Soil Moisture retrieval from satellite SAR imagery

By Keith Morrison

Soil moisture retrieval from satellite synthetic aperture radar (SAR) imagery uses the knowledge that the signal reflected from a soil is related to its dielectric properties. For a given soil type, variations in dielectric are controlled solely by moisture content changes. Thus, a backscatter value at a pixel can be inverted via scattering models to obtain surface moisture. However, this retrieval is complicated by the additional sensitivity of the backscatter to surface roughness and overlying vegetation biomass.

For the simplest cases of bare or lightly vegetated soils, extraction of accurate soil moisture information relies on an accurate model representation of the relative contributions of soil moisture and surface roughness. Models to invert backscatter into soil moisture can be broadly categorised into physical, empirical, or semi-empirical. Empirical models have used experimental results to derive explicit relationships between the radar backscattering and moisture. However, these models tend to be site-specific, only being applicable to situations where radar parameters and soil conditions are close to those used in the initial model derivation. Semi-empirical models start with a theoretical description of the scene, and then use simulated or experimental data to direct the implementation of the model. Such models are useful as they provide relatively simple relationships between surface properties and radar observables that capture a lot of the physics of the radar-soil interaction. The key advantages of such models are that they are much less site dependent in comparison to empirical models, and can also be applied when little or no information about the surface roughness is available. Theoretical, or physical, models are based on a robust description of the mathematics of the radar-soil interaction, providing backscatter through a rigorous inversion. Their generality means they are applicable to a wide range of site conditions and sensor characteristics. However, in practice, because the models require the input of a large number of variables it makes their parameterisation complex, and consequently their implementation difficult. As such, semi-empirical models have generally been the most favoured.

The approaches outlined above only use the incoherent component – backscatter intensity – to characterise the soil moisture, discarding potentially useful information contained in the phase. Recently, however, a causal link between soil moisture and interferometric phase has been demonstrated, and the development of phase-derived soil products will see increasing attention. The figure below shows the first demonstration of phase-retrieved soil moisture, applied across agricultural fields (De Zan et al, 2014). Here, the differential phase (in degrees) between two SAR images clearly shows delineation along field boundaries, associated with differing moisture states.

Reference

De Zan, F., et. al., 2014. IEEE Transactions on Geoscience and Remote Sensing, 52, 418–425

Posted in Climate, earth observation, Hydrology, land use, Measurements and instrumentation, Numerical modelling, Remote sensing | Tagged | Leave a comment

Can observations of the ocean help predict the weather?

By Amos Lawless

It has long been recognized that there are strong interactions between the atmosphere and the ocean. For example, the sea surface temperature affects what happens in the lower boundary of the atmosphere, while heat, momentum and moisture fluxes from the atmosphere help determine the ocean state. Such two-way interactions are made use of in forecasting on seasonal or climate time scales, with computational simulations of the coupled atmosphere-ocean system being routinely used. More recently operational forecasting centres have started to move towards representing the coupled system on shorter time scales, with the idea that even for a weather forecast of a few hours or days ahead, knowledge of the ocean can provide useful information.

A big challenge in performing coupled atmosphere-ocean simulations on short time scales is to determine the current state of both the atmosphere and ocean from which to make a forecast. In standard atmospheric or oceanic prediction the current state is determined by combining observations (for example, from satellites) with computational simulations, using techniques known as data assimilation. Data assimilation aims to produce the optimal combination of the available information, taking into account the statistics of the errors in the data and the physics of the problem. This is a well-established science in forecasting for the atmosphere or ocean separately, but determining the coupled atmospheric and oceanic states together is more difficult. In particular, the atmosphere and ocean evolve on very different space and time scales, which is not very well handled by current methods of data assimilation. Furthermore, it is important that the estimated atmospheric and oceanic states are consistent with each other, otherwise unrealistic features may appear in the forecast at the air-sea boundary (a phenomenon known as initialization shock).

However, testing new methods of data assimilation on simulations of the full atmosphere-ocean system is non-trivial, since each simulation uses a lot of computational resources. In recent projects sponsored by the European Space Agency and the Natural Environment Research Council we have developed an idealised system on which to develop new ideas. Our system consists of just one single column of the atmosphere (based on the system used at the European Centre for Medium-range Weather Forecasts, ECMWF) coupled to a single column of the ocean, as illustrated in Figure 1.  Using this system we have been able to compare current data assimilation methods with new, intermediate methods currently being developed at ECMWF and the Met Office, as well as with more advanced methods that are not yet technically possible to implement in the operational systems. Results indicate that even with the intermediate methods it is possible to gain useful information about the atmospheric state from observations of the ocean. However, there is potentially more benefit to be gained in moving towards advanced data assimilation methods over the coming years. We can certainly expect that in years to come observations of the ocean will provide valuable information for our daily weather forecasts.

References

Smith, P.J., Fowler, A.M. and Lawless, A.S., 2015. Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model. Tellus A, 67, 27025, http://dx.doi.org/10.3402/tellusa.v67.27025.

