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Climate change has the potential to increase surface ozone (O3) concentrations, known as the “ozone–climate penalty”, through changes to atmospheric chemistry, transport and dry deposition. In the tropics, the response of surface O3 to changing climate is relatively understudied but has important consequences for air pollution and human and ecosystem health. In this study, we evaluate the change in surface O3 due to climate change over South America and Africa using three state-of-the-art Earth system models that follow the Shared Socioeconomic Pathway 3-7.0 emission scenario from CMIP6. In order to quantify changes due to climate change alone, we evaluate the difference between simulations including climate change and simulations with a fixed present-day climate. We find that by 2100, models predict an ozone–climate penalty in areas where O3 is already predicted to be high due to the impacts of precursor emissions, namely urban and biomass burning areas, although on average, models predict a decrease in surface O3 due to climate change. We identify a small but robust positive trend in annual mean surface O3 over polluted areas. Additionally, during biomass burning seasons, seasonal mean O3 concentrations increase by 15 ppb (model range 12 to 18 ppb) in areas with substantial biomass burning such as the arc of deforestation in the Amazon. The ozone–climate penalty in polluted areas is shown to be driven by an increased rate of O3 chemical production, which is strongly influenced by NOx concentrations and is therefore specific to the emission pathway chosen. Multiple linear regression finds the change in NOx concentration to be a strong predictor of the change in O3 production, whereas increased isoprene emission rate is positively correlated with increased O3 destruction, suggesting NOx-limited conditions over the majority of tropical Africa and South America. However, models disagree on the role of climate change in remote, low-NOx regions, partly because of significant differences in NOx concentrations produced by each model. We also find that the magnitude and location of the ozone–climate penalty in the Congo Basin has greater inter-model variation than that in the Amazon, so further model development and validation are needed to constrain the response in central Africa. We conclude that if the climate were to change according to the emission scenario used here, models predict that forested areas in biomass burning locations and urban populations will be at increasing risk of high O3 exposure, irrespective of any direct impacts on O3 via the prescribed emission scenario.

Carbon and water cycle dynamics of vegetation are controlled primarily by photosynthesis and stomatal conductance (gs). Our goal is to improve the representation of these key physiological processes within the JULES land surface model, with a particular focus on refining the temperature sensitivity of photosynthesis, impacting modelled carbon, energy and water fluxes. We test (1) an implementation of the Farquhar et al. (1980) photosynthesis scheme and associated plant functional type-dependent photosynthetic temperature response functions, (2) the optimality-based gs scheme from Medlyn et al. (2011) and (3) the Kattge and Knorr (2007) photosynthetic capacity thermal acclimation scheme. New parameters for each model configuration are adopted from recent large observational datasets that synthesise global experimental data. These developments to JULES incorporate current physiological understanding of vegetation behaviour into the model and enable users to derive direct links between model parameters and ongoing measurement campaigns that refine such parameter values. Replacement of the original Collatz et al. (1991) C3 photosynthesis model with the Farquhar scheme results in large changes in GPP for the current day, with ∼ 10 % reduction in seasonal (June–August, JJA, and December–February, DJF) mean GPP in tropical forests and ∼ 20 % increase in the northern high-latitude forests in JJA. The optimality-based gs model decreases the latent heat flux for the present day (∼ 10 %, with an associated increase in sensible heat flux) across regions dominated by needleleaf evergreen forest in the Northern Hemisphere summer. Thermal acclimation of photosynthesis coupled with the Medlyn gs scheme reduced tropical forest GPP by up to 5 % and increased GPP in the high-northern-latitude forests by between 2 % and 5 %. Evaluation of simulated carbon and water fluxes by each model configuration against global data products shows this latter configuration generates improvements in these key areas. Thermal acclimation of photosynthesis coupled with the Medlyn gs scheme improved modelled carbon fluxes in tropical and high-northern-latitude forests in JJA and improved the simulation of evapotranspiration across much of the Northern Hemisphere in JJA. Having established good model performance for the contemporary period, we force this new version of JULES offline with a future climate scenario corresponding to rising atmospheric greenhouse gases (Shared Socioeconomic Pathway (SSP5), Representative Concentration Pathway 8.5 (RCP8.5)). In particular, these calculations allow for understanding of the effects of long-term warming. We find that the impact of thermal acclimation coupled with the optimality-based gs model on simulated fluxes increases latent heat flux (+50 %) by the year 2050 compared to the JULES model configuration without acclimation. This new JULES configuration also projects increased GPP across tropical (+10 %) and northern-latitude regions (+30 %) by 2050. We conclude that thermal acclimation of photosynthesis with the Farquhar photosynthesis scheme and the new optimality-based gs scheme together improve the simulation of carbon and water fluxes for the current day and have a large impact on modelled future carbon cycle dynamics in a warming world.

