Can observations of the ocean help predict the weather?

Can observations of the ocean help predict the weather?

by Dr 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.

Figure 1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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

Data Assimilation in the Snow

by Sarah Dance

Snowbird mountains

I’ve just got back from attending the Society of Industrial and Applied Mathematics (SIAM) Conference on Dynamical Systems in the beautiful mountains of Snowbird, Utah,USA.  I was invited to attend the meeting to give part of a Mini-Tutorial on Data Assimilation(available here) with Elaine Spiller and Eric Kostelich.

Even though my undergraduate degree and PhD were in Applied Mathematics, I don’t tend to go to many Mathematics conferences. I often meet with fellow data assimilation practitioners at Meteorology conferences instead.  So it was great to see people proving data assimilation related theorems, applying data assimilation in different applications like neuroscience and cancer treatment, and of course to get some new ideas from dynamical systems approaches that have potential to be applied in different ways.  I particularly enjoyed Mary Silber’s talk on using Landsat data to understand vegetation pattern formation in the drylands of Africa

A slow march through the desert

Gowda/Silber’s work on African drylands. This image shows shrublands in Somalia from high above. Two images – from 1952 (purple) and 2006 (green) – are overlaid here for comparison. The colors highlight the large communities of shrubs and grasses which grow in bands along this sloping landscape. Over the fifty years shown here, all the vegetation has moved uphill – the green bands of modern plant growth are further up the hillside than the purple bands from 1952.

Tales from the Alice Holt Forest: carbon fluxes, data assimilation and fieldwork

by Ewan Pinnington

Forests play an important role in the global carbon cycle, removing large amounts of CO2 from the atmosphere and thus helping to mitigate the effect of human-induced climate change. The state of the global carbon cycle in the IPCC AR5 suggests that the land surface is the most uncertain component of the global carbon cycle. The response of ecosystem carbon uptake to land use change and disturbance (e.g. fire, felling, insect outbreak) is a large component of this uncertainty. Additionally, there is much disagreement on whether forests and terrestrial ecosystems will continue to remove the same proportion of CO2 from the atmosphere under future climate regimes. It is therefore important to improve our understanding of ecosystem carbon cycle processes in the context of a changing climate.

Here we focus on the effect on ecosystem carbon dynamics of disturbance from selective felling (thinning) at the Alice Holt research forest in Hampshire, UK. Thinning is a management practice used to improve ecosystem services or the quality of a final tree crop and is globally widespread. At Alice Holt a program of thinning was carried out in 2014 where one side of the forest was thinned and the other side left unmanaged. During thinning approximately 46% of trees were removed from the area of interest.

Figure 1: At the top of Alice Holt flux tower.

 

Using the technique of eddy-covariance at flux tower sites we can produce direct measurements of the carbon fluxes in a forest ecosystem. T

he flux tower at Alice Holt has been producing measurements since 1999 (Wilkinson et al., 2012), a view from the flux tower is shown in Figure 1. These measurements represent the Net Ecosystem Exchange of CO2 (NEE). The NEE is composed of both photosynthesis and respiration fluxes. The total amount of carbon removed from the atmosphere through photosynthesis is termed the Gross Primary Productivity (GPP). The Total Ecosystem Respiration (TER) is made up of autotrophic respiration (Ra) from plants and heterotrophic respiration (Rh) from soil microbes and other organisms incapable of photosynthesis. We then have, NEE = -GPP + TER, so that a negative NEE value represents removal of carbon from the atmosphere and a positive NEE value represents an input of carbon to the atmosphere. A schematic of these fluxes is shown in Figure 2.                                                               

Figure 2: Fluxes of carbon around a forest ecosystem.

 

The flux tower at Alice Holt is on the boundary between the thinned and unthinned forest. This allows us to partition the NEE observations between the two areas of forest using a flux footprint model (Wilkinson et al., 2016). We also conducted an extensive fieldwork campaign in 2015 to estimate the difference in structure between the thinned and unthinned forest. However, these observations are not enough alone to understand the effect of disturbance. We therefore also use mathematical models describing the carbon balance of our ecosystem, here we use the DALEC2 model of ecosystem carbon balance (Bloom and Williams, 2015). In order to find the best estimate for our system we use the mathematical technique of data assimilation in order to combine all our available observations with our prior model predictions. More infomation on the novel data assimilation techniques developed can be found in Pinnington et al., 2016. These techniques allow us to find two distinct parameter sets for the DALEC2 model corresponding to the thinned and unthinned forest. We can then inspect the model output for both areas of forest and attempt to further understand the effect of selective felling on ecosystem carbon dynamics.

Figure 3: Model predicted cumulative fluxes for 2015 after data assimilatiom. Solid line: NEE, dotted line: TER, dashed line: GPP. Orange: model prediction for thinned forest, blue: model prediction for unthinned forest. Shaded region: model uncertainty after assimilation (± 1 standard deviation).

 

In Figure 3 we show the cumulative fluxes for both the thinned and unthinned forest after disturbance in 2015. We would probably assume that removing 46% of the trees from the thinned section would reduce the amount of carbon uptake in comparison to the unthinned section. However, we can see that both forests removed a total of approximately 425 g C m-2 in 2015, despite the thinned forest having 46% of its trees removed in the previous year. From our best modelled predictions this unchanged carbon uptake is possible due to significant reductions in TER. So, even though the thinned forest has lower GPP, its net carbon uptake is similar to the unthinned forest. Our model suggests that GPP is a main driver for TER, therefore removing a large amount of trees has significantly reduced ecosystem respiration. This result is supported by other ecological studies (Heinemeyer et al., 2012, Högberg et al., 2001, Janssens et al., 2001). This has implications for future predictions of land surface carbon uptake and whether forests will continue to sequester atmospheric CO2 at similar rates, or if they will be limited by increased GPP leading to increased respiration. For more information on this work please see Pinnington et al., 2017.

