Met Office Academic Partnership Summer Internship Reports:
1. The effects of volcanic eruptions on the dynamics of the stratosphere
2. Reconstructing AMOC variability using wind and buoyancy forcing anomalies
3. Mechanisms of Atlantic Decadal Climate Fluctuations and Assessment of
Forecast Skill
4. Decadal-scale Climate Variability on the Central Iranian Plateau Spanning the
So-called 4.2 ka BP Drought Event
5. Controls on atmospheric blocking under climate change
6. Improving Probabilistic Weather and Climate Predictions
7. Improving satellite constraints on volcanic ash cloud heights
8. Convectively-driven baroclinic flows in the laboratory as a model for baroclinic
adjustment
1. The effects of volcanic eruptions on the dynamics of the
stratosphere
Student: Matt Patterson
Supervisor: Lesley Gray (AOPP)
Matt’s project involved investigating the effects of volcanic eruptions on the dynamics of the
stratosphere. Following a sufficiently large eruption near the equator, aerosol particles are
injected into stratosphere and form aerosol clouds. These clouds absorb radiation from the
Sun and the Earth, heating the lower stratosphere. This temperature anomaly indirectly
affects the propagation of waves in the atmosphere and Matt used diagnostics from the ERA
Interim, NASA Merra, JRA55 and NCEP-CFSR datasets like EP flux and residual circulation
to study this. Previous work by Graf had suggested that wave propagation from the
troposphere into the stratosphere actually increased during winters following eruptions. This
was surprising, but his analysis found that these more up-to-date datasets all show the same
result.
Figure 1. Difference between years with and without a volcanic eruption in EP flux
Figure 1 above shows the difference between years with and without a volcanic eruption in
EP flux, (F). F is displayed as vectors with div F in the background. Red areas of the plot
indicate a source of wave activity. The plot shows that following volcanic eruptions, the
amount of waves produced during winter in the Northern hemisphere troposphere is
increased. However, the increased wave forcing doesn’t seem to strongly affect the
stratospheric polar vortex. The main volcanic signal is a strengthened vortex due to the
enhanced temperature gradient between the equator and poles.
Matt found the placement was a really helpful insight into how climate physicists work and
gave him a taste of and for research. He enjoyed doing genuinely original work and talking to
scientists who are passionate about their subject!
As his supervisor, Lesley had a great experience working on this project with Matt, stating:
“Over the summer It has been a pleasure to have Matthew working with us this summer. It
has been a really useful opportunity to look at an aspect of volcanic forcing that I’d always
meant to have a look at but never quite got around to. Matthew very quickly came up to
speed and has found some really interesting results using more up-to-date reanalyses. It
confirms a surprising result from a few years back, and has helped me to decide that it’s
worth looking further, to see if models reproduce the results. So it’s been really helpful to my
research, and is a great chance to help encourage potential new DPhil students – so a double
plus!”
2. Reconstructing AMOC variability using wind and buoyancy
forcing anomalies
Student: So Takao
Supervisors: Helen Johnson (Earth Sciences) and David Marshall (AOPP)
So Takao worked with Helen Johnson and David Marshall in the Physical Oceanography
group, together with Helen Pillar at the University of Copenhagen, to reconstruct variability in
the Atlantic meridional overturning circulation (AMOC).
The AMOC is a large scale ocean circulation in the Atlantic that carries warm, saline water
northward and brings cold, dense water southward at depth. The phenomenon is closely
related to the heat transport of the Atlantic ocean and is therefore believed to play an
important role in the climate of Western Europe. The AMOC at 26°N has been continuously
monitored since 2004 by oceanographers from the National Oceanographic Centre in
Southampton and their US colleagues and this has resulted in an unprecedented 10 year
time series of the mean AMOC strength at 26°N, which has revealed large variability on
different timescales. Knowledge of this variability could be useful in reducing uncertainties in
future climate predictions.
So’s summer project focused on reconstructing the time series of AMOC variability from a
range of observed forcing datasets, by projecting surface wind and buoyancy forcing
anomalies onto linear sensitivity patterns, which were calculated using the adjoint of the
MITgcm ocean circulation model. Our reconstruction using NCEP reanalysis II (1979-2015)
forcing successfully reproduces the AMOC variability on short time scales, which can be
attributed to wind forcing, but diverges from the observed AMOC on longer time scales when
the response to surface heat fluxes becomes more important. We believe that this
divergence is caused by limitations of our linear sensitivity approach, which prevents us from
capturing the effects of forcing anomalies on time scales longer than 15 years, and on
differences between the decadal variability of the model and the real ocean.