Fowler, A.M. and Lawless, A.S., 2016. An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error. Monthly Weather Review, 144, 4007-4030, https://doi.org/10.1175/MWR-D-15-0420.1

Posted in Boundary layer, data assimilation, Numerical modelling, Oceans, Weather forecasting | Leave a comment

It melts from the top too …

By David Ferreira

The global sea level rises at about 3 mm/year. Oceans absorb nearly 90% of the heat trapped in the atmosphere by anthropogenic gases like carbon dioxide. As water warms, it expands: this effect explains about half of the observed sea level rise. The other half is due to the melting of ice stored over land, that is, glaciers, the Greenland ice sheet and the Antarctic ice sheet.

Although the latter was a relatively small contributor, recent estimates suggest an increased mass loss from Antarctica in the last decade. Up to now, Antarctica was thought to lose most of its mass at the edges.

The Antarctic ice sheet behaves a bit like a pile of dough that slowly collapses under its own weight. The ice spreads over the whole, and then over the oceans as floating ice, known as ice shelves. Ice shelves are usually found at the end of fast ice-streams channeled by mountains (there are hundreds of these around the continents). The ice shelves in contact with the “warm” ocean (~ 2-4 °C) and melt slowly. Occasionally the process is more abrupt, the ice shelves shed icebergs, some of which are many kilometres in size (an iceberg much larger than Greater London is about to break loose from the Larsen ice shelf). On long timescales, the ice loss at the edges is compensated by snow falling on top of the ice sheet. In recent decades, however, the mass loss at the edges has been slightly larger than the gain through snowfall (a transfer of water to the oceans and a contribution to the sea level rise). The leading explanation for this recent imbalance is that the rate at which warm water is brought to the ice shelves has increased, possibly because of a strengthening of the winds that drive the ocean currents.

A recent paper brings a new element into the picture: the Antarctic ice sheet does not only melt at the edges but also from the top (Kingslake et al., 2017). The surface melt process was thought to be exclusive to Greenland as Antarctica is too cold, even in summer, for temperature to rise above 0°C. So, how is this happening? Melt water in Antarctica seems to originate next to blue ice or exposed rocks. Within the white world of Antarctica, blue ice and rocks are dark. That is, they absorb more sunlight than snow and could (locally) create the conditions for melting. The melt water then gathers into elongated ponds that can grow by kilometres within weeks. Kingslake et al. have documented this process for hundreds of ice streams around Antarctica, sometimes deep into the continent, highlighting a much more widespread phenomenon than previously thought.

What are the possible consequences? These ponds can accelerate the mass loss to the ocean. For example, if they form over land, they may flush to the base of the ice sheet, “lubricate” the ice-ground interface and speed up the ice flow to the coast. If the ponds form over the ice shelves, the added pressure due to the weight of liquid water can help fracture the ice shelves and create icebergs.

Then, the natural question is whether the Antarctic ice shelf is more susceptible to rising temperatures than we think. Unlike the melting at the edge which involves indirect mechanisms through changing winds and ocean currents, surface melting could be directly influenced by increasing temperatures. How important could that be in terms of sea level rise? This remains to be quantified as modern ice sheet models do not take this effect into account, or at least underestimate it.

Reference

Kingslake et al, 2017: doi: 10.1038/nature22049

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Reducing climate change from aviation: could climate-friendly routing play a part?

By Emma Irvine

It’s commonly known that burning fossil fuels, like in jet engines, leads to the emission of carbon dioxide (CO2) which causes global warming. It is perhaps less well known that, particularly in the case of aviation, carbon dioxide is not the only (nor necessarily the smallest) problem. When it comes to determining its climate impact, the aviation sector is complicated, with an entire range of ‘non-CO2’ impacts to consider. Burning jet fuel also releases water vapour, another greenhouse gas, as well as oxides of nitrogen which leads to changes in ozone and methane, two other greenhouse gases. In addition, aircraft cruising at high altitude through very cold and moist air form long-lasting contrails. These long-lived contrails have potentially as large a climate impact as that from aviation CO2 emissions (Reference 1).

Figure 1.  Long-lived contrail cirrus criss-crossing the morning sky over Reading.

How can we continue to fly, but with a smaller impact on climate? It is encouraging to note that not only are there many possibilities being investigated, but some are already being introduced. There is gradual technological development which brings, for example, more fuel-efficient aircraft engines. There is development of cleaner aviation fuels which are not petroleum-based (although at present the alternative fuels certified for use have to be blended 50:50 in combination with the traditional kerosene). Improvements to the way air traffic is managed may also play a role. It is in this latter category that climate-optimised aircraft routing falls.

Traditionally, aircraft routes are optimised by factors such as operating cost, fuel use and flight time.  A new idea, investigated under the European project REACT4C (Reference 2), was to find the optimal route which minimised the impact of that flight on climate, where the climate impact included not only the CO2 emissions (i.e. fuel burn) but also the non-CO2 impacts. This approach, whilst having the advantage of not requiring any developments to aircraft technology or different air traffic control procedures, is made non-trivial by the non-CO2 impacts. Just the detailed computations of the impact of emissions from one day of trans-Atlantic flights required huge computational effort, since some of the emissions have a long lifetime and their impact depends on not just where they are emitted but where the emissions are transported to and the chemical processes they undergo during their lifetime. 