The Amazon is the largest continuous tropical forest in the world and plays a key role in the global carbon cycle. Human-induced disturbances (e.g., deforestation and wildfires) in combination with climate change have impacted its carbon cycling. However, uncertainties remain on the magnitude of carbon fluxes associated with human-induced disturbances and the old-growth forest sink, and thus the net land carbon balance of the Amazon. Here we synthesize state-of-the-art estimates of the land carbon flux components in the Amazon. To quantify the human-disturbance fluxes from deforestation, land use and land cover changes and degradation, we use a set of bookkeeping models. The annual intact sink was quantified using a set of 16 Dynamic Global Vegetation Models (DGVMs). We then combine the carbon flux estimates from disturbances with the intact sink estimates to provide a bottom-up estimate of the net land carbon flux and compare them alongside top-down estimates from atmospheric model inversions. Between 2010 and 2018, the net land carbon flux in the Brazilian Amazon estimated with the bottom-up approach was -59 (±160) Tg C yr-1 and +36 (±125) Tg C yr-1 with the top-down approach. Despite disagreeing on the sign of the flux, this analysis suggests that the Brazilian Amazon was on average near carbon neutral over the 2010-2018 period, given the large uncertainties underlying both methods. The net land carbon fluxes for the years 2019 and 2020 based on the bottom-up approach were larger than for 2010-2018. This is likely primarily due to direct emissions related to an increase in deforestation although it may possibly be partly caused by a weakening of the forest carbon sink, both in response to deforestation and a warming climate. Spatially, both methodologies agree that the south-eastern Amazon was a net carbon source over the whole study period. These results have important implications for the mitigation potential of Brazilian ecosystems within the goals of the Paris Agreement.

Global process-based models with dynamic vegetation and functions show an increasing ongoing land carbon (C) sink, primarily due to CO2 fertilization effects (Sitch et al., 2008; Schimel et al., 2015). However, the phosphorus (P) and Nitrogen (N) related processes may constrain the plant’s photosynthetic and growth capacity and the C sink magnitude in temperate forests consequently (Aerts and Chapin, 1999; Vitousek et al., 1997; Vitousek, 2004). The co-limitation of N and P changes the plant C fixation capacity and the uptake of the atmospheric CO2 consequently. However, modelling studies are incapable of reproducing the productivity decrease due to these limitations (Fleischer et al., 2019). In this study, we introduced the P dynamics, and its interaction with N and C cycles in the Joint UK Land Environment Simulator (JULES-CNP), and parametrized and calibrated it using a well-site Free-Air Carbon Enrichment (FACE) facility in Mill Haft woodland, England. We further investigate the role of N and P availability and limiting impact on C cycles under different CO2 conditions. Furthermore, we modified the SOC representation in JULES (RothC model) to simulate observed Soil C soil stabilization capacity that is mainly related to soil texture. Our study shows an NPP reduction due to N and P limitation under both ambient and elevated CO2 conditions at the study site. Furthermore, using an improved SOC model in JULES, our results show an increase in soil C turnover with rising CO2 which leads to lower equilibrium soil C stocks than expected from the rise in soil C input alone. Reference: Aerts, R. and Chapin, F. S.: The Mineral Nutrition of Wild Plants Revisited: A Re-evaluation of Processes and Patterns, Adv. Ecol. Res., 30, 1–67, https://doi.org/10.1016/S0065-2504(08)60016-1, 1999. Fleischer, K., Rammig, A., De Kauwe, M. G., Walker, A. P., Domingues, T. F., Fuchslueger, L., Garcia, S., Goll, D. S., Grandis, A., Jiang, M., Haverd, V., Hofhansl, F., Holm, J. A., Kruijt, B., Leung, F., Medlyn, B. E., Mercado, L. M., Norby, R. J., Pak, B., von Randow, C., Quesada, C. A., Schaap, K. J., Valverde-Barrantes, O. J., Wang, Y.-P., Yang, X., Zaehle, S., Zhu, Q., and Lapola, D. M.: Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition, Nat. Geosci., 12, 736–741, https://doi.org/10.1038/s41561-019-0404-9, 2019. Vitousek, P. M.: Nutrient Cycling and Limitation: Hawai’i as a Model System, Princeton University Press, 2004. Vitousek, P. M., Mooney, H. A., Lubchenco, J. M., and Melillo, J. M.: Human Domination of Earth Ecosystems, Science (80-. )., 278, 21, 1997.

