphere primarily responsible for our weather) is much shallower than the radius of the Earth. This means that locally the curvature of the Earth can
be neglected. This is a very accurate approximation which only becomes questionable if we
are interested in detailed processes much higher up in the atmosphere, for example space
weather. However, associated with the shallow approximation is a simplification of the Coriolis effect — the important influence that Earth’s rotation has on our weather and climate.
This is a much less accurate approximation, increasingly so towards the equator, particularly
for deep tropical vertical motion. Removing this assumption was the primary motivation for
removing the shallow approximation from the UM and making it a deep model. Both New
Dynamics and ENDGame are deep-atmosphere models.
effect was partly mitigated by the addition of artificial damping and diffusion (which smooths out
the solution compared to reality) but the model still suffers from occasional failures, which require
additional computational or human resource to resolve. To address these issues, a programme of
research, known as ENDGame (Even Newer Dynamics for General atmospheric modelling of the
environment), was instigated soon after New Dynamics became operational. More than a decade
later that work is now coming to fruition as ENDGame replaces New Dynamics in our global model
configurations of the UM.
This report describes the ENDGame dynamical core and its benefits for performance on both
weather and climate prediction timescales. On 15 th July 2014 ENDGame became operational
in our global (deterministic4 and ensemble5 ) Numerical Weather Prediction (NWP) systems following completion of operational trialling. Also included in the global deterministic model upgrade and
briefly described in this report are:
• An increase in the horizontal resolution from 25 km (labelled N512) to 17 km (N768).
• Important revisions to the physical parametrizations.
• Improvements to the resolution used in our data assimilation process to define the initial state.
• Improved use of satellite data in this data assimilation.
4A
single forecast run with the highest resolution affordable from our best estimate of the initial state of the atmosphere.
runs (25-50) of a model (usually at lower resolutions) from slightly perturbed initial conditions and with small
perturbations to the model’s physical formulation. Ensembles are designed to capture the inherent uncertainty in the measurement of the initial atmospheric state and in our knowledge of the model physics to provide a probabilistic estimate of the
risk of severe weather.
5 Multiple
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Tech Box 3: Semi-implicit semi-Lagrangian
Crucial to making good forecasts is the accurate transport of the properties of a parcel of
air by the wind. Many methods for achieving this can be impractically expensive on a global
latitude-longitude grid. However the semi-Lagrangian (SL) method is both accurate and
practical on such grids. It works by tracing back where a parcel of air has come from and
evaluating its properties at that point by interpolation. The downside to this method though
is that it does not exactly conserve the quantity being transported.
The remaining terms of the equations (those not associated with transport) are responsible
for the various wave-like motions of the atmosphere: planetary waves, gravity waves and
sound waves. Each of these modes is more or less important for the evolution of the
atmosphere but each needs to be handled stably by the numerical scheme. For example
sound waves play a negligibly small direct role in determining the weather yet they are the
fastest propagating waves and therefore could dramatically increase the time it takes for
the model to run. This problem is overcome by applying the semi-implicit (SI) scheme to
these terms. This involves averaging each term over the time step. The various waves are
then stable but only those that vary slowly enough (which are those waves of interest and
importance) are handled accurately.
It is the use of this combination of schemes, the SISL approach, that allows both New Dynamics and ENDGame to achieve the stringent efficiency requirements for operational use.
Finally, the ENDGame dynamical core will also be rolled out in our (1 12 km) UK NWP system and
global seasonal forecast (GloSea) in early 2015 and is already being used as the basis for the
atmospheric component of the next generation Earth-System climate prediction model (UKESM1)
being developed by the Met Office in collaboration with UK academia.
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3
ENDGame description
ENDGame solves the same virtually unapproximated equations as New Dynamics and in essence
uses the same numerical approach. However, a number of significant changes to these numerical
schemes has been implemented that improves its behaviour and performance.
The most significant of these changes is the introduction of an iterative approach to solving the
set of equations. Essentially ENDGame uses something similar to the New Dynamics solution as
only a first estimate to its solution. From this (already very accurate) first estimate it then creates
successively better solutions, building on the knowledge of each earlier estimate (see Tech Box 4).
Tech Box 4: Iterative approach to the model time step
New Dynamics: This schematic illustrates
how the UM structures the tasks required
within a single model time step, where the
solid lines denote the approach used in New
Dynamics. “Slow” physical processes (such
as radiation, cloud and precipitation) are
applied to the solution from the previous time
step, whilst “fast” processes (such as sub-grid
turbulence and convection) are applied after
transport from the SISL scheme. Finally,
we solve for the atmospheric pressure. In
New Dynamics, this solution for the pressure
contributes to a large proportion of the total
computational cost.
ENDGame: The dashed arrows denote the
parts of the time step that are iterated in
ENDGame. The iterative approach to the “inner loop” allows a simpler, more scalable solution for pressure. Iterations of the “outer loop”
provide improved estimations for the end of
time step quantities to be used within the SISL
scheme, improving the numerical accuracy of
the final solution. Currently, we apply 2 iterations in each of these loops.