An additional complexity of the project, which was also one of its greatest strengths, was the involvement of scientists and engineers across a range of disciplines, from atmospheric and climate modelling to aeronautical engineers and air traffic control experts from six different countries. Establishing a common scientific language across disciplines can sometimes be a challenge – for example meteorologists are very attached to pressure as an indicator of altitude, whilst engineers and air traffic control specialists use flight levels in thousands of feet (which despite their units are really pressure levels in disguise).  However in the end the project benefits from the individual expertise of each of its members, and ensured greater applicability of the results to the aviation industry.   

The study simulated the full set of trans-Atlantic flights on each of 5 days with different weather conditions (in terms of their upper-level winds). By varying the flight path of each flight, we were able to find safe combinations of flights through north Atlantic airspace which, if their total climate impact was calculated, had a smaller climate impact than the set of flights with smallest economic cost. One of the headline results from the project was that it should be possible to achieve a 10% reduction in total aviation climate impact with a 1% increase in economic cost (Reference 2). Any cost increase may be unpalatable to an industry run on tight margins, but the study also showed that this cost increase could be compensated for by incentivising climate-optimised routing through market-based measures. Our current research, under the umbrella of the Single European Sky Air Traffic Management Research (SESAR) (Reference 3), seeks to apply this idea to the more congested airspace over Europe.

References

  1. Burkhardt and Kärcher 2011: http://www.nature.com/nclimate/journal/v1/n1/full/nclimate1068.html
  2. Grewe et al. 2017: http://iopscience.iop.org/article/10.1088/1748-9326/aa5ba0
  3. https://www.sesarju.eu/
Posted in Atmospheric chemistry, aviation, Climate, Weather, Weather forecasting | Tagged | Leave a comment

Why has there been a rapid increase in heat-related extremes in Western Europe since the mid-1990s?

By Buwen Dong

In the last few decades, Europe has warmed not only faster than the global average, but also faster than expected from anthropogenic greenhouse gas increases (van Oldenborgh et al., 2009). With the warming, Europe experienced record-breaking heat waves and extreme temperatures, such as the 2003 European heatwave, 2010 Russian heatwave, and 2015 European heatwave, which imposed disastrous impacts on individuals and society.

Illustrated in Figure 1 are time series of the area averaged summer (June to August, JJA) surface air temperature (SAT), and summer or annual temperature extreme anomalies over Western Europe (35oN-70oN, 10oW-40oE, land only) relative to the climatology over the time series expanding period. One of most important features is the abrupt surface warming since the mid-1990s and rapid increases in temperature extremes. The changes in SAT and temperature extremes during the recent 16 years (1996-2011) relative to the early period 1964-1993 are more than 1.0 degrees Celsius (degC).

Figure 1. Time series of summer (JJA) or annual mean anomalies relative to the climatology (mean of the whole period) averaged over Western Europe (35oN-70oN, 10oW-40oE). (a) Surface Air Temperature (SAT, degrees Celsius),Tmax, Tmin, and Daily Temperature Range (DTR, degC), (b) annual hottest day temperature (TXx) and warmest night temperature (TNx) (degC), TXx and TNx based on two data sets of HadEX2 and E-OBS are shown. Black and red range bars indicate the earlier period of 1964–1993 and the recent period of 1996–2011.

What has caused the rapid summer warming and increases in high temperature extremes over Western Europe? Relative to an early period of 1964-93, sea surface temperatures (SSTs) have warmed, particularly in the North Atlantic and Indian Oceans, and sea ice extent (SIE) has decreased. Due to air quality legislation, anthropogenic aerosol (AAer) precursor emissions in Europe and North America have decreased since the 1980s and greenhouse gas (GHG) concentrations have increased. In order to understand the relative importance of these forcing factors on the rapid Western European summer warming and increases in hot temperature extremes, numerical experiments with the atmospheric component of a state of the art global climate model have been performed in a study by Dong et al (2016).

Some area averaged summer or annual changes in temperature extreme indices over Western Europe between two periods for both observations and model simulations are illustrated in Figure 2. There is good agreement between the model forced by changes in all forcings and observed changes in summer seasonal mean SAT, Tmax, and Tmin. In response to changes in all forcings, the model simulates an area-averaged summer mean SAT change of 1.16 ± 0.21 degC over Western Europe, which is very close to observed change of 0.93 degC. The changes in SST/SIE explain 62.2 ± 13.0% of the area-averaged SAT signal, with the 37.8 ± 13.6% explained by the direct impact of changes in GHGs and AAer. Both changes in SST/SIE and AAer lead to an increase in Tmax, while the increase in Tmin is predominantly due to the change in SST/SIE. The direct impact of AAer changes act to increase Daily Temperature Range (DTR), but change in DTR is countered by direct impact of GHG forcing. However, DTR change in response to all forcings is overestimated by the model. Results also suggest that the direct impact of AAer changes plays an important role in the increase in the annual hottest day temperature (TXx) (explaining 45.5 ± 17.6% of the signal in the response to changes in all forcings) while the increase in the annual warmest night temperature (TNx) is mainly mediated through the warming of the ocean.

