The Earth’s forest, covering almost a third of the land surface, plays a vital role in our weather and climate. Around us, trees are continually exchanging water, carbon, and energy with the atmosphere. When we include trees in UKESM, we have to consider how they might affect rainfall, cloud formation, chemistry and global temperature rise. But the world’s forests are sensitive to global environmental change – some driving an expansion of forests, some impeding their ability to help regulate their local services and global climate. Getting our trees as close to the real world as possible is therefore critical to the performance of UKESM.

Join us on a deep dive into the Earth System Model world of virtual forests, as we explore how climate modellers are simulating and evaluating tree cover in UKESM. We expand on the themes running through our National Tree Week interview, published by the National Centre for Atmospheric Science, such as how we ‘grow’ virtual forests, and how we can improve tree cover simulation.

Why trees are essential to climate projections

Forests have far-reaching effects on our climate – some local but also throughout the Earth System.  Locally, forests often darken the Earth’s surface albedo (the planet’s ability to reflect sunlight). In some areas, forestation activities could have a warming effect on our climate (Betts, 2000), while in colder regions, where warming conditions allow trees to creep into areas that would have been previously too cold, more dark vegetation sticks out above the reflective snow in the winter, further accelerating local warming (Turton, 2017).

Trees also act as fountains (Pearce, 2019), pulling moisture from the ground and releasing it as water vapour through tiny pores in their leaves, known as stomata. They also trap rainwater on their leaves and stems before it reaches the ground and recycle it back into the atmosphere. This evaporation of water can be up to half of the total rainfall, and the combined transpired and intercepted water form clouds and rain above and downstream of the forests. As well as this rainfall-recycling activity, the forests act to pull more rainfall from the atmosphere as their bulkiness slows the air down, causing convergence, or squeezing, of the air-water body. In fact it is considered that the rainforests would not exist without the increased rainfall due to the convergence and the rainfall recycling. Forests also act as carbon sinks, holding an estimated ~300 Pg carbon (Köhl et al., 2015). As plants also obtain carbon dioxide for photosynthesis through stomata openings, increased CO2 in the air allows trees to either increase growth or survive better in dry conditions by closing their stomata and reducing transpiration while still assimilating the same carbon levels. Combined with expanding tree lines in cold climates, trees have removed up to a quarter of the carbon dioxide emitted by humans (Friedlingstein et al., 2020), locking it in their wood for decades or centuries. There are, however,  signs that increased uptake and forest expansion might be becoming less effective at absorbing carbon (Brienen et al., 2015; Wang et al., 2020). Hotter temperatures are putting the microbes that release woodland carbon back into the atmosphere into overdrive and making water or heat stress that trees experience worse in warmer climates (Duffy et al., 2021). Changing rainfall patterns or intensity reduces the amount of water available for forests to grow in some places. While changing climate is also leading to more forest fires (Kelley et al., 2019a), releasing more carbon (Burton et al., submitted) and potentially more disease and pest spread. And on top of all of this, deforestation, particularly in South America and Africa, has driven a decline in global forest carbon stocks for the last couple of decades (Köhl et al., 2015). Many of the most advanced Earth System Models try to capture all these interactions so that they can work out how forests might change in the future, and what role these changes play at controlling or accelerating climate change.

Figure 1: Global tree (left) and grass (right) cover, taken from VCF observations (DiMiceli et al., 2017) (top), gridded as per (Adzhar et al., submitted); tree cover simulated by JULES-ES (middle); UKESM (Sellar et al., 2019) (bottom).

How does UKESM grow forests?

A lot of today’s climate models represent many of the tree’s essential interactions with climate, including the effects they have on the land albedo, water exchanges with the atmosphere and in some cases, with rivers and river flow. Some climate simulations even consider the amount of carbon taken up from photosynthesis. “Physical only” models, such as the Met Office’s latest HADGEM3-CG3.1 (Williams et al., 2018), prescribe what kind of vegetation grows where and where other land-cover types are (e.g. cities) using observations of today’s land surface, making the simulation of today’s climate much more accurate and stable. But not allowing the land cover to change in the future means the model doesn’t capture changes to many of these important feedback mechanisms  – which can have an enormous impact under some of the most extreme future emissions and climate change scenarios. So in UKESM, we simulate changes in vegetation cover of the land surface over time.

Rather than try to represent the millions of plant and tree species, UKESM instead simulates changes in a small number of plant “types”. We have 9 natural types, which includes 5 different trees (Harper et al., 2016). They are split by leaf type, either broadleaf which include trees like the big leaved sycamores, or needleleaf such as fir trees; whether they retain their leaves all year round, or if they lose them, whether it is in the winter or dry season; and if they are found in the tropics, temperate regions or in colder climates. Within each grid cell the entire population of a plant type behaves in the same way. For example, in a single grid cell, all broadleaf evergreen trees will grow together and shrink together (Clark et al., 2011). These different plant types can grow, compete against each other for light and water, and are killed by events like droughts and fires. We also simulate deforestation, with farming competing for space with forests of much of the world (Burton et al., 2019; Robertson, 2019). The action of the trees to recycle the water back to the atmosphere is modelled through incorporating equations that describe the impact of wind, sun, temperature and humidity on the drawing up of the water through the tree trunks, the leaves and the wet leaves (Clark et al., 2011). 