It might be imagined that this would significantly increase the cost of ENDGame compared with
New Dynamics and this was indeed a major concern during its development. However, there are
two factors that mitigate that cost. The first is that the iterative nature of the scheme permits us to
simplify certain aspects of each iteration whilst ending up solving the full equations without simplification. This reduces the cost of each iterative step considerably, but when run on a small number
of processors ENDGame is still more expensive than New Dynamics. This is where the second
mitigating factor comes in: ENDGame is significantly more scalable than New Dynamics (Fig. 1).
This means that if the number of processors on which the model is run is doubled then the time it
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5
New Dynamics
ENDGame
Strong scaling factor
4.5
4
3.5
3
2.5
2
1.5
1
20
40
60
80
100
120
140
160
180
200
Number of Power 7 nodes
Figure 1: Strong scaling plot for ENDGame and New Dynamics forecasts at N768 resolution on the IBM
Power 7 supercomputer. This shows the number of times faster the forecasts run on a given number of nodes
compared to a baseline forecast on 24 nodes. 1 IBM node contains 32 processors.
takes ENDGame to complete is closer to half the original time than New Dynamics. On the number
of processors used for a typical production forecast ENDGame runs in a similar or shorter time
than New Dynamics. Indeed this increase in scalability is essential to us being able to increase the
mid-latitude resolution of the global model from 25 km to 17 km and still produce the forecast within
1 hour; New Dynamics could not achieve that because of its much poorer scalability.
The cause of the improved scalability of ENDGame is two-fold. The first is the simplifications of each
iteration alluded to above; each iteration is more straightforwardly scalable than the full scheme used
in New Dynamics. A more significant cause is that whilst both ENDGame and New Dynamics solve
the equations on a latitude-longitude grid with an associated clustering of meridians near each pole,
ENDGame radically changes how different variables are stored near to the pole. This removes the
need to solve a complex equation at the polar point. This directly improves scalability by reducing
communication between processors at the pole, but also indirectly improves it by providing a more
numerically stable solution, which requires more communication-intensive filtering near the poles.
In terms of its impact on the quality of the model forecast, the main benefit of ENDGame comes from
the improved numerical accuracy discussed above. This provides the technical benefit of making
the model more computationally robust (which allows it to run without program failures) but with
minimal artificial damping and no artificial diffusion (less smoothing of the solution). The impacts of
this will be demonstrated later in this report.
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4
Improvements to physics, resolution, data assimilation and
satellite data usage
4.1
Improvements to the physical parametrizations
During the final stages of ENDGame’s development, we continued to pull through improvements
to the physical parametrization schemes, which in global models we have implemented alongside
ENDGame itself. A list of the most significant of these changes is included in Appendix A. In
presenting the impacts of ENDGame in Sec. 5, we highlight where the impact is largely due to
ENDGame alone (e.g. improvements to extra-tropical features and circulation) and where it is
due to a combination of ENDGame and physics changes (primarily in the tropics, where the improvements to the model’s convection scheme contribute to further increased variability). These
upgrades to the model’s physics schemes also provide significant improvements to other areas of
model performance, particularly the accuracy of forecasting “surface weather” parameters, which
are not discussed in detail here.
4.2
Resolution upgrade to the deterministic global NWP model and data assimilation
At the same time as implementing the ENDGame dynamical core and improved physics, the horizontal resolution of the global deterministic NWP model has been increased (Table 1) from N512
(corresponding to a grid spacing of approximately 25 km in the mid-latitudes6 ) to N768 (≈ 17 km
in the mid-latitudes). The model time step has been reduced accordingly, from 10 to 7.5 minutes.
The increase in horizontal resolution allows for more detail in surface features such as orography
(Fig. 2) and in the free atmosphere allows for more small-scale features (waves etc.) to be resolved,
improving the transfer of energy, heat and momentum between scales and improving forecasts.
Finally, alongside the upgrade to N768 in the model, we have increased the resolution of the data
assimilation component (4D-Var 7 ) used to provide the initial conditions for the model forecasts.
The resolution used in 4D-Var will go from N216 (≈ 60 km in the mid-latitudes) to N320 (≈ 40 km).
This increase in resolution helps to produce a more accurate initial analysis. The vertical resolution of the global NWP configuration remains unchanged with 70 levels in total, approximately 50
in the troposphere (the lowest 10-18 km of the atmosphere), approximately 20 in the stratosphere6 See
Table 1 for a description of the resolution notation.
dimensional data assimilation (4D-Var) is the technique used to provide an optimal and balanced initial state of the
atmosphere from which to run the global forecasts. The 4D-Var process produces an initial state by using a huge range of
remote sensing and in-situ observations of the atmosphere (land and ocean) combined in a statistically optimal way with a
”first guess” of the atmospheric state from a previous model run. It is four dimensional as it uses information across the 3
spatial dimensions (latitude, longitude, height) and also a 6 hr “window” in time. The outcome is an analysis that is our best
estimate of the current state of the atmosphere.
7 Four
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mesosphere (regions immediately above the troposphere) and a model lid at 85 km (where the
pressure is ≈ 0.1 hPa). Research is underway to assess the benefits of increasing vertical resolution as part of a future model upgrade on our next supercomputer in 2016-17.
Horizontal resolution (N number)
Grid points (EW×NS)
Physical resolution (NS)
Physical resolution (EW @ 50° N/S)
Physical resolution (EW @ equator)
N512 (New Dynamics)
1024×769
26.1 km
25.1 km
39.1 km
N768 (ENDGame)
1536×1152
17.4 km
16.7 km
26.1 km
Table 1: Details of the resolution upgrade to the N768 ENDGame grid. The N number refers to the maximum
number of 2 grid-point waves that can be represented in the east-west direction.
