Joint UK Land Environment Simulator Research Articles

Future amazon basin wetland hydrology under projected climate change

Climate change over the Amazon basin has the potential to cause major hydrological and ecological impacts over the region’s extensive wetlands. To investigate this the Joint UK Land Environment Simulator (JULES) land surface model is first extended to include riverine inundation. Potential impacts of future climate change on Amazon basin wetlands are then evaluated by driving this updated JULES model with modelled meteorology projections from six different climate simulations reaching approximately 4°C global warming at the end of the 21st Century. The projected changes in inundation extent and seasonality are assessed over four major wetland regions. The simulations project, on average, a significant decrease in total Amazon basin inundated area of 11% (range: -36% to +9%) by the 2090s. This considerable spread is primarily driven by disparity in simulated precipitation changes, ultimately driven by sea surface temperature differences. The wetter contemporary climate simulations simulate the greatest drying by the end of this Century, resulting in the largest wetland area reductions. The largest qualitative disagreement is over the western Iquitos wetland, with inundated area changes ranging from a very large reduction of -53% to an increase of 12%. A new wetland classification scheme is developed to summarise the projected changes in wetland seasonality. The largest drops in simulated wetland season length occur over the Central/East Manaus and West Iquitos wetland regions, with reductions of up to 10 and 8 months respectively. Such significant changes in future inundation are likely to have a major impact on regional wetland hydrology and their ecosystems.

Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain

Abstract. Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO2; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate.

Constraining the carbon cycle in JULES-ES-1.0

Abstract. Land surface models are an important tool in the study of climate change and its impacts, but their use can be hampered by uncertainties in input parameter settings and by errors in the models. We apply uncertainty quantification (UQ) techniques to constrain the input parameter space and corresponding historical simulations of JULES-ES-1.0 (Joint UK Land Environment Simulator Earth System), the land surface component of the UK Earth System Model, UKESM1.0. We use an ensemble of historical simulations of the land surface model to rule out ensemble members and corresponding input parameter settings that do not match modern observations of the land surface and carbon cycle. As JULES-ES-1.0 is computationally expensive, we use a cheap statistical proxy termed an emulator, trained on the ensemble of model runs, to rule out parts of the parameter space where the simulator has not yet been run. We use history matching, an iterated approach to constraining JULES-ES-1.0, running an initial ensemble and training the emulator, before choosing a second wave of ensemble members consistent with historical land surface observations. We successfully rule out 88 % of the initial input parameter space as being statistically inconsistent with observed land surface behaviour. The result is a set of historical simulations and a constrained input space that are statistically consistent with observations. Furthermore, we use sensitivity analysis to identify the most (and least) important input parameters for controlling the global output of JULES-ES-1.0 and provide information on how parameters might be varied to improve the performance of the model and eliminate model biases.

Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1

Abstract. Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs).

Constraining the budget of atmospheric carbonyl sulfide using a 3-D chemical transport model

Abstract. Carbonyl sulfide (OCS) has emerged as a valuable proxy for photosynthetic uptake of carbon dioxide (CO2) and is known to be important in the formation of aerosols in the stratosphere. However, uncertainties in the global OCS budget remain large. This is mainly due to the following three flux terms: vegetation uptake, soil uptake and oceanic emissions. Bottom-up estimates do not yield a closed budget, which is thought to be due to tropical emissions of OCS that are not accounted for. Here we present a simulation of atmospheric OCS over the period 2004–2018 using the TOMCAT 3-D chemical transport model that is aimed at better constraining some terms in the OCS budget. Vegetative uptake of OCS is estimated by scaling gross primary productivity (GPP) output from the Joint UK Land Environment Simulator (JULES) using the leaf relative uptake (LRU) approach. The remaining surface budget terms are taken from available literature flux inventories and adequately scaled to bring the budget into balance. The model is compared with limb-sounding satellite observations made by the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) and surface flask measurements from 14 National Oceanic and Atmospheric Administration – Earth System Research Laboratory (NOAA-ESRL) sites worldwide. We find that calculating vegetative uptake using the LRU underestimates the surface seasonal cycle amplitude (SCA) in the Northern Hemisphere (NH) mid-latitudes and high latitudes by approximately 37 ppt (35 %). The inclusion of a large tropical source is able to balance the global budget, but further improvement to the SCA and phasing would likely require a flux inversion scheme. Compared to co-located ACE-FTS OCS profiles between 5 and 30 km, TOMCAT remains within 25 ppt (approximately 5 % of mean tropospheric concentration) of the measurements throughout the majority of this region and lies within the standard deviation of these measurements. This provides confidence in the representation of atmospheric loss and surface fluxes of OCS in the model. Atmospheric sinks account for 154 Gg S of the annual budget, which is 10 %–50 % larger than previous studies. Comparing the surface monthly anomalies from the NOAA-ESRL flask data to the model simulations shows a root-mean-square error range of 3.3–25.8 ppt. We estimate the total biosphere uptake to be 951 Gg S, which is in the range of recent inversion studies (893–1053 Gg S), but our terrestrial vegetation flux accounts for 629 Gg S of the annual budget, which is lower than other recent studies (657–756 Gg S). However, to close the budget, we compensate for this with a large annual oceanic emission term of 689 Gg S focused over the tropics, which is much larger than bottom-up estimates (285 Gg S). Hence, we agree with recent findings that missing OCS sources likely originate from the tropical region. This work shows that satellite OCS profiles offer a good constraint on atmospheric sinks of OCS through the troposphere and stratosphere and are therefore useful for helping to improve surface budget terms. This work also shows that the LRU approach is an adequate representation of the OCS vegetative uptake, but this method could be improved by various means, such as using a higher-resolution GPP product or plant-functional-type-dependent LRU. Future work will utilise TOMCAT in a formal inversion scheme to better quantify the OCS budget.