Figure 2: Observed and model simulated summer seasonal mean (JJA) changes between two periods for SAT, Tmax, Tmin, and DTR, the annual changes in annual hottest day (TXx) and warmest night (TNx) temperature, averaged over Western Europe. SAT, Tmax, Tmin, DTR, TXx, and TNx are in degC. (a) Observed changes (based on CRUTS3.2 and HadEX2) data sets, and simulated responses to changes in SST/SIE, GHG concentrations, and AAer precursor emissions. The coloured bars indicated the central estimates and the whiskers show the 90% confidence intervals based on a two tailed Student t-test. (b) Model simulated changes in response to different forcings. SST & SIE is the response to changes in SST/SIE. GHG is the response to GHG concentrations, and Aerosols is the response to changes in AAer precursor emissions.

Whilst each forcing factor causes summer mean surface warming and associated temperature extreme changes over Western Europe, the physical processes are distinct in each case. For example, SST/SIE changes lead to more or less uniform summer mean warming at the surface. In contrast, changes in AAer lead to a band of surface warming and temperature extreme changes in latitude of 40oN-55oN. The results in this study illustrate the important role of the direct impact of changes in AAer not only on summer mean temperature but also on temperature extremes. Reduction of AAer precursor emissions not only induces increased downward solar radiation through aerosol-radiation and aerosol-cloud interactions, but also induces local positive feedbacks between surface warming and reduced cloud cover, reduced precipitation, soil moisture, and evaporation.

Looking forward in the next few decades, greenhouse gas concentrations will continue to rise and anthropogenic aerosol precursor emissions over Europe and North America will continue to decline. Our results suggest that the changes in seasonal mean SAT and temperature extremes over Western Europe since the mid-1990s are most likely to be sustained or amplified in the near term, unless other factors intervene.

References

Dong, B.-W., R. T. Sutton, and L. Shaffrey, 2016: Understanding the rapid summer warming and changes in temperature extremes since the mid-1990s over Western Europe. Clim. Dyn. doi:10.1007/s00382-016-3158-8

van Oldenborgh GJ, Drijfhout S, van Ulden A, Haarsma R, Sterl A, Severijns C, Hazeleger W, Dijkstra H, 2009: Western Europe is warming much faster than expected. Clim Past 5(1):1–12

Posted in Aerosols, Atmospheric chemistry, Climate, Climate change, Climate modelling, Environmental hazards, Numerical modelling | Leave a comment

The physics behind a physics scheme

By Alan Grant

When I joined the Met Office (or, as it was then, The Meteorological Office), I was posted to the boundary layer group. I spent a number of years investigating the atmospheric boundary layer, using data from aircraft and tethered balloons. The justification for the work was to increase our understanding of the boundary layer, which would hopefully lead to improvements in the parametrization of the boundary layer in forecast models. Fast forwarding to the present, I now work on the boundary layer that forms below the surface of the ocean, using high resolution large eddy models, instead of autonomous underwater vehicles (AUVs) and gliders.  The aim of the work remains the same, to develop better parametrizations.

Figure 1. The sea surface in a North Pacific Storm. Photo credit – NOAA

A simple approach to parametrizing the ocean boundary layer is to use parametrizations developed for the cloud-free, atmosphere boundary layer, but upside down (making appropriate changes to account for different densities and heat capacities of air and water). This is a reasonable strategy, but it turns out that there is more to the ocean boundary layer than this, and unsurprisingly the source of the difference between the oceanic and atmospheric boundary layers is the boundary condition.

The possible effects of the surface waves, one of the more striking features of the ocean surface, is an obvious difference between the oceanic and atmospheric boundary layers. Breaking waves, and the interaction between turbulent currents and the Stokes drift of the surface waves (a Lagrangian drift which arises from the non-linearity of the Navier-Stokes equations) has dramatic effect on the properties of the turbulence in the boundary layer.

A more fundamental difference between the oceanic and atmospheric boundary layers is the effect that the surface stress has on the boundary layer flow. In the atmospheric boundary layer momentum is transferred to the surface, so that the surface exerts a drag on the atmosphere. Along with the transport of momentum to the surface, there is also a transport of the mean kinetic energy of the flow (not to be confused with turbulent kinetic energy) from the outer region of the boundary layer towards the surface. This flux of mean kinetic energy maintains the flow near the surface, and supplies the energy needed for the large dissipation rates that occur in the surface layer. The ultimate source of this kinetic energy flux is the work done by the pressure gradient.