4.3
Additional package of satellite changes
The assimilation of observations to provide an accurate initial state is also a key component in
producing accurate weather forecasts. The final component of the changes made to the global
deterministic NWP system on 15 th July 2014 is the improved use of satellite (remote sensing)
observations. These include:
• Reduced thinning of IASI data, leading to a doubling of the data ingested.
• Reduced thinning of ATOVS data, leading to a 30% increase in the data ingested.
• Reduced thinning of scatterometer data, leading to a 50% increase in the data ingested.
• Improved assimilation of GPS Radio Occultation data, allowing for tangent point drift.
• Introduction of Meteosat-7 MVIRI clear sky radiances over the Indian Ocean.
• Changes to the snow analysis to make use of both the model’s snow depth and amount.
In tests, this package has been shown to provide a modest improvement to the global forecasts.
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Figure 2: The orography fields and schematic of the grid resolutions for the UM at N96, N144, N216, N320,
N512, and N768. The grid overlay on each model is shown at 8 times the actual grid box size for clarity, but the
relative increase between each resolution is accurate. Note the increased detail in the orography as we move
to higher resolution, for example over the Alps, East African highlands, Iran, Pakistan and Afghanistan. The
N144 resolution was used for operational forecasting in the early 1990’s, N216 from 1998-2005, N320 from
2005-2010 in and N512 from 2010 to present. N96 was the resolution used for HadGEM2 climate simulations
submitted to IPCC-AR5
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5
Performance benefits
In previous sections we described how the improvements in stability and scalability have improved
the efficiency of our global predictions. Here, we describe how the improved numerical accuracy and
reduced damping in ENDGame improve the quality of our global predictions and also highlight the
additional benefits arising from the improved physical parametrizations and increased resolution.
5.1
Improved representation of extra-tropical and tropical storms
One of the clearest benefits from ENDGame is an increase in atmospheric variability. This is manifest in improved details and intensity of large-scale storms in both weather forecasts and climate
predictions, which arises from the use of less artificial damping in the ENDGame formulation. A simple measure of the atmospheric variability (or “storminess”) is provided by the globally integrated
eddy kinetic energy
8
(KE — Fig. 3). This forms one component of the important energy stores
in the atmosphere originally described by Lorenz,9 the others being the zonal kinetic energy (KZ)
and the zonal and eddy available potential energy (AZ and AE) which measure how much energy
is available in the atmosphere for conversion into motions such as storms and winds (Fig. 3(a)).
As forecasts run forward in time they tend to lose KE due to the damping processes in the model
(b)
(a)
Figure 3: (a) Globally integrated Lorenz energy cycle showing zonal and eddy available potential energy
stores (AZ, AE) and zonal and eddy kinetic energy stores (KZ, KE). The arrows connecting the energy stores
show the various energy conversion terms (CA,CZ,CE,CK), while the arrows on the left are the generation
of available potential energy through diabatic heating (GZ,GE) and on the right the removal of kinetic energy
through frictional dissipation (DZ,DE), (b) Globally integrated eddy kinetic energy (KE) for various resolution
from a set of 12 3-day forecasts initialised from ECMWF analyses. KE for the independent ECMWF analysis
(green), New dynamics (red), and ENDGame (blue) are shown.
required to control numerical noise. This means that storms and associated winds are not as intense as they are in the real atmosphere. For New Dynamics the 3-day forecast KE is less than
8 A typical eddy described here is a large-scale atmospheric tropical or extra-tropical cyclone covering 1000s of kilometres.
9 E.N.
Lorenz. “Available potential energy and maintenance of the general circulation”. In: Tellus 7 (1955), p. 157.
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the ECMWF analysis10 (by around 8%) with a larger reduction at lower resolution (compare red and
green lines in Fig. 3(b)). The ENDGame 3-day forecasts (blue line in Fig. 3(b)) are a significant improvement with much higher levels of KE at all resolutions and a closer match to the analysis value.
We can also see the benefits of increasing the resolution of the global model (i.e. decreasing the
size of the grid boxes to provide more detail). Moving from 130 km (N96) resolution to 17 km (N768)
gives much more accurate values of atmospheric variability (KE — Fig. 3(b)). Averaged across a
number of forecasts the 60 km resolution ENDGame forecasts have higher levels of KE than even
the equivalent 17 km forecasts using New Dynamics. This 60 km resolution is already operational
in our seasonal forecasts (GloSea) and will be the typical resolution for our climate predictions for
the next IPCC assessment report (AR6). This ability to maintain higher and more accurate levels of
KE throughout the forecast range is a significant benefit from ENDGame and improves our ability to
provide accurate risk based predictions of extreme weather events in our global deterministic and
ensemble systems.