Most Land Surface Models (LSMs), the land components of Earth system models (ESMs), include representation of N limitation on ecosystem productivity. However only few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be particularly important as N tends to be abundant but the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO2 conditions remains highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We evaluate JULES-CNP at the Amazon nutrient fertilization experiment (AFEX), a low fertility site, representative of about 60 % of Amazon soils. We apply the model under ambient CO2 and elevated CO2. The model is able to reproduce the observed plant and soil P pools and fluxes under ambient CO2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO2 and by 46 % under elevated CO2. Under elevated CO2, biomass in simulations accounting for CNP increase by 10 % relative to at contemporary CO2, although it is 5 % lower compared with CN and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low fertility soils.

Tropical forests, while only occupying 12% to 15% of the Earth's surface, contain about 25% of the world's carbon biomass, with soils representing the second largest reservoir. Yet, recent studies have suggested that, in response to changing environmental conditions, in future decades tropical forests can switch from carbon sinks to carbon sources, with profound implications for the global carbon cycle. Most of these conclusions result from studies in lowland humid forests. However, other tropical forests, such as those occurring in the Andes are also important determinants of regional-to-global biogeochemical functioning, and their sensitivity to future warming has been less studied than in lowland forests. In this study, we explore intra and interspecific thermal sensitivity of soil respiration and its components (autotrophic and heterotrophic) in 15 dominant tree species in the tropical Andes, through an experimental thermosecuence in the Colombian Andes that uses elevation as a proxy for warming. In this thermosequence, a common garden experiment was set up and individuals from 15 dominant species were planted in three sites that represent a temperature gradient: the higher elevation site (2452 masl) corresponds to the base condition; the mid-elevation site (1326 masl) represents a warming of 8°C; and the lower site (575 masl) and it represents a warming of 12°C. Our results indicate consistently higher respiration values with increased temperature both within and between tree species. We used 𝑸10 values (the factor by which soil respiration increases for every 10-degree rise in temperature) to determine the temperature sensitivity of soil respiration. More specifically, for a warming of 5°C there is a temperature coefficient of 𝑸10 = 2 and for a warming of 9°C and there is a temperature coefficient 𝑸10 = 3, this means that for the greater increase temperature the soil respiration can increase faster. Notably, our results show that not all species respond equally to augmented temperatures, highlighting the potential for differential effects of increased temperature and more generally, of environmental change in the compositions and function of these strategic ecosystems. Collectively, our results are relevant for the management and adaptation of ecosystems, particularly tropical Andean forest, and for the refinement of ecological models that support projections of global environmental change and carbon cycle.

Brazil is responsible for about one third of the global land use and land cover change (LULCC) carbon dioxide emissions. However, there is a disagreement among different methodologies on the magnitude and trends in emissions and their geographic distribution. One of the main uncertainties is associated with different LULCC datatasets used as input in the different approaches. In this work we perform an evaluation of LULCC datasets for Brazil, including the global dataset (HYDE 3.2) used in the annual Global Carbon Budget (GCB), and national Brazilian dataset (MapBiomas) over the period 2000-2018. We also analyze the latest global HYDE 3.3 dataset based on new FAO inventory estimates and multi-annual ESA CCI satellite-based land cover maps. Results show that the new HYDE 3.3 can represent well the observed spatial variation in cropland and pastures areas over the last decades compared to national data (MapBiomas) and shows an improvement compared to HYDE 3.2 used in GCB. However, the magnitude of LULCC assessed with HYDE 3.3 is lower than national estimates from MapBiomas. Finally, we used HYDE 3.3 as input to two different approaches included in GCB, a global bookkeeping model (BLUE) and a process-based Dynamic Global Vegetation Model (JULES-ES) to determine the impact of the new version of HYDE dataset on Brazil’s land-use emissions trends over the period 2000-2017. Both JULES-ES and BLUE now simulate a negative land-use emissions trend for the last two decades. This negative trend is in agreement with Brazilian INPE-EM, global H&N bookkeeping models, FAO and as reported in National GHG inventories (NGHGI), although magnitudes differ among approaches. Overall, the inclusion of the multi-annual ESA CCI Land Cover dataset to allocate spatially the FAO statistical data has improved spatial representation of agricultural area change in Brazil in the last two decades, contributing to improve global model capability to simulate Brazil’s LULCC emissions in agreement with national trends estimates and spatial distribution.

Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

This article presents the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. This new numerical system is denominated JULES-CCATT-BRAMS. We demonstrate the performance of this new model system in relation to several meteorological variables and the CO2 mixing ratio over a large part of South America, focusing on the Amazon basin. The evaluation was conducted for two time periods, the wet (March) and dry (September) seasons of 2010. The model errors were calculated in relation to meteorological observations at conventional stations in airports and automatic stations. In addition, CO2 mixing ratios in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with observations obtained with airborne instruments. The results of this study show that the JULES-CCATT-BRAMS modeling system provided a significant gain in performance for the considered atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally employed by CCATT-BRAMS. In addition, the new system significantly increases the ability to simulate processes involving air–surface interactions, due to the ability of JULES to simulate photosynthesis, respiration and dynamic vegetation, among other processes. We also discuss a wide range of numerical studies involving coupled atmospheric, land surface and chemistry processes that could be done with the system introduced here. Thus, this work presents to the scientific community a free modeling tool, with good performance in comparison with observational data and reanalysis model data, at least for the region and time period discussed here. Therefore, in principle, this model is able to produce atmospheric hindcast/forecast simulations at different spatial resolutions for any time period and any region of the globe.

Every year, a dense smoke haze of regional dimensions covers a large portion of South America originated from fire activities in the Amazon Basin and Central parts of Brazil during the dry/biomass-burning season between August and October. Over a large portion of South America, the average aerosol optical depth at 550 nm exceeds 1.0 during the fire season while the background value during the rainy season is below 0.2. Smoke aerosol particles increase scattering and absorption of the incident solar radiation. The regional-scale aerosol layer reduces the amount of solar energy reaching the surface, cools the near surface air, and increases the diffuse radiation fraction over a large disturbed area of the Amazon rainforest. These factors affect the energy and CO2 fluxes at the surface. In this work, we applied a fully integrated atmospheric model to assess the impact of smoke aerosols in CO2 fluxes in the Amazon region during 2010. We address the effects of the attenuation of the solar global radiation and the enhancement of the diffuse solar radiation flux inside the canopy. Our results indicated that the smoke aerosols led to an increase of about 22 % of the gross primary productivity of Amazonia, 9 % of plant respiration and a decline in soil respiration from of 3 %. Consequently, Amazonia net ecosystem exchange during September 2010 dropped from +101 to −104 TgC when the aerosol effects were considered, mainly due to the aerosol diffuse radiation effect. For the forest biome, our results pointed to a dominance of the diffuse radiation effect on CO2 fluxes, reaching a balance of 50 % – 50 % between the diffuse and direct aerosol effects for high aerosol loads. For C3 grass type and cerrado, as expected, the contribution of the diffuse radiation effect is much lower, tending to zero with the increase of aerosol load. That is, the Amazon during the dry season, in the presence of high smoke aerosol loads, change from being a source to be a sink of CO2 to the atmosphere.

L. M. Mercado1,2, J. Lloyd3, A. J. Dolman4, S. Sitch5, and S. Patino2,3,6 1Centre for Ecology and Hydrology, Wallingford, Oxon, OX10 8BB, UK 2Max Planck Institute for Biogeochemistry, 07745 Jena, Germany 3School of Geography, University of Leeds, LS2 9JT, UK 4Free University of Amsterdam, 1081 HV Amsterdam, The Netherlands 5Met Office Hadley Centre, JCHMR, Wallingford, Oxon, OX10 8BB, UK 6Instituto de Investigacion de Recursos Biologicos Alexander von Humboldt, Diagonal 27 No. 15-09, Bogota D.C, Colombia

This article presents the development of a new numerical system denominated JULES-CCATT-BRAMS, which resulted from the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. The performance of this system in relation to several meteorological variables (wind speed at 10 m, air temperature at 2 m, dew point temperature at 2 m, pressure reduced to mean sea level and 6 h accumulated precipitation) and the CO2 concentration above an extensive area of South America is also presented, focusing on the Amazon basin. The evaluations were conducted for two periods, the wet (March) and dry (September) seasons of 2010. The statistics used to perform the evaluation included bias (BIAS) and root mean squared error (RMSE). The errors were calculated in relation to observations at conventional stations in airports and automatic stations. In addition, CO2 concentrations in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with aircraft data. The results of this study show that the JULES model coupled to CCATT-BRAMS provided a significant gain in performance in the evaluated atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally utilized by CCATT-BRAMS. Simulations of CO2 concentrations in Amazonia and a comparison with observations are also discussed and show that the system presents a gain in performance relative to previous studies. Finally, we discuss a wide range of numerical studies integrating coupled atmospheric, land surface and chemistry processes that could be produced with the system described here. Therefore, this work presents to the scientific community a free tool, with good performance in relation to the observed data and re-analyses, able to produce atmospheric simulations/forecasts at different resolutions, for any period of time and in any region of the globe.