We have used this reconstruction to “predict” the AMOC for the 15 month period (April 2014
to June 2015) for which observational data at 26N are not yet available. When the RAPID
team of oceanographers recover their moorings this autumn, we expect that they will find a
mean AMOC over this period roughly equal to that over the past few years (a change of
within 0.3 Sv from the 2009-2014 mean which was 15.6 Sv), and with no significant
weakening events over the past winter. This prediction forms the basis of two blog articles
(on the RAPID and OSNAP project websites).
By using more historic surface forcing data such as that available via the NCEP reanalysis I
(1948-2015) and 20th century reanalysis (1851-2011) products, we were also able to obtain
longer time series of AMOC variability, which opens up the possibility of understanding past
behaviour of the AMOC. We intend to write a paper comparing the AMOC variability that
results from different reanalysis products; investigating how much the AMOC anomalies they
generate deviate from one another will help to understand the accuracy of these products. In
the meantime, So has started a Masters degree in Applied Maths at Imperial College.
3. Mechanisms of Atlantic Decadal Climate Fluctuations and
Assessment of Forecast Skill
Student: Ben Huddart
Supervisors: Aneesh Subramanian (AOPP), Laure Zanna (AOPP), Tim Palmer (AOPP)
The sea surface temperatures (SSTs) and climate in the Atlantic sector are well known to
vary on seasonal to decadal timescales with a large impact on rainfall and temperature over
Europe, Eastern US and Africa. However the mechanisms controlling these long-term climate
variations are still unknown. Understanding the mechanisms associated with these long-term
oceanic and atmospheric fluctuations in the Atlantic sector can potentially lead to more
accurate assessments of climate predictions and associated uncertainties. Seasonal and
decadal predictability of Atlantic climate and SSTs is of particular interest to the U. K. Met
Office and Europe in general, due to its downstream influence over the U.K. weather and its
direct influence on climate in Europe. This leads to the primary focus of our project, which is
to identify the mechanisms leading to predictive skill of seasonal and decadal modes of the
Atlantic Ocean system.
There is clearly a gap in our understanding of drivers controlling Atlantic seasonal and
decadal climate variability and its predictability, what timescales and regional variability
contributes most to the predictability. We focused on using sophisticated statistical, yet
dynamically based, models (Linear Inverse Models, LIM) to identify statistical relations among
variables, diagnose physical processes, and isolate potentially predictable components of the
flows.
The LIM is a simplified reduced stochastic-dynamic climate model (Majda et al., 2009). The
governing dynamics of the system in LIM is modeled as:
dx = (Lx + ξ) dt (1)
where x represents an appropriate system state vector, L is a linear dynamical operator
matrix, and ξ is a vector of stochastic Gaussian noise. For such a system, L can be estimated
from observational estimates of covariance. Eq. 1 can then be solved analytically for different
lead times τ: x(t + τ) = G(τ)x(t) + ε where G(τ) = exp(Lτ) represents the decaying, predictable
signals at forecast lead time τ and ε is the nonlinear stochastic unpredictable component. The
dynamical operator, L, contains summarizing information about the damped and oscillatory
behaviors of the interacting modes in x, while the scaling covariance matrix ξ summarizes
unresolved high-frequency dynamics, parameterized as multivariate normal noise. We
analysed the LIM interaction matrices in novel ways for seasonal and decadal mode
diagnostics to study the patterns and regions that control the coherent behaviours in the
decadal modes.
We use LIM to build a reduced climate state statistical model using observed Atlantic SST
anomalies between latitudes 20°S and 66°N from 1870 to present. Additionally, subjective
data pre-filtering are performed in order to isolate climate signals of interest. We refer to this
model as the filtered LIM. The filtered LIM analysis involves attempting to identify time-scale
interactions among decadal modes and between decadal and interannual to intraannual
modes. This is achieved through spectral analysis and analysis of LIM modal interactions.
The skill and mechanisms are then evaluated against the state-of-the-art ECMWF seasonal
forecasting system for seasonal SST anomalies and Met Office forecast system for decadal
SST anomalies (SSTa) to understand the seasonal and decadal skill in the Atlantic Sector.
