Joint UK Land Environment Simulator Research Articles

Calibration and evaluation of JULES‐crop for maize in Brazil

AbstractMaize (Zea mays L.) is a prominent Brazilian commodity, being the second largest crop produced and fifth exported product by the country. Due to its importance for the agricultural sector, there is a concern about the effect of climate change on the crop. Process‐based models are valuable tools to evaluate the effects of climate on crop yields. The Joint UK Land Environment Simulator (JULES) is a land‐surface model that can be run with an integrated crop model parameterization. The resulting model (JULES‐crop) thus integrates crop physiology principles with the complexity of atmosphere–biosphere coupling. It has been shown to be a valuable tool for large‐scale simulations of crop yields as a function of environmental and management variables. In this study, we calibrated JULES‐crop using a robust experimental dataset collected for summer and off‐season maize fields across Brazil. A targeted local sensitivity analysis was performed to detect parameters of major importance during the calibration process. After calibration, the model was able to satisfactorily simulate both season and off‐season cultivars. Modeling efficiency (EF) was high for leaf area index (EF = .73 and .71, respectively, for summer season and off‐season datasets), crop height (EF = .89), and grain dry mass (EF = .61 and .89, respectively, for summer season and off‐season datasets). The model showed a lower accuracy for simulating leaf dry mass in summer season cultivars (EF = .39) and soil moisture (EF = .44), demonstrating the necessity of further improvements including additional parametrizations of the rainfed conditions.

Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one full simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we developed a statistical emulation framework for land surface models, enabling fast predictions at a high resolution. To do so, our emulation framework acknowledges, and makes use of, the multitude of contextual data that are often fed into land surface models (sometimes called forcing data, or driving data), such as air temperature or various soil properties. We use The Joint UK Land Environment Simulator (JULES) as a case study for this methodology, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. The JULES is perhaps one of the most complex land surface models and so our success here suggests incredible gains can be made for all types of land surface model.

Deficiencies of Phenology Models in Simulating Spatial and Temporal Variations in Temperate Spring Leaf Phenology

AbstractSpring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. Based on long‐term (period 1963–2014) in situ observations of budburst date and leaf unfolding date of more than 300 deciduous woody species from 32 sites across the temperate zone in China, we conducted model‐data comparison of spatial and temporal variations for spring leaf phenology calculated using the phenology modules that were embed into 10 existing terrestrial ecosystem models. Our results suggested that ORganizing Carbon and Hydrology in Dynamic EcosystEms and Spatially Explicit Individual‐Based performed the best in reproducing the spatial patterns of spring leaf phenology, but tended to underestimate the temporal variations in responding to temperature warming, showing low interannual variability (IAV) and temperature sensitivity (ST). In contrast, the performances of Vegetation Integrated SImulator for Trace Gases were the best in modeling IAV and ST. BIOME3, Lund‐Potsdam‐Jena model, Joint UK Land Environment Simulator, BioGeochemical Cycles, Community Land Model, Integrated Biosphere Simulator, and Commonwealth Scientific and Industrial Research Organisation Atmosphere Biosphere Land Exchange Model failed to reproduce both the spatial and temporal patterns. Using temperature series (1960–2100) form Coupled Model Intercomparison Project Number 6 scenarios to force the 10 phenology modules, our results highlighted large uncertainties in predicting spring leaf phenology changes with the warming climate, and more work is required to deal with the deficiencies of phenology model parameters and algorithms.

A new approach to simulate peat accumulation, degradation and stability in a global land surface scheme (JULES vn5.8_accumulate_soil) for northern and temperate peatlands

Abstract. Peatlands have often been neglected in Earth system models (ESMs). Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material) and purely organic soil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrum of soil types nor their dynamic nature can be captured by current ESMs. Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation – and thus peatland formation, degradation and stability – is integrated in the vertically resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES and compare this to measured peat age–depth profiles. The new scheme is tested and evaluated at northern and temperate sites. This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss, soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage. These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience. The new model produces similar results to the original model for mineral soils and realistic profiles of soil organic carbon for peatlands. We evaluate the model against typical peat profiles based on 216 northern and temperate sites from a global dataset of peat cores. The root-mean-squared error (RMSE) in the soil carbon profile is reduced by 35 %–80 % in the best-performing JULES-Peat simulations compared with the standard JULES configuration. The RMSE in these JULES-Peat simulations is 7.7–16.7 kg C m−3 depending on climate zone, which is considerably smaller than the soil carbon itself (around 30–60 kg C m−3). The RMSE at mineral soil sites is also reduced in JULES-Peat compared with the original JULES configuration (reduced by ∼ 30 %–50 %). Thus, JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not require any additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils and associated carbon-cycle feedbacks in ESMs.