In the oceanic boundary layer, the momentum transferred from the atmosphere to the surface acts to generate the mean current. Along with the transfer of momentum into the ocean there is, again, a transfer of mean kinetic energy, but now it is directed away from the surface into the ocean.  This energy flux supports the generation of turbulence at the base of the well-mixed portion of the boundary layer, and turbulent mixing in the stratified layer below. The turbulent mixing in the stratified layer is an important feature of the upper ocean, but is poorly represented in current parametrizations.

To improve parametrizations of the mixing in the stratified layer we need to understand in more detail the process outlined above, and large-eddy simulation can be used to make detailed studies of this and other processes. By understanding the fundamental physics that lies behind the physics scheme we can hopefully improve the parametrization of the surface boundary layer in ocean models.

Posted in Boundary layer, Environmental physics, Numerical modelling, Oceans, Waves | Leave a comment

Changing wet and dry seasons

By Richard Allan

The fickle nature of weather patterns is ultimately responsible for the where and when of tropical rainfall extremes which wreak damage on agriculture, infrastructure and people. Tropical cyclones, such as Enawo which battered Madagascar in March, can severely impact low-lying, highly populated regions through intense rainfall combined with strong winds and storm surges. Explosive thunderstorms operating at smaller spatial scales can generate flash flooding and may lead to devastating landslides in mountainous terrain. A sustained dearth of rainfall or multiple failed seasonal rains, as implicated in drought currently impacting Somalia, Kenya and Ethiopia, are also inextricably linked with evolving weather patterns, often driven by the slower heart-beat of the oceans as they pace out the internal rhythm of El Niño Southern Oscillation and its decadal physiognomies.

Despite the dominant role of chaotic atmospheric and oceanic fluctuations in meting out extremes of weather, there are several controlling factors at the largest terrestrial scales. Barely perceptibly, they nudge the distribution of extremes away from present day patterns through global climatic changes caused by the influence of human activities on the planet’s energy budget. Rising concentrations of greenhouse gases in the atmosphere are slowly but inexorably perturbing the climate system, modifying the ultimate fuel for rainfall extremes: water and energy. While detailed computer simulations capture the fluid, thermodynamic essence of the atmosphere, the processes building up to rainfall extremes, the fine-scale detail of convective storms, must be approximated using a simplified approach but very much rooted in physics. The reliability of future projections out across progressively more distant decades relies on fundamental linkages between the skill in depicting essential physics at the tens to hundreds of kilometre scales and the unrepresented detail which determines impacts. Nevertheless, there is a physical basis for anticipating substantial yet contrasting changes across wet and dry meteorological regimes as the climate warms.

Figure 1: Monthly anomalies in observed tropical total column integrated moisture and surface temperature. Using updated data from Allan et al. (2014) Surv. Geophys.

Figure 1: Seasonal anomalies in observed tropical total column integrated moisture and surface temperature 1988-2016 relative to 1995-2000 climatology using updated data from Allan et al. (2014) Surv. Geophys.

As global temperatures increase, the invisible, gaseous water vapour in the air becomes more abundant, to the tune of 7% for each degree Celsius of tropical warming. This is incontrovertible, observable (see Figure 1) and has huge implications for climate. Water vapour amplifies climate change through the most well-known vicious cycle (warming leads to more atmospheric moisture and a more potent greenhouse effect which feeds back on the warming, amplifying the magnitude of the climate’s response to rising greenhouse gases). This increase in moisture crucially also fuels a greater intensity of rainfall.

When considering the vast areas experiencing the seasonal progression of the tropical rainy belt, the simplest result of a warming planet is the intensification of the wet seasons. The chaotic nature of weather patterns dictates that seasonal rains may fail one year while be supercharged in another depending on the precise position and mood of this rainy belt as it straddles the hemispheres. Yet when conditions are right for a seasonal deluge, it will be more intense in a warmer world due to the more copious quantities of available moisture blown in by the winds.

Things get more complicated when considering the dry zones and dry seasons. Averaged over many years, more atmospheric moisture arrives from over the ocean than is exported from land. If this wasn’t the case, then rivers would have to run in reverse! But for the dry season, a warmer, thirstier atmosphere can more effectively sap the ground of its moisture and temporarily export moisture out of the region. All this points toward an intensification of the wet and dry seasons as the planet warms but this simplistic message is complicated by several factors which include:

  1. The tropical rainy belt alters in precise location from year to year and its position also responds to climate change;
  2. Tropical cloud and winds rapidly adjust to modified heating patterns from greenhouse gases and aerosol particle pollution that ultimately are driving global climate change over longer time-scales, the slow pace of which is determined by the capacity of our vast oceans to take up excess heat;
  3. The effect of these radiative forcings of climate and chaotic weather fluctuations confound detection of wetter wet seasons and drier dry seasons that relate to increasing moisture;
  4. Possible long-term changes in moisture within the soil can feed back on the atmospheric circulation and rainfall characteristics, complicating the picture locally; 
  5. Finally, how arid a location is depends on much more than merely precipitation.