On the seasonal timescale, the LIM forecasts show skill for lead times of up to 4 months in
spring time (Apr-Jun) and have the least skill in winter (Jan-Apr) and summer (Jul-Sept) of
only about 2-3 months before the forecast root mean square error exceeds climatological
forecast error (Fig. 1).
Figure 1. RMS error relative to climatological forecasts for two year forecasts initialised at
different months. The dotted line indicates the average over all months. A relative error of 1
indicates that the forecast error is of similar magnitude as assuming the climatological SSTa
as forecast. Inset: Initial growth rate of absolute RMSE of forecast SSTa.
We also evaluate the forecast skill using an anomaly correlation metric in time at every grid
point. We then show that the correlation deteriorates most rapidly in the sub-polar gyre
region, with very little correlation between the forecasted and the observed SST anomalies
beyond 3-4 months in this region. This forecast skill, measured as anomaly correlation, is
highest in the Tropics, with reasonable skill (positive correlation) up to 12 months lead time.
The error growth rate in LIM forecasts are similar to that seen in ECMWF seasonal forecasts,
with the RMSE increasing by about 0.3oC over the first six months.
On the decadal timescales, we use the filtered LIM to explore the predictability of the decadal
component of the SSTa. Forecast skill is lost in time periods of 3-4 years over the entire North
Atlantic basin. The spatial maps of temporal anomaly correlations show that the sub-polar
gyre has the highest predictability compared to other regions in the N. Atlantic on decadal
timescales (Fig. 2). Using the filtered LIM with only decadal components of the signal as
opposed to using higher frequency signals (inter-annual and intra-annual) tends to reduce the
skill over all regions in the basin (Fig. 3).
Figure 2 Temporal correlation over the North Atlantic region for a range of lead times using
the filtered LIM with only the decadal component of SSTa.
Figure 3 Temporal correlation over the North Atlantic region for a range of lead times using
the filtered LIM with both the decadal component and the higher frequency (inter-annual and
intra- annual) components of SSTa.
Hence, the higher frequency SSTa signal tends to help improve forecast skill on decadal
timescales over all regions in the basin, and most in the tropical and sub- tropical regions of
the basin. Coupling was mainly observed between interannual and decadal modes of
variability with very little or no coupling between the intraannual modes and decadal modes
for the decadal forecast experiments.
We show from this study that there are modes of variability in the decadal and interannual
frequencies both in the tropics and extra-tropics, which influence the predictability of SST
anomalies on seasonal to decadal timescales. Further analysis will be required to understand
the physical mechanisms by which these modes couple and how they influence the
predictability on these timescales. This and other aspects of the system which influence
predictability over the North Atlantic region will be explored in future studies to help improve
weather and climate prediction in this region.
Reference:
Huddart, B., Subramanian, A., Zanna, L. & Palmer, T. N., 2015: Seasonal and decadal
forecasts of Atlantic SST using a Linear Inverse Model. J. Clim., In Prep
4. Decadal-scale Climate Variability on the Central Iranian
Plateau Spanning the So-called 4.2 ka BP Drought Event
Student: Luke Maxfield
Supervisor: Stacy Carolin (Dept. Earth Sciences)
Luke’s summer placement was with the Isotopes and Climate research group at Oxford's
Department of Earth Sciences, supervised by Dr Stacy Carolin, a palaeoclimatologist in the
department.
His project was a palaeoclimate study of Western Asia, and involved geochemical analysis of
a speleothem. Cave speleothems can provide extremely good records of past climates, by
encoding climatic information in their isotopic composition as they grow. The speleothem he
was working on, GZ14-1, was sampled from Gol-e zard cave in northern Iran. Northern Iran is
an interesting area to investigate past climate change as the regional atmospheric circulation
is thought to be sensitive to a number of Earth's climate systems, such as the Indian Summer
Monsoon (ISM) and the North Atlantic Oscillation (NAO), though these relations are currently
not well understood.
He first used a MicroMill to drill out and store over 400 calcite powder samples at 250 or 500
μm intervals along the growth axis of the stalagmite. The age model of this stalagmite shows
that this sampling interval produces samples at ~5-yr resolution. From these samples, he
extracted 30-60 μg calcite powder, which he placed into glass autosampler vials in
preparation for the analysis of stable oxygen and carbon isotopes on the Delta V isotope ratio
mass spectrometer (IRMS). He also extracted an additional 70-90 μg of calcite powder from
the same samples in preparation for trace element analysis on the Element inductively
coupled plasma mass spectrometer (ICP-MS). Overall, he assisted in preparing and running
over 100 samples on both instruments.
