EUMETSAT Conference 2014: Final highlights

Headquarters of the WMO, which we visited during the conference for a discussion on the socioeconomic benefits of meteorological satellites.

The EUMETSAT Meteorological Satellites Conference 2014 featured a lot of new science. Two particular points which stood out to me was the assimilation of new products into numerical weather forecasting systems, and the use of satellite data in improving our conceptual understanding of weather systems.

Until this conference I was not aware how new it was to incorporate soil moisture into numerical weather forecasting systems. Such forecasting systems spend a good deal of resources on assimilating observational data to initialise the forecast. This is very important because, as pioneering work by Ed Lorenz showed back in the 1960s, tiny differences in the initial state of an atmospheric model (and of course the real atmosphere) can lead to huge differences in the resulting forecast, even for relatively short-range forecasts.

Soil moisture is clearly a useful thing to know about in our forecasts. For weather forecasts it mainly plays a role in supplying water for weather systems. Wet surfaces supply water to the atmosphere, causing or intensifying rainfall.

A few years ago soil moisture satellite products were not considered mature enough to assimilate into weather forecast systems. This is partly because our measurements were quite uncertain (we couldn’t attach very accurate numbers to them), but also because our uncertainty was poorly characterised (we didn’t know how accurate our measurements were). In a sense, the latter is more important. Like much of science, the point is not always knowing things exactly, but accepting that it’s impossible to achieve perfect accuracy and to at least know exactly how certain we are about a measurement (Tamsin Edwards has a related blog post focusing on climate rather than weather).

After some experimental studies showed the potential for soil moisture data to improve weather forecasts, operational forecasting centres across the world began to adopt this extra data source – the ones I heard about at the conference were ECMWF and the UK Met Office, but there are probably others.

Now let’s move to something less mathematical, but equally as important and exciting. On Thursday I listened to two excellent presentations on the Conceptual Models for the Southern Hemisphere (CM4SH) project. The rationale behind CM4SH is that the vast majority of weather forecasting ‘wisdom’ is derived from Northern Hemisphere perspectives, through an accident of history. But understanding the weather of the Southern Hemisphere isn’t as simple as flipping everything upside down. Although the physics of the weather is clearly the same, the actual meteorological situation in Southern Hemisphere countries is different. For example, South Africa lies in the midlatitude belt like Europe does, but it sits rather closer towards the Equator, so the same weather system could have different effects. The configuration of Southern Hemisphere land masses is very different, and that leads to rather different weather behaviour.

CM4SH is a collaboration between the national meteorological services of South Africa, Argentina, Australia and Brazil. The work focused on building up a catalogue of typical meteorological situations in different regions of the Southern Hemisphere, analysing similarities and differences. The international CM4SH team used Google Drive to build a catalogue of these situations, their typical causes, behaviour and effects. Satellite imagery is obviously a major part of the catalogue, as it allows forecasters to track the flow of moisture, presence of clouds, direction and strength of winds. The resulting catalogue allows Southern Hemisphere forecasters to classify meteorological situations and quickly find out the typical effects of different systems. For example, if a forecaster sees a particular meteorological configuration, they can quickly check the catalogue for the effects of similar situations in the past, and see that they need to assess the risk of, say, flooding, in a vulnerable region.

I think projects like this reflect the power of the Internet to supercharge our science. Earlier this week I wrote about how the data from the new GPM mission were available and easily accessible within weeks. GPM is a huge international collaboration combining the resources of a whole constellation of satellites. CM4SH is a project which makes use of expertise from four national meteorological services to create an unprecedented collaborative resource for forecaster training and education, freely available. The CM4SH catalogue will grow over time and become more refined – the beauty of collaborative projects like this is that, as long as someone does a little pruning now and then, they can only ever become more useful.

EUMETSAT Post 1: Challenges and advances in satellite measurement

EUMETSAT Post 2: Socioeconomic benefits of meteorological satellites

EUMETSAT Conference 2014: Socioeconomic benefits of meteorological satellites

Globally, governments spend about $10 billion on meteorological satellites every year. That’s a lot of money. How do we know it’s worth it?

Yesterday night the EUMETSAT conference branched off to the WMO for a side-event asking that very question. I was impressed by the rigour of their calculations, but also by the thoughtful responses to the question of how this information should – and should not – be used.

