Meteosat satellite image showing the moon's lunar shadow on 11 August 1999. Copyright © 2001 EUMETSAT

I’m no doubt showing my age here but I have very clear memories of standing out on the Department’s external testing area (a.k.a. the coffee balcony) and observing, with many others, the 97% partial solar eclipse over Reading on the 17th August 1999.

The eclipse path. From Espenak and Anderson (1997).

Eyes are good at adapting to diminished light so the partial eclipse we experienced in Reading didn’t seem so very dark, but there was a strange feeling in the air and, even as a scientist, I had a slight niggling worry that perhaps this time the sun wouldn’t re-appear from behind the moon. Fortunately it did though and it turned out that we were actually more fortunate in our eclipse viewing in Reading than were the many people who went down to Cornwall to experience the total eclipse there. Cornwall was cloudy due to an occluded front close to the west of Ireland whereas we only had partial cloud over southeast England. This post is not about how weather can interfere with our eclipse viewing though, instead it’s about how eclipses can interfere with our weather.

Measurements of insolation and 1m screen temperature at Reading (5 minute averages) .

We’ve known for a long time that an eclipse causes a reduction in solar insolation and an associated reduction in surface temperature. Winds also tend to slacken and reduce in variability during an eclipse due to the stabilization of the boundary layer and associated reduction in turbulent motions. Karen Aplin and Giles Harrison made some measurements during the 1999 eclipse at both Camborne in Cornwall and at our Atmospheric Observatory here in Reading. Here they observed a maximum reduction of temperature of about 2C occurring about 15 mins after the time of maximum eclipse and the expected pronounced drop in windspeed and reduction in variability. They also found some evidence of a more controversial impact of an eclipse on weather, wind direction changes at both Reading and Camborne at the onset and end of the eclipse.

Composite of wind and temperature measurements from the 28 May 1900 eclipse over the US obtained by combining the results for stations similarly situated in regard to the path of the eclipse. The dashed lines enclose radii of about 1500 and 2500 miles where 2500 miles is the edge of the penumbra. From Clayton (1901).

To understand why these wind direction changes are controversial we need to study some history. Back in 1901 H. Helm Clayton postulated the existence of a cold-cored eclipse cyclone based on his analysis of measurements of the US eclipse of 28 May 1900 made in several locations. This eclipse cyclone is a cold outflow of air from the umbra (the region totally obscured by an eclipse) leading to negative surface pressure anomaly with outer ring of positive pressure anomaly. This leads to an anticyclonic circulation associated with the cold core extending out to a distance of about 1500 miles. Whilst a review of Clayton’s work in Science at the time proclaimed ‘Clayton has gone far ahead of all previous investigations of the phenomena of eclipse meteorology…’, Frank Bigelow wrote a rather damming letter to the same journal refuting Clayton’s ideas. This controversy still exists today with Founda et al. (2007) declaring that the alleged ‘eclipse wind’ associated with the eclipse cyclone is no doubt rather an enhanced wind chill effect and Anderson (1999) writing ‘Subjective impressions in the highly emotional moments leading up to and through totality are also likely to encourage the persistence of this story….’. There’s nothing better than a century of debate to pique the interest of meteorologists so Giles and I decided to see if the latest high resolution Met Office weather forecast model could help us solve the mystery of the eclipse wind.

We’re very fortunate in the UK to have a high density of meteorological stations reporting hourly (synoptic) weather measurements. The difficulty in using these measurements to determine the effect of an eclipse is that, of course, weather is always evolving and we don’t have measurements from the exact same time ….. but without the eclipse. In the past researchers have got around this problem by interpolating from measurements before and after the eclipse or even comparing against measurements from a different (but somehow equivalent) day. We decided a new way was needed – we compared our synoptic weather measurements against a high resolution (1.5 km gridspacing in the horizontal) simulation of the Met Office weather forecast model. Now, a thorough search of the Met Office model code revealed a distinct lack of an ‘eclipse parameterization scheme’ so our forecast was completely ignorant of this spectacular phenomenon. Hence, we could use the model to tell us what would have happened without the eclipse.

Temporal evolution of MIDAS surface meteorological observations (left column, plotted at the observation hour) and UKV model output interpolated to station locations at times when MIDAS surface station observations are available (right column) for (a-b) temperature, (c-d) wind speed, and (d-e) wind direction. Thin lines are for individual stations with, for a given variable, a given colour corresponding to data from the same station. Thick black line is the mean value with error bars given by ±1.96 standard error (yielding the 95% confidence interval). From Gray and Harrison (2012).

So, what did we find? We concentrated our analysis on an inland, relatively cloud-free region, including Reading and found differences occurred between the evolution of the station measurements and forecast predictions at the same stations after the onset of the eclipse. The winds decreased by an average of 0.7 ms-1 and turned anticlockwise by an average of 17 degrees in the station measurements but not in the forecasts. Is this finally conclusive proof of the eclipse wind?