While “dry-gets-drier” does not apply in a simplistic way over land, intriguing signals of amplification in the seasonality of rainfall are apparent over tropical land with chaotic fluctuations superimposed upon longer-term trends (Figure 2). More intense dry seasons have potentially serious consequences for ecosystem productivity. Yet these signals are confounded by dominating variations in weather patterns and by movement in the climatological locations of wet and dry regimes over time. Better defining of the wet seasons is an important step in detecting and monitoring changes important for impacts on societies. The intensification of wet and dry seasons appears a likely but not guaranteed response in a warming climate yet substantial changes in rainfall, increases or decreases, are projected for considerable proportions of tropical land over the 21st century.

Figure 2: Changes in rainfall over tropical land for the wet season (top) and the dry season (bottom) in gauge-based observations (blue, red), an atmosphere-only simulation with prescribed observed sea surface temperature (brown) and fully-coupled climate models (black with shading showing ±1 standard deviation across 15 models). Updated from Liu and Allan (2013) ERL.

Figure 2: Changes in rainfall over tropical land for the wet season (top) and the dry season (bottom) in gauge-based observations (blue, red), an atmosphere-only simulation with prescribed observed sea surface temperature (brown) and fully-coupled climate models (black with shading showing ±1 standard deviation across 15 models). Updated from Liu and Allan (2013) ERL.

The where, when and scale of drought and deluge will continue to centre on the complex evolution of the atmosphere and ocean circulation but weather patterns are already being nudged away from their climatological normal. Limiting impacts requires substantial and sustained cuts in greenhouse gas emissions to tackle the root causes of warming but also crucial is better preparedness through supporting the development of climate resilient societies.

References

Allan et al. (2014) Physically consistent responses of the global atmospheric hydrological cycle in models and observations, Surv. Geophys., doi:10.1007/s10712-012-9213-z.

Berg et al.(2016) Land-atmosphere feedbacks amplify aridity increase over land under global warming, Nature Climate Change, doi:10.1038/nclimate3029

Bony et al. (2013) Robust direct effect of carbon dioxide on tropical circulation and regional precipitation, Nature Geoscience doi:10.1038/ngeo1799

Byrne & O’Gorman (2015) The response of precipitation minus evapotranspiration to climate warming: Why the “wet-get-wetter, dry-get-drier” scaling does not hold over land, J. Climate, doi:10.1175/JCLI-D-15-0369.1

Chadwick et al.(2015) Large rainfall changes consistently projected over substantial areas of tropical land. Nature Climate Change, doi:10.1038/nclimate2805

Chou et al. (2013) increase in the range between wet and dry season precipitation, Nature Geoscience, doi:10.1038/ngeo1744

Dunning et al. (2016) The onset and cessation of seasonal rainfall over Africa, J Geophys. Res., doi:10.1002/2016JD025428

Greve et al. (2014) Global assessment of trends in wetting and drying over land, Nature Geoscience, doi:10.1038/ngeo2247

Hegerl et al. (2015) Challenges in quantifying changes in the global water cycle. Bull. Am. Meteorol. Soc., doi: 10.1175/BAMS-D-13-00212.1

Held & Soden (2006) Robust responses of the hydrological cycle to global warming. J Clim doi: 10.1175/JCLI3990.1

Liu & Allan (2013) Observed and simulated precipitation responses in wet and dry regions 1850-2100, Environ. Res. Lett., doi:10.1088/1748-9326/8/3/034002

Murray-Tortarolo et al. (2016), The dry season intensity as a key driver of NPP trends, Geophys. Res. Lett., doi:10.1002/2016GL068240

Trenberth et al. (2014) Global warming and changes in drought. Nature Climate Change, doi:10.1038/nclimate2067

Posted in Climate, Climate change, Climate modelling, drought, Numerical modelling | Leave a comment

Potential links between Arctic sea ice loss and mid-latitude weather: revisiting an influential earlier study

by Len Shaffrey

The Arctic is changing rapidly due to human emissions of greenhouse gases. Arctic sea ice extent has been declining by 12% per decade since reliable satellite estimates began in 1979. By summer 2012, Arctic sea ice extent had been reduced by nearly half of its value in 1979. The reduction of sea ice has been accompanied by rapid warming, with Arctic surface temperatures increasing at nearly twice the pace of global temperature rise.

The dramatic changes in the Arctic have raised questions about whether the loss of sea ice is having an impact on mid-latitude atmospheric circulation and on the frequency of extreme weather events. One of the first papers to address this question was Francis and Vavrus (2012) (hereafter FV12). FV12 presented observational evidence that Arctic sea ice loss (and in particular the rapid loss of Arctic sea ice loss after Summer 2007) was associated with a weakening of the mid-latitude jet stream over North America and the North Atlantic. The apparent weakening of the jet stream from 1980 to 2010 can be seen in the time series of October-November-December (OND) 500 hPa zonal winds shown in Figure 1 (which is based on Figure 3b in FV12). FV12 went on to argue that the weaker jet stream was associated with large, persistent meanders that resulted in extreme weather events such droughts and severe cold spells.