Alain Ratier, Director of EUMETSAT, presented the results of a comprehensive activity aiming at calculating the benefit-cost ratio to the EU of polar-orbiting meteorological satellites. The cost of these things is relatively easy to estimate, but the benefits are a little more difficult. They approached the problem in two steps: first, what is the economic benefit derived from weather forecasts? Second, what impact do meteorological satellites have on weather forecast skill?

The resulting report contains some fascinating facts and figures. It has been estimated that as much as one third of the EU’s GDP is ‘weather-sensitive’. Of course, this isn’t the same as ‘weather forecast sensitive’, but it at least gives a sense of potential vulnerability. The report concluded that the total benefit of weather forecasts to the EU was just over €60 billion per year. Most of that comes in the form of ‘added value to the European economy’ (broadly, use of weather information to help manage transport networks, electricity generation, agricultural activities, and so on), but there are also contributions from protection of property and the value of private use by citizens.

Compared to the calculation of the economic benefits of weather forecasts, the calculation of the effects of satellite data on those forecasts is quite straightforward. One can assess this by ‘suppressing’ source of data in our weather forecasts. Forecasts proceed by using a numerical model of atmospheric physics to predict the future atmospheric state. Since weather prediction is a chaotic problem, it’s important we start the forecast from as close as possible a representation of the real atmospheric state. This is called initialisation and it’s absolutely crucial to weather forecasting.  In order to calculate the effects of satellite information, we can simply exclude satellites from the initialisation phase of the weather prediction.

(left) 5-day forecast for Superstorm Sandy, (middle) the forecast without polar-orbiting satellite data and (right) the actual conditions that occurred. Credit: ECMWF.

The results are quite astounding. Satellite data contributes 64% of the effect of initialisation in improving 24-hour forecasts (the other 36% comes from in-situ observations). This approach reveals that measurements from a single satellite, the EUMETSAT MetOp-A, accounts for nearly 25% of all the improvement in 24-hour forecast accuracy derived from observations. MetOp-A is a relatively new platform, indicating that recent advances are providing huge benefits to weather forecasts.

The impact of satellite observations is vividly illustrated by considering 5-day forecasts of the track of Superstorm Sandy made with and without satellite initialisation. Without the use of polar-orbiting satellites, forecasters would not have predicted that the storm would make landfall on the Western US coast. As it was, the 5-day forecast of the storm track was remarkably close to reality, allowing forecasters to issue warnings of imminent risk of high winds and flooding.

The conclusion is that meteorological satellites provide benefits that outweigh their costs by a factor of 20. This is a conservative estimate in which high-end cost estimates have been compared with low-end benefit estimates. One reason we might expect benefit estimates to be low is that private companies are often reluctant to reveal how they use weather forecasts, either because this information is commercially sensitive or because they risk being charged more for the forecast data they receive!

It’s important to consider the limits of this approach. The obvious one is that cost-benefit estimates do not include the number of human lives that have been saved by weather forecasts. Not only is this difficult to calculate, it’s also impossible to put an economic value to. It would be very interesting to see if the toolbox of social science research has some methods to assess the ‘social’ part of the ‘socioeconomic’ benefits, moving away from attaching monetary value to things and considering those benefits which aren’t as easy to quantify. This doesn’t have to mean human life; any non-monetary social benefit of weather forecasting could be considered.

I think this is especially valuable because it’s questionable whether the cost-benefit approach is truly appropriate. Cost-benefit analyses frame things in a certain way; the WMO and EUMETSAT representatives at the meeting were well aware of this. They may imply greater certainty than is appropriate, and they may encourage a naively quantitative approach to what is fundamentally a qualitative problem: is it for better or for worse that we have meteorological satellites? Answering such a question involves some value judgements simple quantitative approach can gloss over. As LP Riishøjgaard pointed out, although we can make this kind of cost-benefit estimate ‘frighteningly’ easily, it’s not obvious that we should.

EUMETSAT Post 1: Challenges and advances in satellite measurement.

EUMETSAT Post 3: Final highlights.

Rainfall and climate – a dynamic problem

This was originally posted on the EGU blog, GeoLog.

Rain is grace; rain is the sky descending to the earth; without rain, there would be no life. – John Updike

Rain quenches the thirst of soils and vegetation, fuelling ecosystems and much of the world’s agriculture. Whether it ruins a day on the beach or destroys a season’s harvest, it makes humans deeply aware of their vulnerability to the vagaries of the atmosphere. It’s important to understand how rainfall changes in a changing climate. Here, I will describe the issues in understanding precipitation changes and how two recent papers help to solve the puzzle.