Eclipse lovers might like to know that the next total Eclipse over the UK will be on 23 September 2090. It’s unlikely I’ll be running the Met Office model then but maybe the partial eclipse of 20 March 2015 will provide the opportunity to further explore the existence of the ‘eclipse wind’.

As a novice but increasingly obsessed gardener, I’ve been really interested in how our #wetdrought has been shaping the green space all around us. Certainly all those additional water butts haven’t been put to good use just yet.

In addition to the contrast in rainfall between March and April there was also a big change in the sunshine anomaly from March to April but on the plus side a lower than normal number of frosts despite some cold daytime maxima.

One problem for plants when the soil is waterlogged like it is now is that oxygen uptake by the roots is reduced which can lead to root rotting. Judging by my lawn, I suspect that this has been happening quite a bit to some of the young plants in my garden and confusingly they actually look like they are wilting! Soil moisture can also be measured from space (although not at great depth) and the most recent maps of this variable show a broad area of wet conditions over all of Northern Europe. Over Iberia, in contrast, conditions are very dry and likely a continuation of the dry winter associated with the lack of storm activity in this region.

Of course, as is always the case in the garden, there is always one plant which thrives in the conditions which all the others hate. Since the RHS reliably informs us that flag irises will be perfectly happy in these damp conditions I thought I would close with a gratuitous picture of one in full bloom.

“April doet wat hij wil” is a common saying in Dutch, meaning: “April does whatever it wants”, and suggesting that the weather in April couldn’t care less about any hosepipe ban. Indeed, even though we are in drought, we saw the wettest April on record in the UK. Interestingly, in their news release on April being the wettest in a century, the MetOffice also noted that “despite the heavy rain experienced this month, sunshine amounts so far this month have not been far off the average“. Thus, unless all the rain fell overnight, we will have seen plenty of isolated showers in April, bringing the ideal type of weather for the DYMECS project here at Reading.

DYMECS (the Dynamical and Microphysical Evolution of Convective Storms)

The DYMECS project is a collaboration between Reading and the MetOffice, aimed at studying the evolution of convective storms in observations and in the MetOffice forecast model. Our principal observations are made with CAMRa (Chilbolton Advanced Meteorological Radar) at Chilbolton, which we use to track storms with an automated scanning algorithm, retrieving high-resolution data on the microphysical and dynamical structures of these storms. Similar data will be extracted from the forecast model and separate model runs for sensitivity studies, which may show how a different representation of certain microphysical and dynamical processes can improve the evolution of convective storms in the model. In this blog, we will focus on how a single scanning day is approached by our team.

What is a perfect day for DYMECS?

UKV rainfall forecasts for 14Z 2012-04-11 (Emilie Carter). The UKV is the 1.5km resolution operational forecast model and is run every 6 hours. These images are cropped to show the rainfall over the Southern UK, which we can potentially scan with the radar at Chilbolton. The top right shows the rainfall observed with the radar network.

This set of forecasts for 3pm on Wednesday 11th April, 2012, shows the kind of rainfall pattern that indicates an abundance of convective storms. There are plenty of storms in the vicinity of Chilbolton (conveniently near the center of these plots) and when studying the animation, we see that the storms develop from small rainfall regions in the early afternoon to larger storm systems in the evening, so we may be able to track some of the storms as they evolve into bigger systems. The next step is to check with the people at the Chilbolton facility that the radar is available and – in case it’s a holiday or weekend – if someone will be around to turn it on and off for us!

Step 1: Identifying your target storm

In order to find out where the storms are in relation to Chilbolton, we use the MetOffice rainfall radar data (Nimrod), which is updated every 5 minutes and shows rainfall observations on a 1km grid. A basic flood-fill algorithm will identify individual rainfall regions, given an initial threshold (usually 1mm/hr) and assign each storm a label. For each labelled storm we then store its area, range and azimuth position relative to Chilbolton, maximum rainfall, and a whole host of other properties.

Step 2: Keeping track of your target storm

Prioritized storms tracked continuously

Since we want to make sure we keep tracking the same storm once we’ve decided it’s interesting, we also need to know where it’s going. First, we compare two consecutive Nimrod images (at times T-2 and T-1) and use a cross-correlation function to obtain the displacement of rainfall features from one to the next, which gives us u and v velocity fields. All the storms identified in the image from time T-1, S(T-1), are then advected by their mean velocity, to provide us their new locations at time T, S’(T-1). We compare the locations of these advected storms S’(T-1) with the actual storms observed at time T, that is S(T) and if there is enough overlap between two storms, we assign the storm at time T the same identifying label as the overlapping storm from time T-1. As you can see in the animation, this algorithm seems to keep track of the same storms (in the red boxes) quite convincingly.

Step 3: Blasting storms with microwaves!