Over the eight weeks, I discussed relevant scientific literature with my supervisor to provide
some context for the work I was doing. This introduced me to an interesting application of
palaeoclimate studies: understanding how changes in climate have influenced the
development of human civilisations. A particularly interesting topic of debate is a 4.2ka BP
‘drought event', which has been linked to the collapse of the Akkadian Empire (Staubwasser
& Weiss, 2006). My preliminary stable oxygen isotope results reveal a large 40-yr excursion
in the record at 4440 BP and a smaller 30-yr excursion at 4250 BP, based on the tentative
age model (Figure 1).
Figure 1 Iran stalagmite GZ14-1 stable isotope record, inferred as a rainfall proxy with more
positive (down) indicating drier conditions. Yellow bars highlight stable isotope excursions.
Currently, it is difficult to interpret the results based on the limited number of measurements
that have been analysed on the instruments, however the samples that I drilled and prepared
are set to reveal exciting new climate data from this region that is absent in the literature todate.
What he found most valuable about the placement was learning about the scientific process,
from deciding a research question to processing data and writing a paper. Luke found it a
great experience and is now looking forward to seeing future results. The completed dataset
will be presented at the 2015 American Geophysical Union (AGU) Fall Meeting in San
Francisco this December.
5. Controls on atmospheric blocking under climate change
Student: Daniel Kennedy.
Supervisors: Tim Woollings (AOPP) and Tess Parker (AOPP)
Blocking is a high impact weather pattern in which the usual westerly flow in midlatitudes is
blocked by a persistent, quasi-stationary anticyclone, leading to severe cold in winter and
heatwaves in summer. These complex dynamical events have long been a challenge for
weather and climate prediction models, and so confidence in projected blocking changes
remains low.
In this project we diagnosed blocking events in a set of HadGAM1 model experiments
designed to probe uncertainties in the storm track response to climate change. These
experiments perturbed the upper and lower level equator-to-pole temperature gradients
separately to assess the competing effect of these changes on the storm track. The main
result is that projected blocking changes are more sensitive to changes in the upper level
temperature gradient, and hence to uncertainties in the tropical warming rather than the level
of polar amplification.
Second order effects also emerged, for example the temperature anomalies experienced
blocking are projected to change. The strongest example of this is in winter, when the extent
of the cold anomaly is dependent on the amount of Arctic warming. If the Arctic warms
strongly, the area of Eurasia affected by extreme cold temperatures during blocking is greatly
reduced.
6. Improving Probabilistic Weather and Climate Predictions
Student: Michael Varley
Supervisor: Peter Watson (AOPP)
The focus of Michael’s project was weather forecasting, and throughout the summer he was
working with a numerical 'toy model' of the atmosphere called the Lorenz '96 system. This
system has been used to test other proposals for improving atmospheric models in the past,
before they were subsequently implemented in Numerical Weather Prediction Models
(NWPs).
One of the major problems in forecasting is effectively representing the uncertainty in weather
forecasts. The weather is a chaotic system, which means it cannot be solved for analytically,
and even the slightest error in the forecast will grow over time until the behaviour of the
atmosphere looks totally different to the behaviour of the forecast. This makes it very difficult,
if not impossible, to forecast further than about a week in advance. Forecasting is an
inherently probabilistic exercise: it is not possible to make binary statements such as 'it
will/won't rain tomorrow' with any scientific justification. However, it is possible to make
probabilistic statements, such as 'there is a 95% chance it will rain tomorrow'. Effective
representation of uncertainty in a forecast is of paramount importance if we wish the statistical
claim we are making to be reliable.
The standard method of representing forecast uncertainty is to run an ensemble forecast,
where each ensemble member is subtly perturbed from the others in some way. Generally,
the members will have different initial conditions but it is also important to include
perturbations to represent error in the forecast model. Two ways of doing this are to have
slightly different sets of parameters for each ensemble member, or alternatively by adding
some stochastic noise. The novel approach of this project was to combine these approaches
by adding noise to the parameters themselves. This yielded some pleasing results: the
forecasts generated showed improved skill scores compared to using either approach alone.