 

References

Wilkinson, M. et al., 2012: Inter-annual variation of carbon uptake by a plantation oak woodland in south-eastern England. Biogeosciences, 9 (12), 5373–5389.

 

Wilkinson, M., et al., 2016: Effects of management thinning on CO2 exchange by a plantation oak woodland in south-eastern England. Biogeosciences, 13 (8), 2367–2378, doi: 10.5194/bg-13-2367-2016.

 

Bloom, A. A. and M. Williams, 2015: Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological “common sense” in a model data fusion framework. Biogeosciences, 12 (5), 1299–1315, doi: 10.5194/bg-12-1299-2015.

 

Pinnington, E. M., et al., 2016: Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using four-dimensional variational data assimilation. Agricultural and Forest Meteorology, 228229, 299 – 314, doi: http://dx.doi.org/10.1016/j.agrformet.2016.07.006.

 

Pinnington, E. M., et al., 2017: Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions, J. Geophys. Res. Biogeosci., 122, doi:10.1002/2017JG003760.

 

Heinemeyer, A., et al., 2012: Exploring the “overflow tap” theory: linking forest soil co2 fluxes and individual mycorrhizo- sphere components to photosynthesis. Biogeosciences, 9 (1), 79–95.

 

Högberg, P., et al., 2001: Large-scale forest girdling shows that current photosynthesis drives soil respiration. Nature, 411 (6839), 789–792.

 

Janssens, I. A., et al., 2001: Productivity overshadows temperature in determining soil and ecosystem respiration across european forests. Global Change Biology, 7 (3), 269–278, doi: 10.1046/j.1365-2486.2001.00412.x.

2017 Annual European Geosciences Union (EGU) Conference

    by Liz Cooper

The 2017 Annual European Geosciences Union (EGU) conference was held at the International Centre in Vienna from 23rd to 28th April.  During that time over 14,000 scientists from 107 countries shared ideas and results in the form of talks, posters and PICOs .The PICO (Presenting Interactive COntent) format is a relatively new idea for presenting work, where participants prepare an interactive presentation. In each PICO session the presenters first take turns to give a 2 minutes summary of their work for a large audience. The PICOS are then each displayed on an interactive touch screen and conference delegates can chat to the presenters and get further details on the research, with the PICO for illustration. This format has features of both traditional poster and oral presentations and provides a great scope for audience participation. I saw several which took advantage of this, including a very popular flood forecasting adventure game by a fellow Reading Phd student Louise Arnal.

I was delighted to be able to present some of my own recent results at EGU, in a talk titled ‘The effect of domain length and parameter estimation on observation impact in data assimilation for inundation forecasting.’ (see photo)

Presenting at an international conference was a really valuable and enjoyable experience, if a little daunting beforehand. I found it a really useful opportunity to get feedback from experts in the field and find out more about work by people with related interests.

The EGU conference has many participants and covers a huge range of topics from atmospheric and space science to soil science and geomorphology. My research deals with data assimilation for inundation forecasting, so I was most interested in sessions within the Hydrological Sciences and Nonlinear Processes in Science programmes. Even within those disciplines there was a huge breadth of research on display and I saw some really interesting work on synchronization in data assimilation, approaches to detection of floods from satellite data and various methods for measuring and characterizing floods.

As well as subject-specific programmes, there was also a very good Early Career Scientist (ECS) programme at EGU, with networking events, discussion sessions and a dedicated ECS lounge with much appreciated free coffee!

EGU was a hugely enjoyable experience and Vienna is a beautiful city with excellent transport links. With so many parallel sessions it’s really essential to plan which talks and posters are a priority in advance but I would heartily recommend it to anyone involved in geosciences research.

7th Japanese Data Assimilation Workshop

By Joanne A. Waller

For decades data assimilation (DA) has played a crucial role in numerical weather prediction (NWP) where it is used to provide initial conditions for weather forecasts. These ‘initial conditions’ describe the current atmospheric state and are estimated using data assimilation by blending previous forecasts with atmospheric observations, weighted by their respected uncertainties. However data assimilation is not only applicable to NWP and in recent years it has been applied widely to different applications where numerical simulations and observations are available.

At the end of February 2017 over 100 scientists from around the globe arrived at the Japanese RIKEN Advanced Institute for Computational Science (AICS)  for the 7th Japanese Data Assimilation Workshop. The aim of the symposium was to bring together scientist from from numerous different disciplines, such as neuroscience, cardiology, molecular dynamics, cosmology, nanoscale materials science, terrestrial magnetism, paleoclimate, oceanography, atmospheric chemistry and of course NWP, to discuss the data assimilation issues shared  across these broad applications.

Presentations and posters covered a wide variety of topics including: how data assimilation combined with advanced intelligence can help improve numerical models; how high performance computing can be used to deal with the new era of ‘Big Data’; how non-Gaussianity and non-linearity can be handled in data assimilation; ideas on how assimilate data into multi component models (i.e. systems that connect multiple models such as atmospheric, land and ocean models) and many more.

The conference provided a perfect platform for many cross-disciplinary discussions and this highlighted that much can be learnt in general about data assimilation by considering the issues that arise across different scientific areas.

(Photo from http://www.data-assimilation.riken.jp/risda2017/)