Description and evaluation of the JULES-ES set-up for ISIMIP2b

Abstract. Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth system models is often affected by the atmospheric models' climate biases, leading to errors in land surface projections. Here we run the Joint UK Land Environment Simulator Earth System configuration (JULES-ES) land surface model with the Inter-Sectoral Impact Model Intercomparison Project second-phase future projections (ISIMIP2b) bias-corrected climate model data from four global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate the performance of JULES-ES against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current Earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation set-up, postprocessing and initial evaluation, using the International Land Model Benchmarking (ILAMB) project. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.

Combining local model calibration with the emergent constraint approach to reduce uncertainty in the tropical land carbon cycle feedback

Abstract. The role of the land carbon cycle in climate change remains highly uncertain. A key source of the projection spread is related to the assumed response of photosynthesis to warming, especially in the tropics. The optimum temperature for photosynthesis determines whether warming positively or negatively impacts photosynthesis, thereby amplifying or suppressing CO2 fertilisation of photosynthesis under CO2-induced global warming. Land carbon cycle models have been extensively calibrated against local eddy flux measurements, but this has not previously been clearly translated into a reduced uncertainty in terms of how the tropical land carbon sink will respond to warming. Using a previous parameter perturbation ensemble carried out with version 3 of the Hadley Centre coupled climate–carbon cycle model (HadCM3C), we identify an emergent relationship between the optimal temperature for photosynthesis, which is especially relevant in tropical forests, and the projected amount of atmospheric CO2 at the end of the century. We combine this with a constraint on the optimum temperature for photosynthesis, derived from eddy covariance measurements using the adjoint of the Joint UK Land Environment Simulator (JULES) land surface model. Taken together, the emergent relationship from the coupled model and the constraint on the optimum temperature for photosynthesis define an emergent constraint on future atmospheric CO2 in the HadCM3C coupled climate–carbon cycle under a common emissions scenario (A1B). The emergent constraint sharpens the probability density of simulated CO2 change (2100–1900) and moves its peak to a lower value of 497 ± 91 compared to 607 ± 128 ppmv (parts per million by volume) when using the equal-weight prior. Although this result is likely to be model and scenario dependent, it demonstrates the potential of combining the large-scale emergent constraint approach with a parameter estimation using detailed local measurements.

Reduced-order digital twin and latent data assimilation for global wildfire prediction

Abstract. The occurrence of forest fires can impact vegetation in the ecosystem, property, and human health but also indirectly affect the climate. The Joint UK Land Environment Simulator – INteractive Fire and Emissions algorithm for Natural envirOnments (JULES-INFERNO) is a global land surface model, which simulates vegetation, soils, and fire occurrence driven by environmental factors. However, this model incurs substantial computational costs due to the high data dimensionality and the complexity of differential equations. Deep-learning-based digital twins have an advantage in handling large amounts of data. They can reduce the computational cost of subsequent predictive models by extracting data features through reduced-order modelling (ROM) and then compressing the data to a low-dimensional latent space. This study proposes a JULES-INFERNO-based digital twin fire model using ROM techniques and deep learning prediction networks to improve the efficiency of global wildfire predictions. The iterative prediction implemented in the proposed model can use current-year data to predict fires in subsequent years. To avoid the accumulation of errors from the iterative prediction, latent data assimilation (LA) is applied to the prediction process. LA manages to efficiently adjust the prediction results to ensure the stability and sustainability of the prediction. Numerical results show that the proposed model can effectively encode the original data and achieve accurate surrogate predictions. Furthermore, the application of LA can also effectively adjust the bias of the prediction results. The proposed digital twin also runs 500 times faster for online predictions than the original JULES-INFERNO model without requiring high-performance computing (HPC) clusters.

