The Virtual Trees of UKESM

Improving Our Simulations

Douglas Kelley1, Nathan Betts2, Chantelle Burton3, Rebecca Oliver1, Arthur Argles4, Karina Williams3,4, Chris Jones3, Eleanor Blyth1, Rahayu Adzhar1,5, France Gerard1, Li Guangqi6, Lina Mercado1,4, Doug Clark1

 1 UK Centre for Ecology & Hydrology, Wallingford, 2 National Centre for Atmospheric Science, University of Leeds 3 Met Office, Exeter, 4 University of Exeter, 5 Imperial College London, Berkshire, 6 University of Stirling

In the last issue of the UKESM newsletter we explained why trees are essential in climate projections, how we simulate changes in vegetation cover of the land surface and how we evaluate tree cover to ensure we are getting a good performance of the model.  In this newsletter we look at how to improve our simulation of tree cover.

Improving our simulation of tree cover

To identify which processes need including or improvement, we first need to know what controls the extent of forest cover in the real world, and by how much – another active field of research by the UKESM team (Fig. 1). One of the main stresses that affects tree cover that is not yet represented in UKESM is fire (see last year’s newsletter ). Fire is one of the most challenging terrestrial processes to capture in Earth System Modelling because of its extreme nonlinear responses to underlying drivers (Hantson et al., 2016) and small deviations in the location in the worlds “fire zones” can have a big impact on our simulation of the land surface and the carbon it locks up (Hantson et al., 2020). Getting fire into UKESM started with simulating area that has experienced a fire (burnt area) in JULES from observed climate, including modelling fires started by lightning strikes, and human interactions with landscape such as human ignitions (from stray cigarette butts to agricultural and intentional management burns) and land use, as well as simulated fuel loads and fuel moisture (Mangeon et al., 2016). Next step was getting the impact on vegetation from this simulation of fire right in JULES. Tree mortality isn’t the only impact fire has on forest areas – it also consumes and releases dead plant material and carbon locked up in soils back into the atmosphere (Bowman et al., 2009) – and both feedbacks are in now JULES (Burton et al., 2019). The last step is to get the pattern of burnt area fire-feedback working in UKESM, along with the extra climate biases that simulating climate introduce (Figure 2).

How much tree cover extent would increase if it weren’t for different environmental stresses and human impacts as a % of land area 2001-2013 Figure 1: How much tree cover extent would increase if it weren’t for different environmental stresses and human impacts as a % of land area 2001-2013. Based on a matching patterns of observations of different tree cover controls against VCF observations of tree cover. Additional controls not shown are: “Burnt Area” is the area affected by fire; “Rainfall Distribution” is how evenly rainfall is distributed throughout the period, represented by mean no. annual wet days; “Temperature Stress” uses mean maximum daily temperature of the warmest month. “Population Density” based on number of people living in a particular area. Urban, crop and pasture is the area taken up by respective human land uses. “All Human Impacts” sums up all human variables. Mean annual rainfall & temperature and available short wave radiation Reproduced from (Kelley et al., 2019b), and data sources found and regridded in (Kelley et al., 2019a).


The changes in tree cover due to fire in JULES driven run with observed climate Figure 2: The changes in tree cover due to fire in JULES driven run with observed climate (left) looks similar to what we’d expect from the relationship inferred from observations (Fig. 1). But small climate biases in UKESM climate (right) can alter this pattern.

Fig. 1 also illustrates another stress that influences tree cover: water stress (panel B: “rainfall distribution”). When trees don’t have a plentiful supply of water in the soil around their roots, they start to close their stomata, so that they lose less water through them each day. Since the smaller stomatal openings also let in less carbon dioxide, water-stressed trees assimilate less carbon. This can mean that they no longer out-compete the other vegetation types, so tree cover decreases.

In UKESM, trees can access water in soil up to a depth of 3m. In some parts of the world, however, tree roots actually go down much deeper. For example, in the Eastern part of the Amazon rainforest, tree roots have been observed at depths of 18m! (Nepstad et al., 1994) In this area, the soil gets soaked in the wet season, and the trees reach down to use this water in the dry season to keep them going. Because trees in the model can’t reach down as far, this can mean that they get more water stressed than their real-world counterparts (Harper et al., 2020).  

Tree cover is also affected by heat stress in a lot of the tropics (Fig. 1). This is partly because photosynthesis has an “optimum temperature” which usually lies somewhere between 15 and 35oC depending on species and geographic location. Photosynthesis decreases when temperatures exceed this optimum, resulting in less carbon assimilated for trees to grow and expand their cover. In Earth System models this optimum is fixed at one value for a given plant type across the world. However, observational studies suggest that plants can adapt this optimum temperature to make their photosynthesis more efficient in different environments (Kattge and Knorr, 2007; Kumarathunge et al., 2019), and thereby remove more carbon from and transpire more water to the atmosphere. This “thermal acclimation” of photosynthesis is being introduced into JULES (Mercado et al., 2018), whereby optimal temperature adapts to the average ambient temperature of the preceding month. This makes a big difference to Gross Primary Production (the carbon fixed by the photosynthesis), especially in the tropics and northern latitudes in spring (Fig. 3). When we incorporate thermal acclimation into UKESM, we expect to see greater impact under future climate change with potentially larger shifts in temperature.