To make efficient use of the radar, storms are mostly scanned if they are located in roughly the same region (see the animation). For the three or four most interesting storms, we do two types of scans:

1. A set of RHI (range-height indicator) scans at a fixed location through most intense rainfall, so that we can study the convective cores. An example of an RHI scan through a convective core is shown below. On the left, we see where this scan is in relation to the rainfall data. Top right shows the radar reflectivities, with a strong echo above 40 dBZ at about 100km, indicating that there might have been hail involved. We can also easily tell the height of the storm along the scan. Bottom right shows the radial velocity, derived through radar doppler measurements, which indicate the low-level convergence and upper-level outflow.
2. A set of PPI (plan-position indicator) scans at different elevations, giving us slices through the storms at different heights, which can be reconstructed into 3D volumes so that we can study the full storm structure as it evolves. The figure below shows a set of PPI scans that have been collated and the isosurfaces of 5dBZ and 20dBZ are shown to indicate the extent of cloud (grey) and precipitation (red) of this storm. When the same storm is tracked throughout its life cycle, such 3D volumes will enable the analysis of storm area at different heights, for instance.

Step 4: Return to Step 1, or CTRL+C

At some point, either the storms will have moved out of the range of the radar or the working day is done (unless we’re very keen) and we get time to analyse the data. Our next steps will be to find the storms that we successfully tracked throughout a large part of their lifetime and plot out the evolution of some of their characteristics, such as area of rainfall, maximum height of a given reflectivity, and number of convective cores. Similar analyses will be done on storms in the MetOffice forecasts and sensitivity runs so keep an eye out for more DYMECS-related WCD blogs and presentations in the near future!

The Sun’s magnetic field varies on a range of timescales. Over hours and days, this leads to solar activity such as X-ray flares and coronal mass ejections (CMEs), which disturb the Earth’s space environment. This “space weather” waxes and wanes with an approximately 11-year “space season,” most apparent in the number of sunspots, but present in all forms of solar activity. Finally, there are much longer term changes in the solar magnetic field, on centennial and possibly even millennial times. It makes sense to refer to these timescales as “space climate.”

While sunspot records go back a few centuries and proxies for solar activity go back thousands of years, our experience with the solar magnetic field is largely based on the direct measurements only possible during the space age. The figure below shows sunspot number back to 1610: black is a monthly mean clearly showing the solar cycle, red is an 11-year running mean, which demonstrates the cycle-to-cycle variations. The space-age is shaded yellow: clearly, these observations have not been representative of typical conditions, with highly elevated sunspot numbers. The most recent data hint that the “grand solar maximum” of solar activity, which started around 1940, is drawing to a close. So you could say we’re in the midst of space climate change.

The last solar maximum, the seasonal peak of solar activity, was way back in 2000. What does the present/coming cycle hold and how will we know when the Sun’s reached a new solar maximum? Cycle-to-cycle variations in sunspot number mean a simple threshold cannot be used to define solar maximum. The figure below shows that assuming solar maximum will be 11-years from the previous one is also inadvisable – the previous cycle, from minimum to minimum, was almost 12.5 years long. The current cycle could be similarly protracted or be as short as 9 years. Thus sunspot number is only really useful for identifying solar maximum well after it has passed.

Spacecraft measurements of the solar wind in the near-Earth space also show strong solar cycle variations. However, much like sunspot number, using this data to identify solar maximum is complicated by the superposition of climate variations and solar cycle variations. The figure below shows the B, magnitude of the solar wind magnetic field in near-Earth space. Black is spacecraft observations, pink is a reconstruction based on geomagnetic data and the rainbow colours are a range of analogue forecasts based on past behaviour (which I won’t discuss in detail here). From the spacecraft observations, B has clearly been rising for a year or so, suggesting the new cycle is well underway. However, the current value has only just reached that of the previous minimum (1996) and there have been very few energetic CMEs or flares this cycle. So is the Sun still a few years away from a “normal” solar maximum, or is solar maximum nearly upon us, but this cycle is rather puny?

Well, us University of Reading folk (Mike Lockwood, Chris Davis, Luke Barnard, Simon Thomas and myself) are betting on the latter. Though we do have some insider information. The plot below shows the average latitude of sunspots in the north and south solar hemispheres. In the past, solar maximum has occurred when the sunspot latitude has reached ~17 degrees. Coupled with the declining strength of the Sun’s polar magnetic field (not shown), it appears solar maximum is on course for the end of 2012. That doesn’t leave much time for solar activity to pick up, suggesting clement space weather for the coming season. However, there are two caveats to this BBQ cycle. Firstly, while solar storms are expected to be less frequent, when they do occur they could be more extreme in magnitude (ask Luke Barnard about this). Secondly, the reduction in B will allow more galactic cosmic rays to reach Earth’s atmosphere, both posing a space-weather hazard and affecting the global electric circuit (ask Giles Harrison about this).

Also important for Earth’s atmosphere, a weak cycle will likely mean a reduction in solar irradiance. Below is a composite of the total solar irradiance (TSI) in black, with an analogue forecast in pink. This recent solar minimum resulted in the lowest TSI yet observed and it looks like the imminent solar maximum is set to result in a TSI about 0.5 Wm^-2 below the previous space-age maxima. This wavelength-integrated change is small, but it could be far more drastic in the x-ray and UV portions of the solar spectrum, which would have effects for the ionosphere, thermosphere and even stratosphere. Furthermore, this could well be the start of a long-term decline.