Assessing methods for representing soil heterogeneity through a flexible approach within the Joint UK Land Environment Simulator (JULES) at version 3.4.1

Abstract. The interactions between the land surface and the atmosphere can impact weather and climate through the exchanges of water, energy, carbon and momentum. The properties of the land surface are important when modelling these exchanges correctly especially with models being used at increasingly higher resolution. The Joint UK Land Environment Simulator (JULES) currently uses a tiled representation of land cover but can only model a single dominant soil type within a grid box. Hence, there is no representation of sub-grid-scale soil heterogeneity. This paper introduces and evaluates a new flexible surface–soil tiling scheme in JULES. Several different soil tiling approaches are presented for a synthetic case study. The changes to model performance have been compared to the current single-soil scheme and a high-resolution “Truth” scenario. Results have shown that the different soil tiling strategies do have an impact on the water and energy exchanges due to the way vegetation accesses the soil moisture. Tiling the soil according to the surface type, with the soil properties set to the dominant soil type under each surface is the best performing configuration. The results from this setup simulate water and energy fluxes that are the closest to the high-resolution Truth scenario but require much less information on the soil type than the high-resolution soil configuration.

A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models

Abstract. The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992–2020) of annual land cover maps at 300 m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300 m resolution for 1992–2020, created by fusing the 300 m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300 m resolution indicate the percentage cover (0 %–100 %) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models – Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) – to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at https://doi.org/10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).

The second Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL2

Abstract. In this paper we define RAL2 – the second Regional Atmosphere and Land (RAL) science configuration for regional modelling. RAL2 uses the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL2 defines the science configuration of the dynamics and physics schemes of the atmosphere and land and builds on the baseline of RAL1. There are two RAL2 sub-releases, one for mid-latitudes (RAL2-M) and one for tropical regions (RAL2-T). We document the differences between them and where appropriate discuss how RAL2 relates to RAL1 and the corresponding configuration of the global forecasting model. Our results show an increase in medium and low cloud amounts in the mid-latitudes leading to improved cloud forecasts. The increase in cloud amount leads to a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates. RAL2 is expected to be the last RAL science configuration with two sub-releases as research effort is focused on producing a single defined configuration of the model that performs effectively in all regions of the world.

South American mountain ecosystems and global change – a case study for integrating theory and field observations for land surface modelling and ecosystem management

ABSTRACT Background Plot-based monitoring has yielded much information on the taxonomic diversity and carbon (C) storage in tropical lowland forests of the Amazon basin. This has resulted in an improved understanding of the relationship between lowland forest biomass dynamics and global change drivers, such as climate change and atmospheric CO2 concentration. Much less attention has been paid to the mountain ecosystems of South America that comprise montane forests and alpine vegetation (páramo, puna, high Andean grasslands, wetlands, and alpine heath). This vegetation complex provides a variety of ecosystem services and forms a natural laboratory along various physiographic, geological and evolutionary history/biogeography, and land use history gradients. Aims Here, we review existing empirical understanding and model-based approaches to quantify the contribution of mountain ecosystems to ecosystem service provision in the rapidly changing socioecological setting of the South American mountains. The objective of this paper is to outline a broad road map for the implementation of mountain vegetation into dynamic global vegetation models (DGVM) for use in Earth System Models (ESM), based on our current understanding of their structure and function and of their responsiveness to global change drivers. We also identify treeline processes, critical in mountain ecosystems, as key missing elements in DGVMs/ESMs, and thus explore in addition a treeline model. Methods Stocktaking of the availability of empirical data was undertaken from eight research sites along the Andes and in south-eastern Brazil. Out of eight sites, two (one each in Venezuela and Brazil) had some climate, ecological and ecophysiological data potentially suitable to parametrise a DGVM. Tree biomass data were available for six sites. A preliminary assessment of the Joint UK Land Environment Simulator (JULES) DGVM was made to identify gaps in available data and their impacts on model parametrisation and calibration. Additionally, the potential climate-determined elevation of the treeline was modelled to check the DGVM for its ability to identify the transition between the montane forest and alpine vegetation. Results Outcomes of the evaluation of the JULES land surface model identified the following key processes in montane forests: temperature-related decrease in net primary production, respiration, and allocation to above-ground biomass and increase in soil C stocks with elevation. There was a variable agreement between simulated biomass and those derived from field measurements via allometric equations. Conclusions We identified major gaps between data availability and the needs for process-based modelling of South American mountain vegetation and its dynamics in DGVMs. To bridge this gap, we propose a transdisciplinary network, composed of members of the theoretical/modelling and empirical scientific communities, to study the natural dynamics of mountain ecosystems and their responses to global change drivers locally, regionally and at the continental scale, within a social-ecological system framework. The work presented here forms the basis for the design of data collection from field measurements and instrumental monitoring stations to parametrise and verify DGVMs. The network is designed to collaborate with and complement existing long-term research initiatives in the region and will adopt existing standard field protocols. Complementary protocols will ensure compatibility between field data collection and data needed for process-based and empirical models.

