Supplementary information. 1. Model description and experimental design. 1.1 JULES (the Joint UK Land Environment Simulator).

Transcription

1 Supplementary information 1. Model description and experimental design 1.1 JULES (the Joint UK Land Environment Simulator). In order to make inferences about the behaviour of vegetation under conditions of higher temperature and CO 2 concentration, this study used a process-based land-surface model, JULES, including a new dynamic module that enables it to grow sugarcane. JULES (the Joint UK Land Environment Simulator) is based on the Hadley Centre land surface scheme MOSES2. Full descriptions of JULES are provided in Best et al., 2011; Clark et al., JULES simulates the exchange of water, momentum and energy between the soil, land surface, and atmosphere. It is driven by sub-daily (typically 3-hourly) time series of radiation, precipitation, temperature, humidity, wind speed and surface pressure. The soil is divided into layers, with the thermal and hydraulic characteristics defined for all layers. In its standard configuration, JULES divides the land-surface into nine surface types: broadleaf trees, needle leaf trees, C3 (temperate) grass, C4 (tropical) grass, shrubs, urban, inland water, bare soil and ice. Crops are treated as grasses. Sub-grid heterogeneity is represented by tiling of land-surface types (for example, Essery et al., 2003). All land grid boxes can be made up of any mixture of the nine land-surface types, except ice. The surface fluxes of moisture and heat are computed individually for each tile, and the state of the grid box is prognosed via the aggregation of tiles fluxes. The biophysical state of each vegetation tile is defined by its leaf area index (LAI), canopy height and rooting depth. In JULES standard configuration, LAI is held constant or allowed to vary according to a prescribed seasonal cycle. When run in dynamic vegetation mode, using the TRIFFID module or with the PHENOLOGY routine selected, LAI varies with biomass production (Cox et al., 1999). 1.2 Growing sugarcane in JULES the development of JULES-SC As was mentioned above, in its standard configuration, JULES does not simulate the growth of crops. Therefore, a general crop growing module (JULES-Crop) has been developed along similar lines to JULES-SUCROS (see Van den Hoof et al., 2011). JULES- Crop is described in Osbourne et al. (in prep). An implementation of JULES-Crop for growing sugarcane (JULES-SC) has been developed for this study. In JULES-Crop, every 24 hours, the NPP, which has been accumulated at 1-hour time steps, is partitioned into four plant organs (leaves, stem, roots and grain) according to partition fractions. The partition fractions depend on the developmental stage of the plant. In JULES-Crop, there

2 are four developmental stages: sowing, emergence, flowering and maturity. These are determined by the development index (DVI). DVI is crop specific, and depends on the number of growing degree days (e.g. Van den Hoof et al., 2011). The dry biomass of each organ is obtained by integrating the NPP allocated to it over time. In contrast to the standard configuration of JULES, JULES-Crop calculates LAI, stem height, and root depth prognostically - according to the carbon contents of the crop s organs. As is shown in Figure S1, these variables are then returned to the main routines in JULES, where they influence the fluxes of heat, water, carbon and momentum between the atmosphere and land-surface, via changes to roughness length, albedo, rooting depth, LAI etc. In addition to the input data required for JULES, JULES-Crop requires information on crop cardinal temperatures, the thermal time requirements for each growth stage, the crop-specific allometric data and the sowing and harvest dates. The sowing date is defined by the user. Harvesting occurs either automatically when the crop has reached maturity, or on a user-defined date. After harvest, the carbon content of the plant s organs is returned to near zero. In this formulation of JULES, the carbon content of the soil is not varied. Sugarcane is different from the crops for which JULES-Crop was designed in several respects. Firstly, sugarcane may be ratooned (grown from stubble) rather than sown. In practice, sugarcane is allowed to go through 3-5 ratooning cycles before being resown, with progressive ratooning cycles having lower yields. Secondly, sugarcane develops slowly compared to other crops with the planting cycle taking up 15-18