Figure 1. Time series of OND 500 hPa zonal winds (averaged over 140°W to 0°E and 40°N to 60°N) from 1979 to 2010 (black) and with a 5-year running mean applied (blue). Note that for the running-mean time series the 2 years at the beginning and end of the time series are determined by averaging 3 or 4 years years rather than 5. This figure is based on Figure 3b of Francis and Vavrus (2012). Data from NCEP reanalyses.

FV12 received substantial attention from the scientific community. Subsequent a large number of papers were published that also argued for a link between Arctic sea ice loss and changes in mid-latitude circulation (e.g. Lui et al. 2012; Tang et al. 2013, etc.) or argued that the links weren’t statistically robust (Barnes, 2013; Screen and Simmons, 2013; Barnes et al. 2014, etc.). For more details of the debate, see the review papers of Cohen et al. (2014), Barnes and Screen (2015) and Shepherd (2016).

As five years have passed since the publication of FV12, I thought it might be worth revisiting the paper to see if a few extra years of data might provide some additional insight. Figure 2 extends the time series of 500 hPa seen in Figure 1 to OND 2016. It also extends the time series backwards in time to 1948 to provide some additional context. Figure 2 shows no evidence for a weakening of the OND 500 hPa zonal winds over North America and the North Atlantic in the extended time series between 1948 and 2016. It also suggests that the apparent weakening of the OND 500 hPa zonal winds seen in FV12 was due to the choice of the end date of OND 2010. OND 2010 was characterised by the extremely negative North Atlantic Oscillation (NAO) pattern over the North Atlantic, which resulted in a weakened jet stream. Within a few months, the NAO and the strength of the jet stream would return to normal. In the following year, OND 2011, the NAO was strongly positive and the jet stream was stronger than usual. It seems that the apparent link between Arctic sea ice loss and the mid-latitude jet stream is very sensitive to adding a few extra years of data. It also suggests that the arguments and conclusions of FV12 haven’t stood the test of time.

Figure 2. Time series of OND 500 hPa zonal winds (averaged over 140°W to 0°E and 40°N to 60°N) from 1948 to 2016 (black) and with a 5-year running mean applied (blue). Data from NCEP reanalyses.

References
Barnes, E. A., 2013. Revisiting the evidence linking Arctic amplification to extreme weather in midlatitudes. Geophys. Res. Lett. 40, 1–6.

Barnes, E. A., Dunn-Sigouin, E., Masato, G. & Woollings, T., 2014. Exploring recent trends in Northern Hemisphere blocking. Geophys. Res. Lett. 41, 638–644.

Barnes, Elizabeth A. and James Screen, 2015. The impact of Arctic warming on the midlatitude jetstream: Can it? Has it? Will it?. WIREs Climate Change, 6, doi: 10.1002/wcc.337.

Francis, J. A., and S. J. Vavrus, 2012. Evidence linking Arctic amplification to extreme weather in mid-latitudes, Geophys. Res. Lett., 39, L06801, doi:10.1029/2012GL051000.

Liu, J., Curry, J. A., Wang, H., Song, M. & Horton, R., 2012. Impact of declining Arctic sea ice on winter snow. Proc. Natl Acad. Sci. USA 109, 4074–4079.

Screen, J. A. & Simmonds, I., 2013. Exploring links between Arctic amplification and mid-latitude weather. Geophys. Res. Lett. 40, 959–964.

Shepherd, T.G., 2016. Effects of a warming Arctic. Science, 353 (6303). pp. 989-990. ISSN 1095-9203 doi: 10.1126/science.aag2349

Tang, Q., Zhang, X., Yang, X. & Francis, J. A., 2013. Cold winter extremes in northern continents linked to Arctic sea ice loss. Environ. Res. Lett. 8, 014036.

 

 

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Water vapour transport by tropical cyclones over East Asia

By Liang Guo

When talking about tropical cyclones (TCs), people tend to think about gusty winds and heavy rain. These weather phenomena impress us due to the immense impacts on our surroundings. However, these weather phenomena are short-lived. Most TCs reduce to much weaker weather systems in several days after making landfall.

Therefore, when discussing water vapour transport (WVT) in a larger and longer context, the contribution of TCs is often neglected. Especially over East Asia (EA), where the summer monsoon (EASM) is strong and dominates the WVT. However, according to observations (i.e., International Best Track Archive for Climate Stewardship, IBTrACs), EA and the adjacent north-western Pacific is also the most active TC basin. Therefore, quantitatively measuring WVT by the EASM and TCs will help us understand TCs’ role in long-term large-scale WVT.

Figure 1 shows the mean seasonal cycle (averaged between 1979 and 2012) of WVT by the EASM and TCs via two boundaries which are defined within the figure. The first obvious difference is the peaking period. The WVT peak of TCs is in late summer and early autumn, which is later than that of the EASM. This is because TC genesis is sensitive to the sea surface temperature (SST), and SST is higher in late summer and early autumn. As shown in Figure 1, in most of the time, the WVT by EASM is about one order of magnitude larger than that of TCs. However, with the EASM retreating from EA landmass in August, the contribution from TCs is as important as the EASM, and is even larger in September and October.