We have another excellent selection of bloggers for this term:

Week 1 (27th April) – Matt Owens
Week 2 (4th May) – Thorwald Stein, Kirsty Hanley & Emilie Carter
Week 3 (11th May) – Andrew Charlton-Perez
Week 4 (18th May) – Sue Gray
Week 5 (25th May) –
Week 6 (1st June) – Ros Cornforth
Week 7 (8th June) – Ray Bell
Week 8 (15th June) -
Week 9 (22nd June) – Ben Lloyd-Hughes
Week 10 (29th June) – Rob Lee and Rob Warren

Drought has certainly been in the news recently and the beauty of the digital age means that we can monitor this in real time – note the spike on Monday 12^th March when the UK government announced the imposition of a hosepipe ban:

Figure 1 Drought in the news.

We can even visualise it on an interactive map such the proto-type Walker Drought Watch (comments on this are most welcome!).

Drought is amongst the most deadly and costly of natural hazards. Systematic collection of data relating to natural disasters began around 1970 (Guha-Sapir 2004) and since then recorded droughts have affected the lives of nearly 2 billion people and killed over 600,000. In the European context, modern water supply infrastructure has all but eliminated direct mortalities yet the societal impacts of water scarcity remain and cannot be overstated. For example, the European Commission (European Commission 2007) estimate the direct costs of drought within the European Union to be €3 billion per year. This compares annual losses in Europe from windstorms (€2 billion per year) and flooding (€4 billion per year) at 2010 prices. A thorough understanding of the peril is essential for mitigating against the risk as it stands and for preparedness in the face of climate change.

Yet drought remains a nebulous concept and a universal definition has proved to be elusive. The Oxford English Dictionary defines drought as:

1. The condition or quality of being dry; dryness, aridity, lack of moisture.
2. Dryness of the weather or climate; lack of rain.

Unfortunately, the conflation of dryness with aridity and weather with climate serves more to confuse than illuminate. The WMO international meteorological vocabulary provides a

1. Prolonged absence or marked deficiency of precipitation.
2. Period of abnormally dry weather sufficiently prolonged for the lack of precipitation to cause a serious hydrological imbalance.

However, to focus solely on precipitation is to neglect the importance of evaporation and transpiration as moisture sinks which reduce the amount of water available for use. The definition also ignores the importance of lateral inflows (stream and ground water flows) into a region that can serve as important water sources in addition to the local precipitation. Further, the definition makes no reference to the timing of the precipitation deficits, a factor which is crucial in the determination of many drought impacts. Sheffield & Wood (2011) succeed in defining drought both accurately and succinctly as ‘a deficit of water relative to normal conditions’.

Striving for a quantitative definition, many attempts have been made to describe drought numerically through the development of drought indices. The difficulty, and importance, of defining drought objectively is manifest in the large number of indices that have been proposed for use in drought monitoring (well over 100 and counting in the peer reviewed literature). Particular indices have typically been developed on a ad hoc basis to emphasise some particular drought impact, be it meteorological, hydrological, agricultural or socio-economic (to borrow the classification of Wilhite & Glantz 1985). Unfortunately, rather than clarify the definition, the plethora of indices creates further confusion and brings into question the very feasibility of defining drought in a quantitative fashion outside of specific impacts.

Drought occurrence depends on the interaction between the source of the available water and its intended use. This leads to different perceptions of the importance of a given drought for different segments of society. The meteorologist, who views drought as below normal precipitation in a region, might consider a run of 10 dry days to be significant. The arable farmer, who depends on adequate soil moisture for crops during the growing season, will be interested in monthly rainfall totals. Whereas, the water supply company may be interested in aquifer levels that take months or even years to recharge. Location also matters e.g. consider the impact of a summer dry spell of 30 days over London to the same over Tripoli, as does spatial extent.

The majority of notable high precipitation events are characterised by highly localised, short lived, heavy down bursts. The same is not true of the most notable drought events. These typically last for several months or even years and span thousands of square kilometres. Thus, drought characterisation is an intrinsically spatio-temporal problem. Lloyd-Hughes (2012) suggests that the space-time structure of the precipitation deficits is well suited to the characterisation the phenomenon.

The drought now affecting the UK provides an interesting example of the space time evolution of a large scale European drought. Figure 2 (a) is an isometric projection of the event. The view is from the southwest looking backward in time from March 2012 to January 2011. Whilst the image serves the pedagogical purpose of illustrating the 3 dimensional coherency of the event, it does little to reveal the spatio-temporal characteristics of the drought. Advances in web technology such as webGL might soon facilitate the inclusion of interactive displays of this sort of data. Until then, panel (b) provides a Hovmöller type plot of the number of voxels in the drought volume counted north-south through time. This view is made up of pixels which are shaded according to the number of cells within the event volume counted along each meridional band. The coloured elements represent the shadow, or `footprint’, of the event as projected onto the back plane of the bounding box shown in panel (a). The depth of colour represents the integrated thickness of the event in this direction. Panel (c) provides a similar view but counts cells east-west through time. Finally, panel (d) maps cell counts by location to represent the drought severity and maximum spatial extent. Whilst this remains an imperfect representation of the full 3-dimensional event structure, the combined views capture the essence of the event. Importantly, we see the core of the drought centred over mid-western Europe and a zonal expansion into Central and Eastern Europe.