Extra-tropical cyclones
To investigate the impact of ENDGame on storms in the extra-tropics we have applied the method
Distance to analysis (deg)
(a) Distance errors NH
8
N96 New Dynamics
N216 New Dynamics
N320 New Dynamics
N96 ENDGame
N216 ENDGame
N320 ENDGame
6
4
2
0
0
20 40 60 80 100 120
Forecast range (hrs)
(c) Intensity difference NH
Intensity difference (10-5s-1)
Intensity difference (10-5s-1)
Distance to analysis (deg)
of Froude11 to track storms in our global UM predictions. Figure 4 shows the track and intensity
0.2
0.0
-0.2
-0.4
-0.6
0
20 40 60 80 100 120
Forecast range (hrs)
(b) Distance errors SH
8
6
4
2
0
0
20 40 60 80 100 120
Forecast range (hrs)
(d) Intensity difference SH
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
0
20 40 60 80 100 120
Forecast range (hrs)
Figure 4: Track error in degrees (top) and intensity drift measured in 850hPa vorticity (bottom) in all extratropical cyclones identified in the northern hemisphere (left) and southern hemisphere (right) from a set of 200
forecasts initialised from ECMWF analyses and run at 3 different resolutions (N96, N216 and N320) and for
the two different dynamical cores: ENDGame (dashed lines) and New Dynamics (solid lines).
10 An analysis is a best estimate of the initial state of the atmosphere at any time and is a combination of all available
observations and an initial estimate of the model state from a previous forecast. The analysis is also used as the starting
point for our forecasts.
11 L. S. R. Froude. “TIGGE: Comparison of the Prediction of Northern Hemisphere Extratropical Cyclones by Different
Ensemble Prediction Systems”. In: Weather and Forecasting 25 (2010), pp. 819–836.
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errors in extra-tropical cyclones as a function of forecast range from a set of 200 forecasts initialised
from ECMWF analyses. The different colours show results from simulations at different resolutions,
whilst the different line styles distinguish New Dynamics and ENDGame simulations. The spindown of extra-tropical cyclone intensity in the ENDGame configuration is significantly less than
that from New Dynamics and as with the global eddy kinetic energy, the impact of the change in
dynamical core can be as large as the impact of a change in horizontal resolution. For example, the
N216 (60 km) ENDGame cyclones are on average deeper than their N320 (40 km) New Dynamics
counterparts.
We also see improvements in the representation of extra-tropical cyclones at climate timescales
(Fig. 5). In particular, the underestimate of cyclone intensity in the Pacific and Atlantic stormtracks
in boreal winter (DJF) seen with New Dynamics is significantly reduced with ENDGame.
(a) New Dynamics NH DJF (b) ENDGame NH DJF
4
4
6
5
6
5
4
4
4
4
6
6
5
5
-2
-1.2
-0.4
0.4
1.2
2
-2
-0.4
0.4
4
-0.4
5
5
5
5
4
4
-1.2
2
6
4
-2
1.2
(d) ENDGame SH JJA
6
(c) New Dynamics SH JJA
-1.2
0.4
1.2
2
-2
-1.2
-0.4
0.4
1.2
2
−5
s−1 ) derived from cyclone
tracking in N216 atmosphere-only climate simulations. New Dynamics and ENDGame results for the northern
hemisphere are shown in (a) and (b) respectively with the equivalent plots for the southern hemisphere in (c)
and (d). The local winter results are shown for each hemisphere.
Figure 5: Bias (Model − ERA-Interim re-analysis) in cyclone intensity (units 10
Tropical cyclones
In the tropics, cyclonic systems (hurricanes and typhoons) tend to be more intense than their
mid-latitude counterparts and difficult to resolve at global model resolutions. The change to the
ENDGame dynamical core combined with the increased horizontal resolution and upgrades to the
model’s convection scheme significantly improves our ability to model these features.
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As an example of this we show N768 ENDGame and N512 New Dynamics forecasts of tropical
cyclone Amanda, a category 5 tropical cyclone that formed in the east Pacific during the latter part
of May 2014 and was the first hurricane of the season (Fig. 6). The N768 ENDGame 36 hour
forecasts provide a deeper tropical cyclone with more intense winds and precipitation near the
tropical cyclone’s core, in closer agreement with the observed storm which can be clearly seen to
have a well defined ”eye” in the NASA visible satellite imagery (Fig. 6(a)). A further example of
(a)
(b)
(c)
Figure 6:
Tropical Cyclone Amanda (a) NASA Aqua satellite view of Amanda (the first named
storm of the 2014 hurricane season) in the Americas southwest of Manzanillo, Mexico, on May 25
(http://earthobservatory.nasa.gov/IOTD/view.php?id=83778) (b) mean sea level pressure (contours), 10m
winds (arrows) and precipitation (colours) for the operational N512 New Dynamics 36 hour forecast initialised
at 00:00GMT on 26 May 2014, (c) the equivalent N768 ENDGAME+revised physics forecast.
the changes in intensity forecasts throughout the lifetime of a tropical cyclone is provided by Fig. 7,
which shows successive forecasts for the central pressure of Typhoon Bolaven (a severe typhoon
from the 2012 season) throughout its lifetime compared to the official estimates of its “observed”
pressure. The New Dynamics forecasts do not approach anything like the observed intensity for
Bolaven, apart from late on in its lifetime, when the cyclone was filling rapidly. The ENDGame
forecasts have much deeper central pressures, which in the deepening phase of the cyclone’s
lifetime are much closer to the observations. Secondly, we see that the pressures at the beginning
of each subsequent ENDGame forecast are not much deeper than in New Dynamics. This shows
that the 4D-Var analysis is not able to capture the intensities sustainable by the model and observed
in the true system. It is possible that this is due to limitations in the resolution or variability within
4D-Var, or due to the processing or methods of assimilation for the available observations. One
potential way to improve this in the near future may be to assimilate estimated cyclone positions
© Crown Copyright 2014
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Figure 7: Official estimates of the “observed” central pressure for Typhoon Bolaven in August 2012, compared
to successive forecasts in the N512 New Dynamics control and N768 ENDGame trial. The analysed pressure
for each forecast is marked as a dot at the beginning of its trace.