3 months to grow and subsequent ratoons somewhat less time. Thirdly, sugarcane partitions carbon to leaf, stem and root, but with the stem carbon divided into sucrose solution and cellulose (structural stem). In this study JULES-Crop was adapted to grow sugarcane using the methodology described in Cuadra et al., The new model is referred to as JULES-SC (see Figure 3 for a summary of the interactions between JULES and JULES-SC). Specifically, the modifications made were as follows: 1. Ratooning. Three new tiles have been added: Sugarcane plant crop (year 1), sugarcane plant crop (year 2) and sugarcane ratoon. It is assumed that the sugarcane grid boxes contain a combination of sugarcane in its first year of planting, sugarcane in its second year of planting and ratoon crop. The fraction of each sugarcane tile type depends on the number of ratooning cycles. Only biomass from the ratoon and plant crop year 2 tiles are included in the yield calculations. It should be noted that we simulate both the planting and ratooning cycles simultaneously within our study areas using this tiling method. 2. Prescribed sowing and harvest windows. Harvesting occurs within a prescribed time window, occurring either at the beginning of the window (if the plant has reached maturity), during the window when the crop reaches maturity or at the end of the window. Because of the ratooning system, re-growth occurs immediately after harvest. 3. New partitioning equations. The carbon partitioning equations given in Cuadra et al., 2012 (equations 1-8) are adopted. The form of these equations is different to the default partitioning equations used in JULES- Crop. Specifically, there is no grain and instead, stem carbon is partitioned into structural stem and sucrose (see discussion above). 4. Yield calculation. Rather than being based on the grain plant organ, yield depends on the dry biomass apportioned to the stem (i.e. structural stem and sucrose). It was not possible to provide a general validation of JULES-SC s simulation of environmental variables and fluxes, such as those in the energy and water balance, because of a lack of available flux tower data over this specific type of (managed) vegetation. The JULES land-surface model on which JULES-SC is based has, however, been validated against flux tower observations for a wide range of plant functional types and environments (Blyth et al., 2011) and has been extensively utilised for land-surface

4 studies both in offline mode and as the land-surface scheme of the Hadley Centre models. Comparing JULES-SC output against JULES therefore provides a first order test of its performance. Table S1 shows the mean latent heat, sensible heat and net radiation over the whole Brazil study area for JULES and JULES-SC for (as in the next section) It can be seen that the differences are small but that overall, sugarcane makes more intensive use of water than natural vegetation. The values are moreover within the range of the observations of a single site within the São Paulo province for (Cuadra et al., 2012). Table S1: Mean annual energy balance for natural vegetation and sugarcane for the Brazil study area Energy ( MJ m 2 day -1 ) Natural vegetation Sugarcane Net Radiation Sensible Heat Latent Heat Meteorological forcing data 2.1 Historical period The meteorological inputs are taken from the 60-year global gridded data described in Sheffield et al., 2006 herein referred to as the Sheffield dataset. The product used in this study has a nominal spatial resolution of 1 0. The primary source of the Sheffield dataset is the NCEP reanalysis (Kalnay et al., 1996). The reanalysis is combined with data from other sources (for example TRMM (Kummerow et al., 1998) and GPCC (Rudolf and Schneider, 2005) to provide the model with precipitation data) in an attempt to correct the well-known biases (see for example Diro et al., 2009) of reanalysis data. Data are downscaled from the resolution of the reanalysis (2.5 0 ) using a statistical approach, which is fully described in Sheffield et al., The Sheffield dataset was chosen over other global land-surface forcing datasets because of its comparatively long time period ( ). No sub-daily local meteorological measurements were available for either of our study areas. 2.2 High temperature scenarios Although an increase in temperature within Africa under scenarios of anthropogenic climate change is projected by all of the CMIP3 models, the nature of changes in precipitation is highly uncertain largely because of discrepancies in the simulation of the West African monsoon between climate models (Boko et al., 2007). In addition,

5 climate models exhibit biases in their simulation of present day precipitation in West Africa (Ruti et al., 2011). These uncertainties mean that it is not possible to make a robust prediction of the impact of climate change on sugarcane yield. Instead, this study uses our model to explore the processes by which increases in temperature and atmospheric carbon dioxide concentration may impact biomass production, and hence yield. As our scientific aim is process-level understanding, rather than prediction, this study uses idealised scenarios of temperature increase rather than climate model output. In order to generate a large signal, temperatures were increased by 4 0 C a value at the upper end of the region s modelled response to a doubling of CO 2 in the atmosphere (Boko et al., 2007). In theory, surface temperature increases would be expected to impact humidity and precipitation, in line with the Clausius-Clapeyron relation. The magnitude of this effect is, however, difficult to ascertain - especially on land, where evaporation will very likely be limited by water availability (Wang and Dickinson, 2012). This is reflected by studies of the historical observed link between humidity and land surface temperature, which show that in some regions, including West Africa, the relationship is seasonally and spatially variable (Willett et al., 2010). Notwithstanding the theoretical link between changes in land surface temperature, humidity and precipitation, there is thus great uncertainty in how these variables will change as a result of land surface warming and changes in circulation. This is in contrast to temperature, for which changes are more robustly projected. For these reasons, we chose to focus on the impact of anthropogenic warming on sugarcane yield and irrigation requirement and we did not adjust the other meteorological inputs to JULES- SC. 3. Boundary and initial conditions Soil texture data were extracted from the International Satellite Land Surface Climatology Project, Initiative II (ISLSCP 2) dataset (Scholes and Brown de Colstoun, 2011). The ISLSCP2 data comprise the proportions of sand, silt and clay globally on a 1x1 0 grid. These soil textures are used to calculate the JULES soil physical parameters, using the Cosby et al., 1984 pedotransfer functions for the hydraulic parameters and the Lu et al., 2007 equations to derive thermal conductivity. The temperature and moisture content of the soil is set initially to a reasonable first guess value (based on regional P- E), and the model is allowed to spin up until these parameters reach equilibrium with the meteorological conditions.