Latitude bands of Gross Primary Production simulated by JULES-vn5.6 with and without acclimation compared against other models and observations Figure 3: Latitude bands of Gross Primary Production simulated by JULES-vn5.6 with and without acclimation compared against FluxCom (Jung et al., 2020; Tramontana et al., 2016) (black line) and MOD17 (Zhao et al., 2005, 2006; Zhao and Running, 2010) (grey line) “observational” products (Jung et al., 2020; Tramontana et al., 2016). Clz (Collatz et al., 1991) and Fq are different ways to model photosynthesis. Jac (Jacobs, 1994) and Med (Medlyn et al., 2011) are different stomatal conductance models. “Ac” includes acclimation and uses Farquhar photosynthesis and Medlyn stomatal conductance, with “KK” using (Kattge and Knorr, 2007) and “DK” using (Kumarathunge et al., 2019) acclimation parameterisations.

With all the plants in a grid cell looking the same, UKESM misses a lot of the variation in tree sizes that make forests more resilient to change (Moore et al., 2018, 2020) – and might explain why trees in many climate models are sometimes slower to recover after disturbances like deforestation, droughts or fire (Kelley et al., 2014).  A new vegetation model,  “Reduced Ecosystem Demography” (RED), is being introduced into JULES which splits the population of plant types into “mass classes”  (Argles et al., 2020). After a bit of plant maintenance, some of the carbon captured by photosynthesis can be put towards plant growth, which in RED, moves populations of plants into high mass classes. And, once finished, disturbance events such as fire can have different mortality rates in different classes. The addition of size will enable both more nuance and realism when modelling forest responses to drought and fire, and the subsequent regrowth (Fig 4).

An offline simulation of RED for an “Amazon forest-like” Figure 4: Reproduced from (Argles et al., 2020). An offline simulation of RED for an “Amazon forest-like”. (a) and (b) climate starting off with no vegetation cover to demonstrate succession caused by growth and competitive dynamics within RED. Fast-spreading PFTs – such as grasses (C3) and shrubs (ESh) – become dominant and are then slowly replaced by slower growing but more longer-lived trees (BET-Tr). Panels (c-e) are time-slices of the forest size structure across each PFT, plants grow through mass classes represented by a flow of population from left to right.

Because of their historic uptake of carbon, planting trees is often seen as a potential solution to climate change, a part of a remedial approach to climate change known as nature-based solutions (Griscom et al., 2017). However, forests’ ability to take up carbon depends on how they respond to future environmental pressures placed on them, especially from climate change. Which in turn is influenced by how trees affect their local and global environments. Ongoing development and evaluation of the virtual trees in UKESM is therefore an essential part of predicting the future of the Earth System.


Argles, A. P. K., Moore, J. R., Huntingford, C., Wiltshire, A. J., Harper, A. B., Jones, C. D. and Cox, P. M.: Robust Ecosystem Demography (RED version 1.0): a parsimonious approach to modelling vegetation dynamics in Earth system models, Geosci. Model Dev., 13(9), 4067–4089, 2020.

Bowman, D. M. J. S., Balch, J. K., Artaxo, P., Bond, W. J., Carlson, J. M., Cochrane, M. A., D’Antonio, C. M., Defries, R. S., Doyle, J. C., Harrison, S. P., Johnston, F. H., Keeley, J. E., Krawchuk, M. A., Kull, C. A., Marston, J. B., Moritz, M. A., Prentice, I. C., Roos, C. I., Scott, A. C., Swetnam, T. W., van der Werf, G. R. and Pyne, S. J.: Fire in the Earth system, Science, 324(5926), 481–484, 2009.

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.

Collatz, G. J., Ball, J. T., Grivet, C. and Berry, J. A.: Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer, Agric. For. Meteorol., 54(2), 107–136, 1991.

Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L., Leavitt, S. M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F. E., Sanderman, J., Silvius, M., Wollenberg, E. and Fargione, J.: Natural climate solutions, Proc. Natl. Acad. Sci. U. S. A., 114(44), 11645–11650, 2017.

Hantson, S., Arneth, A., Harrison, S. P., Kelley, D. I., Prentice, I. C., Rabin, S. S., Archibald, S., Mouillot, F., Arnold, S. R., Artaxo, P., Bachelet, D., Ciais, P., Forrest, M., Friedlingstein, P., Hickler, T., Kaplan, J. O., Kloster, S., Knorr, W., Laslop, G., Li, F., Melton, J. R., Meyn, A., Sitch, S., Spessa, A., van der Werf, G. R., Voulgarakis, A. and Yue, C.: The status and challenge of global fire modelling, Biogeosciences, 13(11), 3359–3375, 2016.