and central pressures from official tropical cyclone warning centres. Another interesting feature
highlighted in Fig. 7 is that both the ENDGame and New Dynamics forecasts continue to deepen
the cyclone until very close to landfall, whilst in reality the cyclone loses intensity several days prior
to this. It is likely that this is due to some missing or poorly modelled physics, such as coupling
to the sea surface temperature or some other boundary layer process. Although this leads to the
ENDGame forecast depth of the cyclone being deeper than the observations, the rates of both
intensification and decay are more realistic in this model than in the New Dynamics counterpart.
Averaged over many cases the tropical cyclone forecasts in N768 ENDGame show deeper intensities compared to their N512 New Dynamics counterparts (Table 2). We have produced these
statistics by tracking individual storms in trial12 periods and comparing their position and other features with those reported by official tropical cyclone warning centres. This shows a significant
increase in the average intensity of tropical cyclones, with the N768 ENDGame forecasts producing
an average central pressure more than 10 hPa deeper than the N512 New Dynamics forecasts, with
the average maximum windspeed being more than 13 kt faster and the average maximum vorticity
(measure of the“spin”) being higher by more than 150%. Whilst some of these improvements come
from the increase in resolution, a significant proportion comes from the change in both the dynamical core and the physics (contrast column 1 and 2 in Table 2), with the most notable contribution
from the physics upgrades being from the increase in the deep convective entrainment rate.
Finally, Table 2 shows that the upgrade leads not only to significant improvements in tropical cyclone
intensity, but also to similar improvements in the forecast tracks of the cyclones. A breakdown of
track error by forecast ranges (not shown) shows only a small percentage decrease in track error in
the first 24 hours, increasing to a 7–10% reduction on days 2–4, with the track error at day 6 being
12 A trial is a test period, usually a month or two in duration, where we produce analyses and forecasts from an experimental
version of the model and from the current operational model. These forecasts sample the same weather regimes and allow
a direct comparison of the predictive skill of the experimental and operational models (e.g. see Fig. 12).
© Crown Copyright 2014
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ENDGame/Physics Impact
at N512 resolution
Ave. central press. err. reduction
3.0 hPa
Central pressure reduction
7.1 hPa
Windspeed error reduction
6.7 kt
Max. windspeed increase
8.9 kt
850 hPa vorticity increase
79%
Track error reduction
7.3%
Track skill score increase
3.8%
ENDGame/Physics/Resol. Impact
N768 vs N512
3.6 hPa
11.1 hPa
9.0 kt
13.4 kt
155%
8.6%
4.5%
Table 2: Collated tropical cyclone verification statistics across several months of trials. The first column shows
the impact of ENDGame and revised physics changes compared to New Dynamics at N512 resolution. The
second column is the additional impact from increasing resolution to N768 in ENDGame forecasts. Windspeed
is shown here in knots ( kt).
reduced by over 15%. The average reduction of track error across all forecast ranges is as large as
8.6%, which is the biggest single improvement in tropical cyclone track errors in over 20 years.
These improvements in tropical cyclones are also found in our longer timescale climate predictions.
Analysis of the seasonal hindcasts using tropical cyclone tracking reveals the upgraded system to
have more storms in the Atlantic and more re-curving storms in both the Atlantic and Pacific basins
(both beneficial). However, the number of storms in the hindcasts are excessive in the West Pacific
(Fig 8).
Figure 8: Track density for tropical cyclones in the current GloSea (New Dynamics) and ENDGame hindcasts
(left), and in ERA-Interim and observed (right).
5.2
Improved representation of tropical equatorial waves and the Madden–
Julian Oscillation
In addition to tropical cyclones, an important component of “tropical weather” are the tropical equatorial waves such as the Kelvin wave and Mixed Rossby Gravity wave (Matsuno13 ). These wave
motions are an integral part of the tropical atmosphere, are important for organising convective
rainfall in the tropics on timescales of days to weeks and play a role in transporting energy from
13 Taroh
Matsuno. “Quasi-geostrophic motions in the equatorial area”. In: J. Meteor. Soc. Japan 44 (1966), pp. 25–43.
© Crown Copyright 2014
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one part of the tropics to another (Kiladis et al.14 ). The observed wave number-frequency spectrum
(a)
(b)
(c)
Figure 9: Wave number-frequency power spectrum at 15N-15S (following Kiladis et al.15 ) for (a) GPCP observed precipitation (Adler et al.16 ), (b) New Dynamics precipitation (c) ENDGame and revised physics precipitation. The theoretical dispersion curves for these equatorial models are also shown as lines.
for tropical precipitation,based on the GPCP climatology (Adler et al.17 ), shows significant spectral
peaks associated with the eastward travelling Kelvin waves and with the westward travelling Equatorial Rossby waves (ER), with a smaller spectral signature from the Inertia Gravity waves (Fig. 9(a)).