Over recent years, it has become increasingly apparent that climate change and air pollution need to be considered jointly for improved attribution and projections of human-caused changes in the Earth system. Exchange processes at the land surface come into play in this context, because many compounds that either act as greenhouse gases, as pollutant precursors, or both, have not only anthropogenic but also terrestrial sources and sinks. And since the fluxes of multiple gases and particulate matter between the terrestrial biota and the atmosphere are directly or indirectly coupled to vegetation and soil carbon, nutrient and water balances, quantification of their geographic patterns or changes over time requires due consideration of the underlying biological processes. In this review we highlight a number of critical aspects and recent progress in this respect, identifying in particular a number of areas where studies have shown that accounting for ecological process understanding can alter global model projections of land-atmosphere interactions substantially. Specifically, this concerns the improved quantification of uncertainties and dynamic system responses, including acclimation, and the incorporation of exchange processes that so far have been missing from global models even though they are proposed to be of relevance for our understanding of terrestrial biota-climate feedbacks. Progress has also been made regarding studies on the impacts of land use/land cover change on climate change, but the absence of a mechanistically based representation of human response-processes in ecosystem models that are coupled to climate models limits our ability to analyse how climate change or air pollution in turn might affect human land use. A more integrated perspective is necessary and should become an active area of research that bridges the socio-economic and biophysical communities.

The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Past studies with JULES have demonstrated the important role of the land surface in the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of separately changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. There was a need to consolidate these and other advances into a single model code so as to be able to study interactions in a consistent manner. This paper describes the consolidation of these advances into the modelling of carbon fluxes and stores, in the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.

We present a computationally efficient modelling system, IMOGEN, designed to undertake global and regional assessment of climate change impacts on the physical and biogeochemical behaviour of the land surface. A pattern-scaling approach to climate change drives a gridded land surface and vegetation model MOSES/TRIFFID. The structure allows extrapolation of General Circulation Model (GCM) simulations to different future pathways of greenhouse gases, including rapid first-order assessments of how the land surface and associated biogeochemical cycles might change. Evaluation of how new terrestrial process understanding influences such predictions can also be made with relative ease.

Vertical profiles in leaf mass per unit leaf area (MA), foliar 13C composition (δ13C) and leaf nitrogen (N), phosphorus (P), carbon (C), potassium (K), magnesium (Mg) and calcium (Ca) concentrations were estimated for 204 rain forest trees growing in 57 sites across the Amazon Basin. Data was analysed using a multilevel modelling approach, allowing a separation of gradients within individual tree canopies (intra-tree gradients) as opposed to stand level gradients occurring because of systematic differences occurring between different trees of different heights (inter-tree gradients). Significant positive intra-tree gradients (i.e. increasing values with increasing sampling height) were observed for MA and [C]DW (the subscript denoting on a dry weight basis) with negative intra-tree gradients observed for δ13C, [Mg]DW and [K]DW. No significant intra-tree gradients were observed for [N]DW, [P]DW or [Ca]DW. Although the magnitudes of inter-tree gradients were not significantly different for MA, δ13C, [C]DW, [K]DW, [N]DW, [P]DW and [Ca]DW, for [Mg]DW there no systematic difference observed between trees of different heights, this being in contrast to the strongly negative intra-tree gradients also found to exist. When expressed on a leaf area basis, significant positive gradients were observed for N, P and K both within and between trees, these being attributable to the positive intra- and inter-tree gradients in MA mentioned above. No systematic intra-tree gradient was observed for either Ca or Mg when expressed on a leaf area basis, but with a significant positive gradient observed for Mg between trees (i.e. with taller trees tending to have a higher Mg per unit area). In contrast to the other variables measured, significant variations in intra-tree gradients for different individuals were found to exist for MA, δ13C and [P] (area basis). This was best associated with the overall average area based [P], this also being considered to be a surrogate for a leaf's photosynthetic capacity, Amax. A new model is presented which is in agreement with the above observations. The model predicts that trees characterised by a low upper canopy Amax should have shallow or even non-existent gradients in Amax, with optimal intra-canopy gradients becoming sharper as a tree's upper canopy Amax increases. Nevertheless, in all cases it is predicted that the optimal within-canopy gradients in Amax should be less than is generally observed for photon irradiance. Although this is consistent with numerous observations, it is also in contrast to previously held notions of optimality.