At the beginning of the project, he began by establishing the computational equivalent of an
atmospheric data set, which was generated from the differential equations which govern the
Lorenz '96 system. The next stage was to analyse the relationship between the large scale
(X) variables, which we regard as resolvable, and tendencies in these due to sub-grid scale
(Y) variables, which are treated as if they cannot be resolved (this is reflective of our lack of
knowledge of what is occurring on small scales in the atmosphere). The relationship between
these variables was modelled as a cubic function. In our ensemble forecasts, the coefficients
of this function are the parameters that were subjected to stochastic perturbation.
In the final week of my internship, Michael presented his findings in a half hour talk to the rest
of the research group, which was followed by a brief Q&A session. Hi stime in the department
was clearly a very enjoyable one, “I very much enjoyed my internship in AOPP, and it was a
real privilege to be able to work alongside such talented and enthusiastic colleagues. My
computer programming skills improved significantly, and I learned a massive amount about
the modelling of non-linear systems, a subject which I was not very familiar with before my
internship.”
This project will be continued by the team in AOPP, with a view to including the results found
this summer in a future publication.
7. Improving satellite constraints on volcanic ash cloud
heights
Student: Isabelle Taylor
Supervisor: Tamsin Mather (Dept. Earth Sciences)
During the April and May 2010 eruption of Eyjafjallajökull, European aviation was grounded
for five days causing large economic and societal impacts with estimated losses of US$5bn to
the global economy. The London VAAC, hosted and run by the Met Office, is responsible for
issuing advisories for volcanic eruptions originating in Iceland and the north-eastern corner of
the North Atlantic. It uses a combination of volcano data; satellite-based, ground-based and
aircraft observations; weather forecast models and dispersion models in order to fulfil this
remit. One of the key model inputs is ash cloud altitude. The aim of this MOAP project was to
improve the values obtained for ash cloud height from satellite IASI data. This was done by
attempting to apply a method known as CO2 slicing, which is frequently used to obtain
meteorological cloud top heights, to acquire values for ash altitude. By the end of the
placement, IDL code had been written which reads in the IASI data from the Eyjafjallajökull
eruption and a simulated clear IASI spectrum. It then computes five ratios for each pixel using
the same channel combinations as the algorithm applying CO2 slicing to MODIS data. Code
has also been written which solves a radiative transfer equation for each channel combination
for 100 layers in the atmosphere, made specific to each pixel in the image by considering the
viewing angle of the sensor and NWF data. This value is then compared to the corresponding
ratio calculated from the image data, and the altitude where there is minimum deviation
between the two is taken as the ash cloud top height. Isabelle will be returning to Oxford in
October as part of the NERC DTP cohort and plans to test this code and run it completely for
multiple images from the Eyjafjallajökull eruption. This will be followed by validating the
results against existing methods of obtaining ash cloud height from IASI imagery and lidar
data. She also hopes to select new and a larger number of channel combinations which will
exploit the greater number of channels available within the CO 2 window in the IASI spectrum.
Her overall aims during her D.Phil. are to improve the values obtained for ash cloud height
which are important for ash hazard avoidance and forecasting and the impacts of volcanic
activity on cloudiness and cloud properties.
8. Convectively-driven baroclinic flows in the laboratory as a
model for baroclinic adjustment
Student: Sylvie Su
Supervisor: Peter Read (AOPP)
Sylvie Su joined AOPP from École Normale Supèrieure in Lyon as a student intern to work on
a preliminary modeling study of baroclinic zonal flows that develop between convective
“tropical” and “polar” zones in a cylindrical tank produced by localised bottom heating and top
surface cooling. Such a configuration is intended to emulate conditions in the Earth’s
atmosphere conducive to baroclinic adjustment, which is a process thought to play an
important (though poorly understood) role in determining the thermal structure of the
midlatitude troposphere. Sylvie’s project entailed setting up and running fully time-dependent
numerical simulations of 2D axisymmetric overturning circulations to reveal some clear
scaling relationships governing the dependence of heat and zonal momentum transport on
the strength of differential heating and rotation. This work is now in preparation for a
publication. By concentrating on 2D axisymmetric flows in which baroclinic instability was
suppressed, this study served as an essential first step in preparation for some larger-scale
studies ongoing in Peter Read’s group (a) to carry out a series of experimental investigations
of baroclinic adjustment in the laboratory, and (b) to extend numerical model simulations of
these flows using an adaptation of the Met Office ENDGAME model.
A typical meridional (radius-height) section of a temperature field. Free convection takes
place above the hot plate and below the cold plate causing the temperature to become almost
constant at large and small radii.
A cross-section of the experiment and its analogue to a mid/high-latitude atmospheric
channel