The New Dynamics simulations have very weak amplitudes for all of the equatorial waves, whereas
simulations using the ENDGame dynamical core + upgraded physics show a significant increase in
Kelvin wave activity and a more modest increase in Equatorial Rossby waves.
The other prominent feature in the observed GPCP wavenumber-frequency power spectrum is the
large amplitude signal at low wavenumbers (1-4) and lower frequencies (30-90 days). This is the
Madden–Julian Oscillation (MJO — Madden and Julian18 ) which is a key mode of intra-seasonal
precipitation and circulation variability in the tropics. This typically has a suppressed and enhanced
tropical precipitation dipole signal which propagates from the western Indian Ocean to Central Pacific with a timescale of 30-90 days. Again New Dynamics has a weak signal and the ENDGame and
upgraded physics simulation has a stronger amplitude for the MJO but is still too weak compared
to observations and spans too small a wavenumber range. Independent tests of ENDGame and
the physics upgrades show the MJO improvements come mainly from the physics, particularly the
increase to the entrainment parameter in the convection scheme (Klingaman and Woolnough19 ),
whilst for the Kelvin waves ENDGame has the biggest impact.
14 G
N Kiladis et al. “Convectively coupled equatorial waves”. In: Rev. Geophys (2009).
F Adler et al. “The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis
(1979–Present)”. In: http://dx.doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2 (2007), pp. 1–21.
18 Roland A Madden and Paul R Julian. “Detection of a 40–50 Day Oscillation in the Zonal Wind in the Tropical Pacific”. In:
http://dx.doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2 (1971).
19 N. Klingaman and S. Woolnough. “Using a case-study approach to improve the Madden–Julian oscillation in the Hadley
Centre model”. In: Q. J. R. Metereol. Soc. (in press).
17 Robert
© Crown Copyright 2014
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5.3
Improvements to the model representation of gravity waves
The use of reduced damping in ENDGame has a marked effect on the model’s ability to explicitly represent internal gravity-wave motion. ENDGame global forecasts contain resolved gravity
waves with much larger amplitude than those in equivalent New Dynamics simulations. Comparable wave amplitudes can be achieved with New Dynamics, but numerical stability issues mean
that this requires very small time steps (Shutts and Vosper20 ), which are not practical in NWP or
climate simulations. Tests have shown that even at relatively coarse NWP resolutions (e.g. N320)
ENDGame global forecasts explicitly represent gravity wave motion in the stratosphere and their
presence clearly adds to the realism of the model and contributes to its improved variability. Orographic gravity waves (mountain waves) are seen above the major mountain ranges in the model.
At N768 resolution ENDGame is able to explicitly represent the large amplitude mountain waves
which break and produce clear air turbulence (CAT) above some of the broader mountain ranges.
Forecast tests for known cases of aircraft encounters with mountain-wave CAT over Greenland
have shown that the model’s representation of the waves near the tropopause is now sufficiently
good that the location and timing of CAT can be accurately diagnosed from the model flow fields
directly, suggesting that aviation CAT forecasts could be substantially improved in the near future.
An example of such a forecast is shown in Fig. 10.
Finally, it is also worth noting that in high-resolution (1 21 km) limited area configurations of the model
(UKV), the gravity-wave improvements are manifested in the existence of trapped lee waves. Tests
suggest that long trains of lee waves, with horizontal wavelengths as short as 15 km, are well
represented in ENDGame versions of UKV, which are completely absent in the equivalent New
Dynamics simulations (see Fig. 11). The downdraughts and turbulent gusts associated with lee
waves represent a hazard to light aircraft, and can also generate dangerous gusts downwind of
hills, causing tree and property damage and over-turning of high-sided vehicles. The improvements
to this aspect of the model will allow better forecasts of hazardous conditions to the public and to
aviation communities.
5.4
Improved forecasts of aviation “jet level” winds
The accurate prediction of the upper atmospheric “jet stream” winds is particularly important for the
aviation industry, which relies on accurate forecasts of the position and strength of the jet streams
for planning intercontinental flights. A consistent improvement seen in all tests of ENDGame is a
reduction of the slow bias in tropospheric windspeeds, which is a long-standing feature of the global
model. Figure 12 shows verification for 6 day forecasts of northern hemisphere and tropical wind
profiles against radiosonde observations from a Nov/Dec 2012 trial. The mean error at this forecast
20 G. J. Shutts and S. B. Vosper. “Stratospheric gravity waves revealed in NWP model forecasts”. In: Quart. J. Roy.
Meteorol. Soc. 137 (2011), pp. 303–317.
© Crown Copyright 2014
19
Figure 10: ENDGame global forecast of mountain wave breaking over Greenland. The flow is easterly (right
to left) across Greenland and the vertically propagating wave motion is evident in undulations of the potential temperature surface (solid contours, units K). The red shading denotes regions of high turbulent kinetic
energy associated with wave breaking. The case shown (25 th May 2010) was studied by Sharman, Doyle,
and Shapiro21 and is a well documented case of an aircraft encounter with mountain wave turbulence along a
transatlantic route across Greenland. The global ENDGame forecast provides a good prediction of the location
and timing of the turbulence.