Figure 1. Seasonal cycle of monthly mean vertically integrated moisture flux passing through the southern (blue) and eastern (red) boundaries. The mean-flow moisture fluxes are shown as solid lines (using the left-hand vertical axis) and TC eddy moisture fluxes as dashed lines (using the right-hand vertical axis). The inner panel shows the definition of the southern and eastern boundaries. Positive values indicate moisture is transported into the EA landmass, and negative values indicate moisture is transported out of the EA landmass. Note that the scale of the right-hand vertical axis is an order of magnitude smaller than the left-hand vertical axis.  Units: kg/s.

The second difference is the direction of WVT. The WVT by the EASM is imported via the southern boundary and is exported to the Japan and the Korean peninsula via the eastern boundary, while the WVT by TCs is in the reverse direction. The WVT import by TCs via the eastern boundary is mainly due to the westward-moving TCs, which bring warm and moist air from the Pacific. The WVT export by TCs via the southern boundary, on the other hand, is smaller and is linked to the northward moving TCs. The small magnitude is due to the air flow coming from land and is weaker and drier after the northward moving TCs making landfall.

An interesting detail of the WVT via the southern boundary is its sudden sign change in September, due to the reverse of the meridional gradient of the mean specific humidity over the EA landmass. During the EASM (June-August), the maximum of the mean specific humidity is located over south China (about 25°N, therefore, a southward gradient). However, with the retreat of the EASM in late August, the maximum of the mean specific humidity also rapidly retreats to near the equator (becomes a northward gradient).

As we can see, due to the difference in the season cycle compared to EASM, TCs’ role on WVT is non-negligible over EA.

Reference

Guo, L., Klingaman, N. P., Vidale, P., Turner, A. G., Demory, M.-E., & Cobb, A., 2017. Contribution of Tropical Cyclones to Atmospheric Moisture Transport and Rainfall over East Asia. Journal of Climate, DOI: 10.1175/JCLI-D-16-0308.1, in press.

 

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Nice weather, atmospheric blocking and forecasts

By Oscar Martinez-Alvarado

With the beginning of spring (either the ‘meteorological’ spring on 1 March or the ‘astronomical’ spring on 20 March, as the Met Office explains here), the UK and indeed the whole Northern Hemisphere start experiencing warmer weather. The next few days are forecast to see settled weather over the UK with warm temperatures during the day, chilly overnight. This will be in part due to the prevalent large-scale atmospheric conditions, featuring what is called an episode of atmospheric blocking. An atmospheric block is a long-lived, usually more than five days, high-pressure system that remains almost stationary over a particular region. While the high-pressure system is there, other sources of weather variability such as mid-latitude cyclones and storms are deflected either to the north or to the south, so that the block acts as a shield for the blocked region against wet and windy weather. This might sound good, but if the blocking conditions remain for too long, this might lead to heat waves and drought in summer or very cold temperatures in winter.

Even though these blocks depend on the large-scale atmospheric conditions, which are generally easier to forecast than conditions at a particular location, forecasting the time when a blocking episode will start or finish is notoriously difficult. Figure 1 is an example of a blocking episode over Scandinavia, observed during the field campaign of the North Atlantic Waveguide and Downstream Impacts Experiment (NAWDEX, see also the blog entry on 31 October 2016). The blocking episode properly began on 4 October 2016 and remained there at least until 17 October. Two blobs representing atmospheric blocking on 4 October 2016, one over Greenland and a second one over Scandinavia, can be seen in Figure 1.Figure 1. Atmospheric blocking over the North Atlantic and Europe on 4 October 2016.

Figure 2 represents the same day, but as it appeared in the forecast six days earlier (i.e. the forecast issued on 28 September 2016). The colour shading represents the probability of having a block. White represents zero probability of a block; light yellow represents a very low probability of a block; dark red represents a very high probability of a having a block. The shading over Scandinavia indicated that a blocking over that region was almost equally likely to happen or not happen. Furthermore, the forecast is not at all confident about the occurrence of blocking over Greenland. In addition to these two locations, there was a very small but non-zero chance of blocking over Spain and a few other spots over Canada and the North Atlantic.

Figure 2. Atmospheric blocking over the North Atlantic and Europe on 4 October 2016 as forecast six days before on 28 September 2016.

Researchers at the Department of Meteorology and as part of the NAWDEX project are investigating the causes for the low blocking forecast skill in forecasts beyond five days and whether it is possible to improve it. It very well may be that this aspect of the atmosphere is actually unpredictable at those time scales, or it might be that we can improve our numerical models better to represent this phenomenon. Being able to predict blocking with confidence more days in advance would bring benefits such as planning ahead for the extreme weather conditions (e.g. heat waves, cold spells and drought) that might arise as a result. This makes the endeavour of improving blocking forecasts worth pursuing.

At the time of writing this blog entry, the 5-day forecast indicated that the current block over the UK should end on Monday 10 April, while a new strong block was forecast to be located over the North Atlantic. I am curious to know whether the forecast was correct or not.

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