Predicting rainfall is difficult. It is a small-scale phenomenon, especially in the towers of convective cloud in the Tropics. Weather forecasting models are just beginning to capture them properly at scales of a kilometre or so, but climate models, which have to be run for decades rather than days, calculate atmospheric conditions on scales of hundreds of kilometres. Rainfall has to be simplified in these models, since we cannot calculate the physical properties of individual clouds. These simplified representations are called parameterisations. A precipitation parameterisation relates the average rainfall over a large area to the average amount of water in the air. Different models do this in different ways and, because it’s a simplification, there is no definitive ‘right’ way. This means there is some disagreement among climate models about how rainfall will change in the future, especially in the Tropics (areas on the figure which are not stippled).

Climate model projections of precipitation change in a future with high greenhouse gas emissions. Left: current generation of models, Right: previous generation of models (around 2005). Top: December-February, Bottom: June-August. Stippling shows areas where models largely agree. White areas show complete disagreement among models (source: Knutti & Sedlacek, 2013).

If we think about precipitation in general theoretical terms, we can find laws which must be followed and use them to make predictions, as Issac Held & Brian Soden did in their study of how the hydrological cycle responds to global warming. Rain is caused by the upward transport of water vapour from the surface into the atmosphere, where it condenses, forms clouds and rains out. The amount of moisture going up must, of course, balance the amount coming back down as rain.

As the climate warms, the amount of water vapour a fixed mass of air can hold increases. This means that, as long as the circulations transporting water upwards remain the same, the total amount of water vapour going upwards must increase – which means the amount of rain coming down must also increase. This is called the ‘rich get richer’ mechanism, because it increases rainfall in regions where there is already a lot of rain driven by upward moisture transport. It’s a fundamental mechanism driven by thermodynamic laws…but that doesn’t mean it’s the only thing going on.

Convective raincloud in tropical Africa (photo credit: Jeff Attaway).

If climate model projections followed the ‘rich get richer’ mechanism, precipitation would increase most in the regions with the most precipitation currently. In fact it is more complicated than that. Robin Chadwick and his colleagues explored the effect of weaker vertical motions in a warmer climate. We can understand this by thinking about what carbon dioxide does to the vertical temperature profile. It warms the mid-troposphere (about 5 km up) more than the surface. To get convective upward motion, the air at the surface must be less dense (i.e. warmer) than the air above. Warming the air aloft suppresses this motion. The Chadwick decomposition calculates the part of the precipitation changes caused by changes in moisture (which goes at about 7% per K) and the part caused by the reduction in upward transport. They find the two tend to roughly cancel each other out, which means the spatial shifts in precipitation are determined by changing patterns of surface temperature (since warm surfaces produce upward motion).

Sandrine Bony and her team decompose precipitation changes into two main components rather than three: one is the ‘dynamical’ component, associated with changing upward motions, and the other is the ‘thermodynamical’ component, including changes in atmospheric moisture content. Unlike the Chadwick method, the thermodynamical component is not designed solely to represent the ‘rich get richer’ mechanism. This means the thermodynamical component isn’t just a 7% per K increase; it includes things like the spatial changes in surface temperature. The dynamical component isolates the change in precipitation caused by changes in upward motion.

Monsoon raincloud over a lake in the Tibetan Plateau (photo credit: Janneke Ijmker).

The ‘rich get richer’ rule of thumb becomes increasingly irrelevant at smaller scales. This is frustrating, because these are the scales we really care about! It’s not particularly useful knowing what will happen in a general sense over the whole Tropical region. Farmers want to know what will happen to the seasonal rains on their small piece of land.

Bony also points out that geoengineering schemes which aim to reduce incoming solar radiation to cool the planet’s surface would leave the dynamical component of precipitation change untouched. This is because the dynamical component is caused by the warming of the mid-troposphere by carbon dioxide, and this remains even if we cool the surface. It is an example of the inexact nature of the cancellation between carbon dioxide increases and geoengineering schemes to decrease the amount of carbon dioxide in the atmosphere, and demonstrates that the only way to stop carbon dioxide-driven climate change properly is to stop emitting carbon dioxide.

Bony and Chadwick’s decompositions show how one can glean a lot more information from climate model projections than one would expect from first glance. We have established some general facts about climate change related to the Earth’s energy budget. In that sense we understand quite well what will happen in a warming climate. However, there is still a lot of diversity between model projections, most of which comes from differences in the dynamical response. Local changes in rainfall are related to changes in circulation, and this is the area in which a lot more work needs to be done.