(a)
(b)
(c)
Figure 11: Lee waves over the UK. Panel (a) shows visible satellite imagery. The lee wave motion is evident in
the banded cloud patterns, particularly across the hills and mountains of Wales and northern England. Panel
(b) shows the UKV forecast using New Dynamics. Very little wave motion is present in the simulation. In
contrast, panel (c) shows the equivalent ENDGame UKV forecast. The model produces realistic lee wave
cloud patterns which correspond well with the satellite image.
© Crown Copyright 2014
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range is improved by about 50%. As with some of the cyclone results above, this bias is much less
sensitive to the change in resolution than to the change in dynamical core. Resolution does improve
the root mean square (RMS) error in northern hemisphere by around 1% at jet levels (250hPa).
In the tropics we see that the ENDGame and upgraded physics make the RMS errors worse at
this level and the enhanced resolution degrades this further. In this region, we have seen that
ENDGame and the upgraded physics and resolution leads the model to have too much variability
compared to the model’s analysis. Work is ongoing to improve this through future upgrades to the
Northern Hemisphere
(CBS area 90N-20N)
model’s
convection
scheme.
Equalized and Meaned from 7/11/2012 00Z to 10/12/2012 12Z
N512 ENDGame+physics
N768 ENDGame+physics
-200
0.0
0
200
Pressure (hPa)
Pressure (hPa)
600
600.0
400.0
400
600
600.0
800
400
600
600.0
1000
-0.6
1000
1000.0
1000.0
1200
1200
1.0
600
600.0
800.0
1000
1000.0
1200
400
800
800.0
1000
400.0
800
800.0
1000.0
200
400.0
800
800.0
-0.5
0.0
0.5
FC-Obs Mean Speed Error
200.0
200
400
0
200.0
200
-1.0
0.0
0
200.0
400.0
-200
0.0
0
200.0
Pressure (hPa)
-200
-200
0.0
Pressure (hPa)
N512 New Dynamics
Tropics (CBS area 20N-20S)
Equalized and Meaned from 7/11/2012 00Z to 10/12/2012 12Z
-0.4
-0.2
0.0
0.2
0.4
FC-Obs Mean Speed Error
Difference from "GA3.1"
0.6
-4
-3
-2
-1
0
FC-Obs Mean Speed Error
1200
1
-0.6
0.0
200.0
200.0
0.0
0.0
200.0
200.0
400
600
600.0
Pressure (hPa)
Pressure (hPa)
Pressure (hPa)
Pressure (hPa)
400.0
400.0
600.0
800.0
1000.0
1000.0
400
600
600.0
800
800.0
800.0
1000.0
1000.0
1000
1000
1200
1200
4
6
8
10
12
14
16
FC-Obs RMS Vector Error
18
-0.8
-0.6
-0.4
-0.2
0.0
0.2
FC-Obs RMS Vector Error
Difference from "GA3.1"
1.0
400.0
800
800.0
0.2
0.4
0.6
0.8
FC-Obs RMS Vector Error
Difference from "GA3.1"
200
200
600.0
0.8
0
0
400.0
-0.2 0.0
0.2
0.4
0.6
FC-Obs Mean Speed Error
Difference from "GA3.1"
-200
-200
0.0
-0.4
0.4
2
4
6
8
10
FC-Obs RMS Vector Error
12
-0.2
0.0
Figure 12: Verification of day 6 northern hemisphere (left hand 4 panels) and tropical (right hand 4 panels)
68% error bars calculated using S/(n-1)1/2
68% error bars calculated using S/(n-1)1/2
wind profiles vs radiosonde observations from a Nov/Dec 2012 trial. Three experiments are shown: the N512
New Dynamics control (red line), the N512 ENDGame and upgraded physics (blue line), and the enhanced
resolution N768 ENDGame and upgraded physics (green line). In each figure, the top-left panel shows the
bias, the top-right panel shows the difference in bias from the New Dynamics control (i.e. the red line). The
bottom-left panel shows the RMS error and the bottom-right shows the difference in this from the control.
A significant advantage of the reduced windspeed bias and improved jet locations is that it has
allowed us to remove the operational scaling of pressure level winds. Previously, winds between
700 and 70 hPa were scaled by factors of up to 4% (depending on the pressure level). This was
designed to improve flight time forecasts for aviation customers, but is also known to increase the
RMS error in these winds. Figure 13 shows predicted flight time errors as a function of the scaling
factor applied for forecast ranges between 24 and 30 hours. This and similar results from other
forecast ranges show that even with New Dynamics, there would have been a small improvement in
both flight time and RMS errors by reducing the maximum scaling to 2%. The N768 ENDGame and
upgraded physics configuration, however, shows even less need for wind scaling to be applied than
© Crown Copyright 2014
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Figure 13: Flight time errors from both N512 New Dynamics (Control) and N768 ENDGame (Trial) as a
function of the amount of wind scaling applied for forecast ranges between 24 and 30 hours.
the New Dynamics control and at some forecast ranges even a 1% scaling is seen to be detrimental.
Moreover, the N768 ENDGame and upgraded physics forecasts with no scaling applied have lower
flight time errors than the N512 New Dynamics forecasts with any value of wind scaling. The
removal of this scaling, therefore, both improves the quality of the forecast products and makes
them consistent with the raw forecast model data.