Hantson, S., Kelley, D. I., Arneth, A., Harrison, S. P., Archibald, S., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., Mangeon, S., Melton, J. R., Nieradzik, L., Rabin, S. S., Colin Prentice, I., Sheehan, T., Sitch, S., Teckentrup, L., Voulgarakis, A. and Yue, C.: Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geoscientific Model Development, 13(7), 3299–3318, doi:10.5194/gmd-13-3299-2020, 2020.

Harper, A. B., Williams, K. E. and McGuire, P. C.: Improvement of modelling plant responses to low soil moisture in JULESvn4. 9 and evaluation against flux tower measurements, Geoscientific Model [online] Available from:, 2020.

Jacobs, C. M. J.: Direct Impact of Atmospheric CO2 Enrichment on Regional Transpiration. Thesis, Department of Meteorology, Wageningen Agricultural University, Landbouwuniversiteit Wageningen., 1994.

Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O’Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U. and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17(5), 1343–1365, 2020.

Kattge, J. and Knorr, W.: Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species, Plant Cell Environ., 30(9), 1176–1190, 2007.

Kelley, D. I., Harrison, S. P. and Prentice, I. C.: Improved simulation of fire–vegetation interactions in the Land surface Processes and eXchanges dynamic global vegetation model (LPX-Mv1), Geoscientific Model Development, 7(5), 2411–2433, 2014.

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.

Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Gerard, F., Ellis, R., Li, G., Marthews, T., Veenendaal, E., Weedon, G., Dong, N. and Lloyd, J.: Trends of burnt area controls and impacts on tropical tree cover, in EGU General Assembly, pp. EGU2019–17591., 2019b.

Kumarathunge, D. P., Medlyn, B. E., Drake, J. E., Tjoelker, M. G., Aspinwall, M. J., Battaglia, M., Cano, F. J., Carter, K. R., Cavaleri, M. A., Cernusak, L. A., Chambers, J. Q., Crous, K. Y., De Kauwe, M. G., Dillaway, D. N., Dreyer, E., Ellsworth, D. S., Ghannoum, O., Han, Q., Hikosaka, K., Jensen, A. M., Kelly, J. W. G., Kruger, E. L., Mercado, L. M., Onoda, Y., Reich, P. B., Rogers, A., Slot, M., Smith, N. G., Tarvainen, L., Tissue, D. T., Togashi, H. F., Tribuzy, E. S., Uddling, J., Vårhammar, A., Wallin, G., Warren, J. M. and Way, D. A.: Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale, New Phytol., 222(2), 768–784, 2019.

Mangeon, S., Voulgarakis, A., Gilham, R., Harper, A., Sitch, S. and Folberth, G.: INFERNO: a fire and emissions scheme for the UK Met Office’s Unified Model, Geoscientific Model Development, 9(8), 2685–2700, 2016.

Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Colin Prentice, I., Barton, C. V. M., Crous, K. Y., de Angelis, P., Freeman, M. and Wingate, L.: Reconciling the optimal and empirical approaches to modelling stomatal conductance, Global Change Biology, 17(6), 2134–2144, doi:10.1111/j.1365-2486.2010.02375.x, 2011.

Mercado, L. M., Medlyn, B. E., Huntingford, C., Oliver, R. J., Clark, D. B., Sitch, S., Zelazowski, P., Kattge, J., Harper, A. B. and Cox, P. M.: Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity, New Phytol., 218(4), 1462–1477, 2018.

Nepstad, D. C., de Carvalho, C. R., Davidson, E. A., Jipp, P. H., Lefebvre, P. A., Negreiros, G. H., da Silva, E. D., Stone, T. A., Trumbore, S. E. and Vieira, S.: The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures, Nature, 372(6507), 666–669, 1994.

Tramontana, G., Jung, M., Camps-Valls, G., Ichii, K., Raduly, B., Reichstein, M., Schwalm, C. R., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S. and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosci. Discuss., 1–33, 2016.

Zhao, M. and Running, S. W.: Drought-induced reduction in global terrestrial net primary production from 2000 through 2009, Science, 329(5994), 940–943, 2010.

Zhao, M., Heinsch, F. A., Nemani, R. R. and Running, S. W.: Improvements of the MODIS terrestrial gross and net primary production global data set, Remote Sens. Environ., 95(2), 164–176, 2005.

Zhao, M., Running, S. W. and Nemani, R. R.: Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses, J. Geophys. Res., 111(G1), doi:10.1029/2004jg000004, 2006.

Download this Newsletter as a PDF (2015KB)