How we evaluate tree cover in UKESM

Given how important trees are in the Earth System, getting tree cover right is vital for any Earth System model. We, therefore, spend a lot of our time comparing our simulated forests to observations to check performance is good enough and to target new development. There are three causes of mismatch between UKESM and observations: 1) How we simulate trees; 2) how we simulate the climate that trees rely on; 3) biases in the observations themselves.  We can work out if a mismatch is caused by climate by also comparing JULES – the underlying land surface scheme – to observations when it’s driven with real-world climate. For example, UKESM does get forests in the right places, and removal and release of carbon from the atmosphere by forests are similar to what we see in the real world. But it is the transitional areas – the vegetation types between forest and grassland such as tropical savannas with sparse but significant tree cover, where UKESM shows room for improvement. At the wet end, there is often too much tree cover in UKESM, while deserts extend too far at the dry end (Fig. 1).  When we drive JULES with observed climate, deserts and grasslands have similar extents to observations, but forests still grow too far into savannas. This shows the lack of savannas is because of too little moisture input from the climate model in semi-arid areas, and some missing or misrepresented process that limits the spread of JULES forests. 

Or it could be an underestimation of savanna tree cover in observations. Modellers normally evaluate ESMs against observations derived from Earth observation (EO) products – normally from satellite data which is a faster, more cost-efficient way of measuring trees than labour-intensive fieldwork on the ground. EO tree cover is also used in place of field data to measure key conservation indicators such as biomass and carbon stocks. However, while EO products have a good track record in estimating tree cover in densely-forested areas, some of the UKESM teams research shows that EO products have an inherent tendency to underestimate woody cover in non-forest (see Fig. 2) (Adzhar et al., submitted). Underestimating tree cover leads to lower derived carbon stock and biomass estimates, making complex, biodiverse savannas seem barren and unproductive when viewed via EO. While the underestimation in EO cover probably doesn’t explain all of the extra forest cover found in JULES, future validation and calibration of the data using field data will help us to avoid growing trees in the model in areas where they shouldn’t be.

Figure 2: Tropical areas where the VCF tree cover product (an example EO product) might not get tree cover quite right. Blue is areas where VCF likely underestimates tree cover, red is areas where it likely overestimates. The darker the shade, the more likely the under or overestimation. Reproduced from (Adzhar et al., submitted)

In the next UKESM newsletter we’ll explain more about improving our simulation of tree cover.

References

1. Adzhar, R., Kelley, D. I. and Gerard, F.: Reassessing MODIS Vegetation Continuous Fields tree cover performance in tropical forests and savannas, Biogeosciences, submitted.
2. Betts, R. A.: Offset of the potential carbon sink from boreal forestation by decreases in surface albedo, Nature, 408(6809), 187–190, 2000.
3. Brienen, R. J. W., et al: Long-term decline of the Amazon carbon sink, Nature, 519(7543), 344–348, 2015.
4. Burton, C., Kelley, D., Jones, C., Betts, R., Cardoso, M. and Anderson, A.: South American fires and their impacts on ecosystems increase with continued emissions, Climate Resilience and Sustainability, submitted.
5. Burton, C., Betts, R., Cardoso, M., Feldpausch, T. R., Harper, A., Jones, C. D., Kelley, D. I., Robertson, E. and Wiltshire, A.: Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES), Geoscientific Model Development, 12(1), 179–193, 2019.
6. Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C. and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geoscientific Model Development, 4(3), 701–722, 2011.
7. DiMiceli, C. M., Carroll, M. L., Sohlberg, R. A., Huang, C., Hansen, M. C. and Townshend, J. R. G.: Annual global automated MODIS vegetation continuous fields (MOD44B) at 250 m spatial resolution for data years beginning day 65, 2000? 2010, collection 5 percent tree cover, University of Maryland, College Park, MD, USA [online] Available from: https://wedocs.unep.org/handle/20.500.11822/18494, 2017.
8. Duffy, K. A., Schwalm, C. R., Arcus, V. L., Koch, G. W., Liang, L. L. and Schipper, L. A.: How close are we to the temperature tipping point of the terrestrial biosphere?, Science, 7(eaay1052) [online] Available from: https://advances.sciencemag.org/content/7/3/eaay1052.abstract, 2021.
9. Friedlingstein, P., et al: Global carbon budget 2020, Earth Syst. Sci. Data, 12(4), 3269–3340, 2020.
10. Harper, A. B., et al: Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information, Geoscientific Model Development, 9(7), 2415–2440, doi:5194/gmd-9-2415-2016, 2016.
11. Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews, T. R. and Dong, N.: How contemporary bioclimatic and human controls change global fire regimes, Nat. Clim. Chang., 9(9), 690–696, 2019a.
12. Köhl, M., Lasco, R., Cifuentes, M., Jonsson, Ö., Korhonen, K. T., Mundhenk, P., de Jesus Navar, J. and Stinson, G.: Changes in forest production, biomass and carbon: Results from the 2015 UN FAO Global Forest Resource Assessment, Forest Ecology and Management, 352, 21–34, doi:1016/j.foreco.2015.05.036, 2015.
13. Pearce, F.: Rivers in the sky: how deforestation is affecting global water cycles, 2019.
14. Robertson, E.: The Local Biophysical Response to Land-Use Change in HadGEM2-ES, J. Clim., 32(22), 7611–7627, 2019.
15. Sellar, A. A., Jones, C. G. and Mulcahy, J. P.: UKESM1: Description and evaluation of the UK Earth System Model, in Modeling Earth … [online] Available from: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001739, 2019.
16. Turton, R. H.: The impact of the radiation balance on snowmelt in a sparse deciduous birch forest, University of Edinburgh. [online] Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.743642 (Accessed 16 January 2021), 2017.
17. Wang, S., et al: Recent global decline of CO2 fertilization effects on vegetation photosynthesis, Science, 370(6522), 1295–1300, 2020.
18. Williams, K. D., et al: The met office global coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations, J. Adv. Model. Earth Syst., 10(2), 357–380, 2018.