© Crown Copyright 2014
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6
Conclusions and future direction
The evidence collected in this report demonstrates that for global models, the adoption of the
ENDGame dynamical core provides a significant improvement to our current prediction capability.
It improves the variability of the model, particularly in the mid-latitudes, which leads to the improved
intensity and tracks of individual storm systems and other synoptic features and the reduction of
slow speed biases in tropospheric winds. In the tropics we see improvements to several modes of
variability, particularly the representation of tropical cyclones. This improved variability also leads
to the model supporting a wider envelope of potential solutions, which increases the spread of our
ensemble prediction systems with potential improvement to our ability to provide risk based predictions of extreme weather. ENDGame’s improved stability and scalability allows us to upgrade the
deterministic global model resolution, reduces the overheads in maintaining operational and production systems and will allow us to continue to improve the resolution of the global model as our
computers are upgraded over the course of the next decade.
In the coming year, we will implement ENDGame in the production global modelling systems still
using New Dynamics, namely our monthly-seasonal and decadal prediction systems. We will also
implement ENDGame in our limited-area weather and climate prediction models, where we expect
to see improvements to the representation of more local features such as lee wave clouds that
form downstream of hills and mountains in stably stratified conditions. After this, we can retire New
Dynamics from the model’s code base and revert to supporting a single dynamical core for use in
seamless prediction across all resolutions and timescales.
In a sense ENDGame finishes the work that New Dynamics started, but the development of our
dynamical cores doesn’t end here. Research has already started on the next-generation dynamical
core (named GungHo22 ) which we expect to replace ENDGame in about 10 years. Being developed
in collaboration between Met Office scientists, academics from across the UK and computational
scientists from the Hartree Centre, GungHo will be part of a completely new model that will deliver
the step change in scalability required to continue to exploit future generations of computers. This
will ensure that, together with our collaborators, we can continue to improve our weather and climate
services into the next decade and beyond.
22 GungHo is an anglicised pronunciation of ”gong he”, where the Chinese characters ”gong” and ”he” are translatable
individually as ”work” and ”together”, so this name reflects both the ambitious and the collaborative nature of the project.
© Crown Copyright 2014
23
Appendix A
List of improvements to the model’s physics
The lists below highlights the most significant improvements in the global NWP model’s physical
parametrizations that have been implemented alongside the upgrade to the ENDGame dynamical
core. Improvements marked
†
are physics options or schemes already being used in climate re-
search configurations of the model, such that applying these changes in NWP brings us closer to
using identical parametrizations across all global modelling systems.
Radiation
• Improved absorption of longwave radiation by CO2 and O3 which improves heating and cooling
in the stratosphere† .
• An increase in the frequency of calls to the radiation scheme (reducing the radiation time step
to 1 hour) which improves the accuracy of the calculations.
• In NWP and seasonal forecast systems, an improved use of climatological aerosol fields for
cloud condensation nuclei.
• An adjustment to the fraction of shortwave radiation reflected (albedo) over sea ice.
• Solar constant adjusted to the latest estimated value of 1361 Wm−2 .
Boundary layer scheme
• Reduced turbulent mixing for stably stratified boundary layers. This has a better physical basis
than previous mixing formulations† .
• Revised stability functions for unstable boundary layers, which again have a better physical
basis† .
• Revised diagnosis of shear-dominated boundary layers, which improves the representation of
cloud in polar cold-air outbreak situations.
Large-scale cloud scheme
• An improved method to represent cloud erosion and a better definition for mixed-phase cloud.
• Improvements to the representation of ice cloud through a new cirrus term and use of the
model winds in the shear term in the treatment of falling ice cloud fraction.
• A smoother phase change for cloud condensate detrained from convective plumes.
© Crown Copyright 2014
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Large-scale precipitation scheme
• Implementation of improved size distribution of water droplets for drizzle to better match observations.
• Improved microphysics substepping to perform multiple calculations over each column in a
single model time step.
• In NWP and seasonal forecast systems, an improvement to the use of climatological aerosol
fields in the microphysics schemes consistent with that made in the radiation scheme.
Convection scheme
• An increase of the entrainment rate for deep convection with related modifications to the detrainment rate.
• Smoothed adaptive detrainment of cloud liquid water, ice and tracers.
• An update to the code base with improvements to the robustness of the scheme.
Gravity wave and flow-blocking drag
• Introduction of a new orographic drag scheme, which has a better physical basis.
Land surface
• The numerical schemes used in the JULES land-surface model have been improved, to provide better accuracy.
• Use of the ”TOPMODEL” hydrology to better diagnose surface and subsurface runoff† .
• Changes to the surface thermal and momentum roughness lengths which improve the surface
energy budgets and drag.
• Improvements in the way the surface emissivity is calculated for different surface types, including non-unity values for the sea surface and sea ice.
© Crown Copyright 2014
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Paper authored by:
David Walters
Nigel Wood
Simon Vosper
Sean Milton
with contributions from:
Clare Bysouth
Paul Earnshaw
Julian Heming
Marion Mittermaier
Claudio Sanchez
Malcolm Roberts
Warren Tennant
© Crown Copyright 2014
26
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© Crown Copyright 2014
27
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