Evaluation of wetland CH4in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Abstract. Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.

Improving the representation of sugarcane crop in the Joint UK Land Environment Simulator (JULES) model for climate impact assessment

AbstractBioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision‐making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES‐crop and JULES‐BE parametrizations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES‐crop and JULES‐BE, together with the response to atmospheric carbon dioxide, temperature and low‐water availability (CTW‐response). Both parametrizations showed comparable performance to other sugarcane dynamic models, with a root mean squared error of 6.75 and 6.05 t ha−1 for stalk dry matter for JULES‐crop and JULES‐BE, respectively. The parametrizations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the years 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35°C become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.

Parameterizing the JULES land surface model for different land covers in the tropical Andes

ABSTRACT Physically based hydrological models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Generating parameter sets for a larger variety of land cover types may be an appropriate approach to simplify setting up JULES for operating simulations beyond the default parameterizations. Here we explore the possibilities of this approach using a case study in the tropical Andes. First, we evaluate to what extent the standard JULES land cover configurations can simulate the hydrological response of dominant soil and land cover types of the region. Next, we adjust the soil water retention parameters on a regional basis using experimental soil data from representative sites. We find that the adjusted parameters result in substantial alteration for the flow partition. Such parameterizations may increase the configurations to implement JULES for a larger variety of land cover types and assess soil disturbance’s potential impact more precisely.

Evaluating the Atibaia River hydrology using JULES6.1

Abstract. Land surface models such as the Joint UK Land Environment Simulator (JULES) are increasingly used for hydrological assessments because of their state-of-the-art representation of physical processes and versatility. Unlike statistical models and AI models, the JULES model simulates the physical water flux under given meteorological conditions, allowing us to understand and investigate the cause and effect of environmental changes. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. The watershed is suffering increasing hydrological risks, which could be attributed to environmental changes, such as urbanization and agricultural activity. The increasing risks highlight the importance of evaluating the land surface processes of the watershed systematically. We explore the use of local precipitation collection in conjunction with data from a global meteorological reanalysis to simulate the basin hydrology. Our results show that key hydrological fluxes in the basin can be represented by the JULES model simulations.

Representation of the phosphorus cycle in the Joint UK Land Environment Simulator (vn5.5_JULES-CNP)

Abstract. Most land surface models (LSMs), i.e. the land components of Earth system models (ESMs), include representation of nitrogen (N) limitation on ecosystem productivity. However, only a few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be important as N tends to be abundant, whereas 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 remain 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 develop and evaluate JULES-CNP using in situ data collected at a low-fertility site in the central Amazon, with a soil P content representative of 60 % of soils across the Amazon basin, to parameterize, calibrate, and evaluate JULES-CNP. Novel soil and plant P pool observations are used for parameterization and calibration, and the model is evaluated against C fluxes and stocks and those soil P pools not used for parameterization or calibration. We then evaluate the model at additional P-limited test sites across the Amazon and in Panama and Hawaii, showing a significant improvement over the C- and CN-only versions of the model. The model is then applied under elevated CO2 (600 ppm) at our study site in the central Amazon to quantify the impact of P limitation on CO2 fertilization. We compare our results against the current state-of-the-art CNP models using the same methodology that was used in the AmazonFACE model intercomparison study. The model is able to reproduce the observed plant and soil P pools and fluxes used for evaluation 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 contemporary CO2 conditions, although it is 5 % lower compared to CN- and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon rainforest with low-fertility soils.