6 In this formulation of JULES, there is no transfer of nutrients between soil and vegetation, and the nitrogen content of the vegetation is specified a priori. In effect, this is equivalent to assuming that there is sufficient application of fertilizer to ensure that plant growth is not limited by the nutrient content of the soil. The vegetation carbon content was set to near zero after ratooning and allowed to accumulate during the growing season as described above. Best, M.J. et al.(2011) The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes Geoscientific Model Development 4 pp Blyth, E. et al.(2011) A comprehensive set of benchmark tests for a land surface model of simultaneous fluxes of water and carbon at both the global and seasonal scale Geoscientific Model Development 4 pp Boko, M. et al.(2007) Africa. Climate Change 2007: Impacts, Adaptation and Vulnerability.. In Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Ed. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, C.E. Hanso. Cambridge University Press: Cambridge. pp Clark, D.B. et al.(2011) The Joint UK Land Environment Simulator (JULES), model description - Part 2: Carbon fluxes and vegetation dynamics Geoscientific Model Development 4 pp Cosby, B.J., G.M. Hornberger, R.B. Clapp and T.R. Ginn(1984) A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resources Research 20 pp Cox, P.M. et al.(1999) The impact of new land surface physics on the GCM simulation of climate and climate sensitivity Climate Dynamics 15 pp Cuadra, S.V. et al.(2012) A biophysical model of sugarcane growth Bioenergy DOI /j x. Diro, G.T., D.I.F. Grimes, E. Black, A. O'Neill and E. Pardo-Iguzquiza(2009) Evaluation of reanalysis rainfall estimates over Ethiopia International Journal of Climatology 29 pp Essery, R.L.H., M.J. Best, R.A. Betts, P.M. Cox and C.M. Taylor(2003) Explicit representation of subgrid heterogeneity in a GCM land surface scheme Journal of Hydrometeorology 4 pp Kalnay, E. et al.(1996) The NCEP/NCAR 40-year reanalysis project Bulletin of the American Meteorological Society 77 pp Kummerow, C., W. Barnes, T. Kozu, J. Shiue and J. Simpson(1998) The Tropical Rainfall Measuring Mission (TRMM) Sensor Package Journal of Atmospheric and Oceanic Technology 15 pp Lu, S., T. Ren and Y. Gong(2007) An improved model for predicting soil thermal conductivity from water content at room temperature Soil Sci. Am. 71 pp Osborne, T.M., J. Hooker, J. Gornall, A. Wiltshire, P. Falloon, R. Betts, T. Wheeler JULES-crop: a generic parameterisation of crops in the Joint UK Land Environment Simulator Rudolf, B. and U. Schneider(2005) Calculation of Gridded Precipitation Data for the Global Land-Surface using in-situ Gauge Observations Proceedings of

7 the 2nd Workshop of the International Precipitation Working Group IPWG, Monterey October 2004, EUMETSAT, ISBN , ISSN X pp Ruti, P.M. et al.(2011) The West African climate system: a review of the AMMA model inter-comparison initiatives Atmospheric Science Letters 12 pp Scholes, R.J. and E. Brown de Colstoun (2011) ISLSCP II Global Gridded Soil Characteristics Accessed: April 2012 Sheffield, J., G. Goteti and E.F. Wood(2006) Development of a 50-year highresolution global dataset of meteorological forcings for land surface modeling Journal of Climate 19 pp Van den Hoof, C., E. Hanert and P.L. Vidale(2011) Simulating dynamic crop growth with an adapted land surface model Äì JULES-SUCROS: Model development and validation Agricultural and Forest Meteorology 151 pp Wang, K. and R.E. Dickinson(2012) A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability Rev. Geophys. 50 pp. RG2005. Willett, K., P. Jones, P. Thorne and Gillett, N.(2010) A comparison of large scale changes in surface humidity over land in observations and CMIP3 general circulation models Environmental Research Letters doi: / /5/2/