References:

Bony, Sandrine, Gilles Bellon, Daniel Klocke, Steven Sherwood, Solange Fermepin & Sébastien Denvil, 2013: Robust direct effect of carbon dioxide on tropical circulation and regional precipitation, Nat. Geosci., doi:10.1038/ngeo1799

Chadwick, Robin, Ian Boutle & Gill Martin, 2013: Spatial Patterns of Precipitation Change in CMIP5: Why the Rich don’t get Richer in the Tropics. J. Climate, doi: 10.1175/JCLI-D-12-00543.1

Held, Isaac M., Brian J. Soden, 2006: Robust Responses of the Hydrological Cycle to Global Warming. J. Climate, 19, 5686–5699. doi: 10.1175/JCLI3990.1

Knutti, Reto & Jan Sedláček, 2013: Robustness and uncertainties in the new CMIP5 climate model projections, Nat. Clim. Change, doi: 10.1038/nclimate1716

New chapters and foreign lands

By January of this year a major chunk of my PhD work was winding to an end. I had spent a long while looking at the effect of stratospheric aerosol geoengineering on the circulation of the stratosphere, which will form the second (and probably biggest) of the three results chapters in my PhD thesis. At the start of my project I had spent a lot of time reading about stratospheric dynamics and it’s now a somewhat familiar area to me. Well, almost. I still find some chunks counter-intuitive, and sometimes downright baffling. But at least it’s baffling in a familiar way.

We are always reluctant to move away from the familiar. But the work for my final results chapter required that I take the plunge into material and theory that was very much unfamiliar. I was going to look at the impacts of geoengineering on the tropospheric circulation. I eased myself into it by thinking about the tropospheric jet streams first. I at least have some grounding in this area. The dynamics of the midlatitude jet streams is somewhat similar to the dynamics of the stratosphere, and my undergraduate degree in Meteorology has quite a heavy emphasis on the theoretical underpinning of it all. The work on the jet was a nice transition.

Recently (over the past month or so) I have been thinking about precipitation. Especially tropical precipitation. Now, the Earth’s Tropics are meteorologically very different from the midlatitudes. In the midlatitudes the Coriolis Force is a significant effect and weather is determined by large scale wavelike motions producing depressions and anticyclones. Rainfall is mostly frontal in nature. In the Tropics the Coriolis Force is negligible. As a consequence we don’t usually see very large horizontal temperature gradients. This means we don’t see large, rotating weather systems. Rain comes from convective storms, on a much smaller scale than midlatitude frontal depressions. There is so much moisture in the air in the Tropics that the vertical temperature profile pretty much everywhere shows evidence of the release of heat when water vapour condenses to form rain. This forms a characteristic moist adiabatic temperature profile (see image below). Without a strong Coriolis Force this temperature profile is spread over the Tropical belt, so we see it even outside the rain-producing regions.

In order to interpret my model results I had to learn to think differently. Intuitions learned from midlatitude dynamics don’t apply this close to the Equator.

Learning new theory can be pretty intimidating. It’s difficult to know which paper to read first. Sometimes I find myself feeling paralysed. I have a pile of things to read but keep having to refer to different sources to understand terminology, or to get to the bottom of some ‘obvious’ physical understanding not fully explained in one piece of research. Then I took a different tack. I went to see one of the hundreds of other people working in the Department of Meteorology.

This department has experts on any conceivable area, and now, when I’m learning new theory, this is becoming invaluable. In a single hour with a researcher in tropical meteorology I ‘got’ it. I understood the fundamental differences between tropical and midlatitude thinking. Now I can read those papers with confidence. Now I understand the terminology, and a little of the intuition as well. Self-teaching works well (and is entirely necessary for a PhD student) but spending a little time with an expert can help one learn how to teach oneself. This is much the same as, how, when learning a foreign language, you must first learn enough to communicate on a basic level. Once you have that, you can begin to immerse yourself, to learn from conversation with native speakers. The amount of learning that goes on increases exponentially with time. You learn far more from native speakers. But you need to do that initial bit of work to access this higher plane of learning.

Talking of foreign lands, I will soon be off to the 2013 EGU (European Geosciences Union) General Meeting in Vienna. It’s a colossal conference (nearly 12,000 people attended last year) and I’m sure the experience will be educational, entertaining, confusing and exhausting. I’m sure I could list adjectives forever on that one. I will try to write some blog posts and Tweets during the conference, reflecting on what it’s like for a naive young PhD student to be launched into one of the world’s biggest academic conferences.