CO2 fertilization of crops offsets yield losses due to future surface ozone damage and climate change

Tropospheric ozone (O3) is harmful to plant productivity and negatively impacts crop yields. O3 concentrations are projected to decrease globally in the optimistic Representative Concentration Pathway of 2.6 W m–2 (RCP2.6) but increase globally following the high-emission scenario under the RCP8.5, with substantial implications for global food security. The damaging effect of O3 on future crop yield is affected by CO2 fertilization and climate change, and their interactions for RCP scenarios have yet to be quantified. In this study, we used the Joint UK Land Environment Simulator modified to include crops (JULES-crop) to quantify the impacts, and relative importance of present-day and future O3, CO2 concentration and meteorology on crop production at the regional scale until 2100 following RCP2.6 and RCP8.5 scenarios. We focus on eight major crop-producing regions that cover the production of wheat, soybean, maize, and rice. Our results show that CO2 alone has the largest effect on regional yields, followed by climate and O3. However, the CO2 fertilization effect is offset by the negative impact of tropospheric O3 in regions with high O3 concentrations, such as South Asia and China. Simulated crop yields in 2050 were compared with Food and Agriculture Organisation (FAO) statistics to investigate the differences between a socioeconomic and a biophysical process-based approach. Results showed that FAO estimates are closer to our JULES-crop RCP8.5 scenario. This study demonstrates that air pollution could be the biggest threat to future food production and highlights an urgent policy need to mitigate the threat of climate change and O3 pollution on food security.

Explicitly modelling microtopography in permafrost landscapes in a land surface model (JULES vn5.4_microtopography)

Abstract. Microtopography can be a key driver of heterogeneity in the ground thermal and hydrological regime of permafrost landscapes. In turn, this heterogeneity can influence plant communities, methane fluxes, and the initiation of abrupt thaw processes. Here we have implemented a two-tile representation of microtopography in JULES (the Joint UK Land Environment Simulator), where tiles are representative of repeating patterns of elevation difference. Tiles are coupled by lateral flows of water, heat, and redistribution of snow, and a surface water store is added to represent ponding. Simulations are performed of two Siberian polygon sites, (Samoylov and Kytalyk) and two Scandinavian palsa sites (Stordalen and Iškoras). The model represents the observed differences between greater snow depth in hollows vs. raised areas well. The model also improves soil moisture for hollows vs. the non-tiled configuration (“standard JULES”) though the raised tile remains drier than observed. The modelled differences in snow depths and soil moisture between tiles result in the lower tile soil temperatures being warmer for palsa sites, as in reality. However, when comparing the soil temperatures for July at 20 cm depth, the difference in temperature between tiles, or “temperature splitting”, is smaller than observed (3.2 vs. 5.5 ∘C). Polygons display small (0.2 ∘C) to zero temperature splitting, in agreement with observations. Consequently, methane fluxes are near identical (+0 % to 9 %) to those for standard JULES for polygons, although they can be greater than standard JULES for palsa sites (+10 % to 49 %). Through a sensitivity analysis we quantify the relative importance of model processes with respect to soil moisture and temperatures, identifying which parameters result in the greatest uncertainty in modelled temperature. Varying the palsa elevation between 0.5 and 3 m has little effect on modelled soil temperatures, showing that using only two tiles can still be a valid representation of sites with a range of palsa elevations. Mire saturation is heavily dependent on landscape-scale drainage. Lateral conductive fluxes, while small, reduce the temperature splitting by ∼ 1 ∘C and correspond to the order of observed lateral degradation rates in peat plateau regions, indicating possible application in an area-based thaw model.

Simulating Increased Permafrost Peatland Plant Productivity in Response to Belowground Fertilisation Using the JULES Land Surface Model

Several experimental studies have shown that climate-warming-induced permafrost thaw releases previously unavailable nitrogen which can lower nitrogen limitation, increase plant productivity, and counteract some of the carbon released from thawing permafrost. The net effect of this belowground fertilisation effect remains debated and is yet to be included in Earth System models. Here, we included the impact of thaw-related nitrogen fertilisation on vegetation in the Joint UK Land Environment Simulator (JULES) land surface model for the first time. We evaluated its ability to replicate a three-year belowground fertilisation experiment in which JULES was generally able to simulate belowground fertilisation in accordance with the observations. We also ran simulations under future climate to investigate how belowground nitrogen fertilisation affects the carbon cycle. These simulations indicate an increase in plant-available inorganic nitrogen at the thaw front by the end of the century with only the productivity of deep-rooting plants increasing in response. This suggests that deep-rooting species will have a competitive advantage under future climate warming. Our results also illustrate the capacity to simulate belowground nitrogen fertilisation at the thaw front in a global land surface model, leading towards a more complete representation of coupled carbon and nitrogen dynamics in the northern high latitudes.