Figure 2 A spatio-temporal representation of the 2012 European drought.

No discussion of the present drought would be complete without reference to the last comparable event which occurred in 1976. Using a standardised measure of the 12-month running total of precipitation (SPI12) we can directly compare the two events.

Figure 3 Comparison of the 1976 and 2012 European droughts.

We can immediately see that the events are of a very different character. The key difference is that in 1976 viewed ‘as if’ today the moisture deficits are orientated south-West – north-East as opposed to the more zonal structure seen today. Looking to the UK, the present drought is largely restricted to the England and Wales rather than the whole of the country and is seen in parts to be even more severe than the 1976 event. A SPI value of less than -2 equates to a return period of approximately 50 years and we need look back to 1921 to find a UK drought of similar severity.

Ascribing the cause of a drought is difficult. Meteorologists typically explain drought in terms of atmospheric circulation patterns that favour the suppression of precipitation (cue much discussion about blocking). However, to paraphrase Hounam (1975), such explanations merely describe the meteorological motions and processes rather than identify the fundamental dynamic and thermodynamic forces which caused the abnormal patterns. Thankfully, the digital age helps again, this time in the guise of computers powerful enough to run weather resolving climate models that are capable of simulating droughts with highly realistic spatio-temporal characteristics. It is likely to be through the analysis of this modelled world that we will gain deep insights into the
origins of drought.

Last week we observed some of the most significant solar activity of solar cycle 24 (SC24), with a series of solar flares and Earth directed coronal mass ejections (CMEs) which were forecast to interact with the near-Earth space environment. This attracted significant media coverage and it was reported that this sequence of events had the potential to cause disruption to Earth’s technological infrastructure, with the possibility of damage to power distribution networks and satellites.

As it happens, the event did interact with the near-Earth space environment, but did not cause significant disruption. Here I’m going to take the opportunity to have a look at this event and give a short summary of some of the ways it could have affected us. I will also briefly analyse how the occurrence of space weather events in SC24 has so far compared with the previous three cycles.

Fig 1. An image of the Sun recorded by NASA's Solar Dynamics Observatory, which displays the X-class solar flare of 06/03/2012 towards the upper left of the image.

This sequence of activity originated from active region AR1492, where two X class flares were released within roughly an hour of each other. Solar flares are the explosive conversion of energy stored in solar magnetic fields into electromagnetic radiation, and the acceleration of charged particles in the solar atmosphere. Flares are designated a classification of either B, C, M, or X according to the observed maximum x-ray flux intensity and each successive class is an order of magnitude larger than the previous. The peak intensities of flares at x-ray wavelengths are distributed as a power law, and X-class events are the rarest, most energetic events. Active region AR1492 was observed to be rumbling with activity ever since the solar rotation brought it into the visible disk, producing some M-class flares and another X-class flare. However the X-class flare on March 6^th was particularly noteworthy because it was the second most energetic flare observed in SC24 (a slightly larger flare occurred in Aug 2011). The last flares observed of equal or greater energy were recorded in Dec 2006, in the declining phase of SC23.

The active region also released two Earth directed CMEs associated with these flares, which were estimated to be travelling at approximately 2000 kms^-1 and 1700 kms^-1, which is far into the high tail of the distribution of CME speeds.  Here is a link to an animation of the first CME, which was recorded by the LASCO instruments. The two outer ring images (blue and red)  are created by obscuring visible light directly from the sun, and detecting visible light reaching the instrument via Thompson scattering off of electrons in the solar corona. Consequently brighter regions represent increased density, and the CME is observed as an increasing “halo” of light as the coronal material is thrown out towards Earth.

How was this event expected to interact with near-Earth space? Unfortunately this will require the introduction of some more acronyms.

Flares and CMEs lead to the generation of solar energetic particles (SEPs). SEP events are observed as large increases (up to 4 orders of magnitude) in the flux of particles in the energy range of roughly 1-100 MeV (1eV = 1.6×10^-19 joules). Flare SEPs are accelerated at the footprint of a flare in the solar corona, and typically last a few hours. High speed CMEs propagating through the solar corona and interplanetary space can develop shock waves (analogous to the shock wave of supersonic aircraft) that efficiently accelerate charged nuclei. Shock accelerated SEPs are generally observed to have a higher intensity in near-Earth space, and can last for up to several days. In both instances SEPs can be detected remotely from the acceleration region, as the particles are guided by interplanetary magnetic field lines connecting the source and observer locations.

The high intensity of energetic particle fluxes observed during SEP events pose a significant hazard to satellites, as they may irreparably damage solid state electronics, whilst chronic exposure over time degrades the performance of the photovoltaic cells that power most satellites. Furthermore, SEPs are a concern in the radiation protection of astronauts as well as the crews and passengers of high-altitude aircraft; particularly those taking polar routes, as SEPs are guided here by the geomagnetic field. For example, an SEP event in 1972 was large enough that had any astronauts been outside the protection of the Earth’s magnetosphere, they would have probably received a fatal dose of radiation.

Furthermore, the Earth’s magnetosphere is in dynamic equilibrium with the solar wind, responding to variations in the speed and density of the solar wind flow and the interplanetary magnetic field (IMF) vector.  When the northward component of the interplanetary magnetic field (B_z) is negative, the solar wind is able to efficiently deposit mass and energy into the magnetosphere, causing an intensification of Earth’s ring current. If these temporary enhancements of the ring current are above a certain threshold they are called a geomagnetic storm. This time varying perturbation to the magnetic field at the Earth’s surface can generate geomagnetically induced currents (GICs) in power distribution networks. The generation of large GICs in power grids can cause catastrophic damage to power transformers –as demonstrated by the failure of the Hydro Quebec power system in 1989, due to a large geomagnetic storm.

We can use in-situ measurements of the solar wind to have a closer look at how this event evolved.  Two satellites particularly useful for this are ACE (Advanced Composition Explorer), a satellite situated at the L1 Lagrange point on the Sun-Earth line, and GOES 13 (Geostationary Operational Environmental Satellites) – which as the name suggests is in a geostationary orbit. In Figure 2 panels A and B display the solar wind speed (V_­sw) and northward component of the IMF as measured by ACE, and panel C displays the energetic proton flux (F) recorded by ACE (red) and GOES 13 (green). The bottom panel shows the variation of the disturbance storm time (Dst) index of geomagnetic activity. The Dst index is constructed to be sensitive to changes in Earth’s ring current, and is therefore suitable for diagnosing geomagnetic storms. Time zero on the plots is set to coincide with the X-flare onset.

Figure 2. Panels A-D show the variation of the solar wind speed, northward component of the IMF, energetic proton flux, and Dst geomagnetic index after the onset of the X-class flare on 06/03/2012.

An SEP event commenced a few hours after the X-flare occurrence. The persistence of this event over many days implies that these are predominantly shock accelerated SEPs. The SEP event was less intense at GOES relative to ACE, which is possibly due to the magnetosphere deflecting some of the SEPs and shielding GOES, but the separation of the satellites and instrumental differences will be factors too. According to an SEP event database produced by NOAA, this is the largest SEP event since 2003, with the 11^th largest peak energetic proton flux in a database of 235 events spanning 1976-present.

At approximately t = 3 days there appears to be a discontinuity in the solar wind speed. This sharp increase in the solar wind flow speed is consistent with the picture of an SEP accelerating shock, ahead of an Earth directed CME, sweeping over ACE, although it would require further work to confirm this.

Thresholds of Dst <50 nT and DsT<100 nT are often used to define “moderate” and “strong” geomagnetic storms (DsT<200 for a “severe” storm). Panel D therefore shows that shortly after the X-flare onset a moderate geomagnetic storm took place, with a brief  period of recovery before the commencement of a strong geomagnetic storm after t = 2.  Comparing the progression of the geomagnetic activity with the variation of B_z shows that the geomagnetic disturbance intensifies after periods when B_z is more negative – the condition required for effective mass and energy deposition from the solar wind to the magnetosphere.

This event attracted a lot of attention because it was one of the few space weather events of SC24. Although this event produced a large SEP event, and a strong geomagnetic storm, it is not the most remarkable of events. To me, what seems more remarkable, is the absence of events in SC24 – such an absense that a fairly typical event becomes noteworthy.

As well as producing a database of SEP events NOAA also produce a catalogue of solar flares and we can use these to compare how the occurrence of SEPs and X-class flares in SC24 compares to previous cycles. Some recent work by Owens et al. has shown that we are probably approaching the maximum activity of SC24, which is predicted to occur sometime in 2012, with the best estimate of the current phase of the solar cycle being 112 degrees.  In Table 1 the total occurrence of SEP events and X-class flares for the phase period 0-112 degrees, for SC21-24, are listed.

There have been roughly half as many events in SC24 than in the three previous cycles. Could this be linked to predictions that the grand solar maximum of the space age is ending, with a predicted 8% chance of a return to Maunder minimum levels of activity in the next 40 years? Or could it just be a statistical fluctuation due to counting small numbers of events? I’m not sure yet, but I do think the next few years will be a really interesting time to study the Sun.

N.B The solar wind data, Dst geomagnetic index, and SEP and flare data used in this post were obtained from NOAA’s Space Weather Prediction Center and I’d like to thank Simon R. Thomas for help compiling this post.

The Earth is a complex system of interacting components, such as the atmosphere and ocean, which produce a wide variety of natural variability. This natural variability ensures that the evolution of a particular region’s climate, e.g. that of Western Europe, could be completely different to another region, or indeed the global mean climate. Such variability can impact on many areas of society; for example winter energy usage, or agriculture in sensitive regions.

Of course, institutions around the world, like the Met Office, are trying to predict this variability, especially on monthly to seasonal timescales. However, recently there has been an increasing focus on predicting at multiannual to decadal timescales. You may be surprised to find that, usually, climate projections make little use of the current observed climate state. This is partly because initial conditions matter little to projections of  the climate in 2100, which instead try to convey the likely range of climate states that may occur given a particular emission scenario. However, over the next few years, and maybe up to a decade or two ahead, it may be possible to predict whether certain climate patterns are more likely to occur than others.

A good example of multidecadal variability is the observed changes in the North Atlantic. Over the past 20 years the North Atlantic has warmed significantly. This is especially true for the subpolar North Atlantic (between 50-70N), which went from anomalously cold to anomalously warm following the mid 1990s (see Figure 1), and was associated with significant weakening of the subpolar North Atlantic circulation. These recent changes in North Atlantic heat content represent a return to the positive phase of the Atlantic Multidecadal Oscillation, which may have impacts on North American and European climate, and may have been important for changes in the numbers of hurricanes. So whats going on here?

Figure 1. Shows the difference in the 0-500m average temperature (degrees C) between 1986-1995 and 1996-2005

From the 1950s to the mid 1990s the heat content of the subpolar North Atlantic actually decreased significantly. This cooling can be largely understood as a direct response to the atmospheric forcing. During this time the North Atlantic Oscillation (NAO) index became more positive. Simply put, a positive NAO is associated with stronger winds across the North Atlantic, and hence increased cooling of the ocean due to increased turbulent heat fluxes (sensible and latent). Since the mid 1990s the NAO index has been much more neutral. Thus, the warming of the North Atlantic could simply be explained by a reduced cooling of the ocean. But was that really the case? Such a hypothesis assumes no dynamical changes in the ocean.

There is already significant evidence, largely from models, that dynamical ocean changes should be expected in response to the NAO. For instance the oceans circulation is largely driven by the curl of the wind stress. Thus, changes in winds can change the ocean circulation, and the northward heat transport of the Atlantic. But in the North Atlantic, it is not just the curl of the wind stress that is important for the circulation. Many model studies suggest that decadal changes in the Atlantic Meridional Overturning Circulation (AMOC), an important ocean current that transports heat northward throughout the whole Atlantic, could follow persistent positive NAO events. This is because the convective formation of deep water at high latitudes, which is important to sustain the AMOC, usually increases following increased cooling.

To try and understand the cause of the warming we performed a few experiments to look at this event using an ocean-only model that was forced with the historical wind stress and buoyancy fluxes (heat flux and precipitation).  The experiments support the view that the warming of the North Atlantic was primarily due to changes in ocean heat transport that followed the positive NAO, and not simply the changes in local surface heat fluxes. Furthermore, the experiments also strongly suggest that a buoyancy forced strengthening of the AMOC played a key role.

Given that the warming of the North Atlantic was not just an instantaneous response to the atmospheric forcing then it is encouraging for the prospects multi-year predictions. This conclusion is also supported by an analysis of the Met Office’s Decadal Prediction System (DePreSys).  Predictions started immediately prior to the warming seem to be able to predict the warming well (see Figure 2), whereas predictions that do not assimilate observations (NoAssim: think typical IPCC type climate projection) do not. Importantly, the initialisation of a strong ocean circulation, and particularly a strong AMOC, is the key for successful predictions.

Figure 2. a) shows the observed 1995-1997 0-500m average temperature anomaly (Degrees C), relative to the 1951-2006 mean. b) and c) show the same, but from DePreSys and NoAssim predictions started from the Nov 1994 observed state. d), e) and f) shows the same as a), b) and c) but now for the years 1998-2000. Black contours show where the ensemble mean anomaly of the 9 members ensemble is significant

It is true to say that this is not yet a fully understood story; there are still some key uncertainties, for example, what is more important for driving the subpolar Atlantic circulation on multidecadal time scales – wind stress or buoyancy fluxes? The results seem to be somewhat dependent on the model used in each study. There is also large uncertainty as to the role of the forcing, especially the aerosol, which varies significantly in time. However, the key question is how much, if at all, these changes in the North Atlantic have had an impact over land, and could we predict it? Such a question can be challenging to answer, given all that is occurring in the climate system, but our initial analysis of DePreSys appears to be encouraging.

Understanding and predicting the summer monsoon in South Asia is important for more than a billion people since their lives depend on the four summer months (June to September) to bring 80% of annual rainfall. Knowledge of the timing, intensity and duration of the monsoon are important for agriculture and industry, while extreme rainfall can cause devastation to society.

Most future projections of monsoon rainfall from global models of the ocean and atmosphere suggest generally small increases (albeit not of the same size over the whole region). However a recent paper has suggested that the monsoon circulation has weakened during the last 50 years.

The low-level monsoon circulation in South Asia consists of a south-westerly flow bringing air laden with moisture from the southern Indian Ocean, across the equator and Arabian Sea to India and nearby countries. A weakening of this circulation can be understood in terms of an overall stabilization (slowing down) of the tropical circulation, which has been shown both generally and in the tropical east-west Walker Circulation. This is a simple consequence of mass conservation since increases in moisture held by the lower atmosphere accelerate beyond changes in global mean precipitation as the planet warms.

Since the monsoon circulation affecting South Asia is spread over such a large portion of the tropics, it is also expected to slow down under warming. But what does this mean for the finer details of regional precipitation? When the monsoon flow first hits India, it rises up over the Western Ghat mountains, dumping considerable precipitation there. Further east, there is a lull or rain shadow, over south-eastern India. Further downstream, the monsoon flow again hits mountains: this time the Arakan Range of Burma. It is not then unreasonable to think that if the horizontal flow incident upon these mountain ranges is reduced, then the resulting uplift and orographic precipitation will also reduce. A range of trends in observations of circulation and precipitation support this. In particular, negative rainfall trends are measured over the Western Ghats along the west coast of India (see Figure 1).

Figure 1: Spatial map of linear trend of rainfall rate for JJAS season based on the (left) APHRODITE (0.5° × 0.5° resolution), (right) IMD (1° × 1° resolution) daily gridded rainfall datasets. The units are mm/day/57years for the period 1951–2007. Copyright © Springer-Verlag, 2012.

It is only in this new study, where an ultra-high resolution global atmospheric model is used to make estimates of future monsoon rainfall, that future monsoon rainfall over the Western Ghat mountains also declines. The model used runs on a grid spacing of 20km, much finer than the 100-200km grid scale typical of other state-of-the art climate models. This allows it to better resolve the steep slopes of the narrow Western Ghats. But are its results believable and is this where the future will take the South Asian monsoon? Results from the coarser resolution models referred to in the IPCC reports show considerable uncertainty among the sign and pattern of rainfall changes over South Asia. While the model used here is at much higher resolution allowing the flow to better interact with orographic features, it lacks an ocean component and prevents important feedbacks between the monsoon circulation and sea surface temperatures. Ultimately the same result needs to be verified in a wider number of high-resolution models, with coupled ocean components.
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At the end of January 2012 the Department for Environment, Food and Rural Affairs (Defra) published the first Climate Change Risk Assessment (CCRA) outlining the potential impacts of climate change on the United Kingdom (UK). The largest risks identified in the report concern water, with summer shortages and winter flooding expected to become more commonplace in the UK. With an increased risk of winter flooding, it may be necessary to develop and improve flood defences to protect UK infrastructure and society. But before considering future flooding, what is the key atmospheric driver of UK winter flooding? This is where Atmospheric Rivers (ARs) come in.

An AR is a narrow filament along which moisture is transported from the subtropics across the mid-latitudes (see Figure). At any one time there are four or five ARs present across the mid-latitudes where approximately 90% of poleward water vapour transport occurs. ARs are located in the lower troposphere within the broader warm conveyor belt of extra-tropical cyclones and are regions of high water vapour content and strong winds. The large water vapour transport in ARs is essential for water supply, but also a hazard due to the heavy precipitation and flooding that can occur when an AR makes landfall. Most research investigating ARs and flood occurrence has been in western North America. Colloquially known as the “Pineapple Express”, these ARs can transport moisture from near the Hawaiian Islands to North America.

Recent research has shown that winter flooding in Britain is connected to ARs. The devastating Cumbrian floods in November 2009 were caused by a persistent AR that was located over Cumbria for about a day (Figure a). The moisture transported in the AR was forced to rise over the Cumbrian Mountains causing intense rainfall and flooding. Even more recently on 17^th November 2010, Cornwall experienced heavy rainfall and floods; the AR behind this event is also shown in the Figure (b). Moreover, ARs have been linked with the 10 largest winter floods in a range of British river basins further indicating that ARs are crucial in explaining winter flooding in the UK. One complicating factor in linking ARs with floods is the river basin itself. Each basin responds to rainfall in a different way depending on properties such as the geology underlying the basin, the steepness of the terrain and land use. With impermeable bedrock, steeper mountains and higher rainfall receipt in western Britain, basins in this region have a rapid-response to rainfall and consequently the strongest AR-flood connection in the UK.

With a clear link between ARs and UK winter flooding, what will happen to ARs under current climate change projections? Two factors could affect ARs in a warming climate. Firstly, a change in AR frequency is likely to impact the number of winter flood events. So if the large-scale atmospheric circulation alters as to cause more persistent extra-tropical cyclones and their associated ARs to hit the UK, then there is a possibility for more winter flood events. Secondly a warmer climate is likely to give rise to an increase in saturation vapour pressure and higher atmospheric water vapour content. It is thought that this will change the hydrological cycle and intensify precipitation extremes leading to a risk of larger floods. However, as yet the effects of anthropogenic climate change on ARs over the North Atlantic are not certain. An assessment of the latest climate change projections will be a good tool to aid our understanding of future changes to ARs that strike the UK.