Assessing vulnerability of agriculture system to climate change in the SAARC region

Agriculture production is primarily influenced by human-induced management interventions whereas climate plays an important role. Climate change with increasing global carbon dioxide concentration and changes in temperature, precipitation and other climatic parameters play a significant role in influencing agriculture productivity. The assessment of the vulnerability of agriculture production under the influence of climate change can be done using process-based vegetation models. We assessed the vulnerability of agriculture production for the South Asian Association for Regional Cooperation (SAARC) nations comprising of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka using the Joint UK Land Environment Simulator (JULES) model. Net Primary Productivity (NPP) was simulated under the forcing of climatic variables of the climate change scenarios of representative concentration pathways (RCP2.6, 4.5 & 8.5) for the year 2050 using JULES. JULES is a process-based model that simulates fluxes of carbon, energy, and momentum between the atmosphere, land surface, and water to estimate the productivity of a system. The sensitivity was calculated as the inter-annual dispersion from the average NPP of assessment period 2016 to 2050, whereas adaptive capacity was calculated as the slope of productivity regressed over the assessment period. The vulnerability was calculated by subtracting the values of adaptability from sensitivity. The JULES simulated NPP was also compared with Moderate Resolution Imaging Spectrometer (MODIS 17A) derived NPP for the period 2000–2014. The simulated NPP had a decreasing trend under projected climate change scenarios of 2050 for all the SAARC nations compared to recent years average of 2000–2014. The analysis reveals that one-third of the SAARC region is highly sensitive under low to moderate emission scenarios, while it was observed that under high emission scenarios the sensitivity would reduce with increased vulnerability. We observed that the vulnerability of the system is not alone influenced by sensitivity and any change in adaptive capacity or resilience would also impact the overall vulnerability of the system.

Impact of merging of historical and future climate data sets on land carbon cycle projections for South America

AbstractEarth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non‐linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias‐correct the UK Met Office ESM, HadGEM2‐ES, against the CRU–JRA observation‐based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2‐ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day−1, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO2 flux becomes a source to the atmosphere by the end of this century. Transitions to a CO2 source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias‐corrected simulations estimate the rise in South American land carbon stocks between pre‐industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate‐induced role of greater soil decomposition in the fate of the Amazon carbon sink.

On the Treatment of Soil Water Stress in GCM Simulations of Vegetation Physiology

Current land surface schemes in weather and climate models make use of the so-called coupled photosynthesis–stomatal conductance (A–gs) models of plant function to determine the surface fluxes that govern the terrestrial energy, water and carbon budgets. Plant physiology is controlled by many environmental factors, and a number of complex feedbacks are involved, but soil moisture control on root water uptake is primary, particularly in sub-tropical to temperate ecosystems. Land surface models represent plant water stress in different ways, but most implement a water stress factor, β, which ranges linearly (more recently also curvilinearly) between β = 1 for unstressed vegetation and β = 0 at the wilting point, expressed in terms of volumetric water content (θ). β is most commonly used to either limit A or gs, and hence carbon and water fluxes, and a pertinent research question is whether these treatments are in fact interchangeable. Following Egea et al. (Agricultural and Forest Meteorology, 2011, 151 (10), 1,370–1,384) and Verhoef et al. (Agricultural and Forest Meteorology, 2014, 191, 22–32), we have implemented new β treatments, reflecting higher levels of biophysical complexity in a state-of-the-art LSM, Joint UK Land Environment Simulator, by allowing root zone soil moisture to limit plant function non-linearly and via individual routes (carbon assimilation, stomatal conductance, or mesophyll conductance) as well as any (non-linear) combinations thereof. The treatment of β does matter to the prediction of water and carbon fluxes: this study demonstrates that it represents a key structural uncertainty in contemporary LSMs, in terms of predictions of gross primary productivity, energy fluxes and soil moisture evolution, both in terms of climate means and response to a number of European droughts, including the 2003 heat wave. Treatments allowing ß to act on vegetation fluxes via stomatal and mesophyll routes are able to simulate the spatiotemporal variability in water use efficiency with higher fidelity during the growing season; they also support a broader range of ecosystem responses, e.g., those observed in regions that are radiation limited or water limited. We conclude that current practice in weather and climate modelling is inconsistent, as well as too simplistic, failing to credibly simulate vegetation response to soil water stress across the typical range of variability that is encountered for current European weather and climate conditions, including extremes of land surface temperature and soil moisture drought. A generalized approach performs better in current climate conditions and promises to be, based on responses to recently observed extremes, more trustworthy for predicting the impacts of climate change.

Evaluating two land surface models for Brazil using a full carbon cycle benchmark with uncertainties

AbstractForecasts of tropical ecosystem C cycling diverge among models due to differences in simulation of internal processes such as turnover, or transit times, of carbon pools. Estimates of these processes for the recent past are needed to test model representations, and so build confidence in model forecasts within and across biomes. Here, we evaluate carbon cycle process representation in two land surface models [Joint UK Land Environment Simulator (JULES) and Integrated Model of Land Surface Processes (INLAND)] for the period 2001–10 across Brazilian biomes. Model outputs are evaluated using the ILAMB system. Probabilistic benchmarking data were created using the carbon data model framework that assimilates observational times series of leaf area index and maps of woody biomass and soil C. New custom uncertainty metrics assess if models are within benchmark uncertainties. Simulations are better in homogeneous areas of vegetation type, and are less robust at ecotones between biomes, likely due to disturbance effects and parameter errors. Gross biosphere‐atmosphere fluxes are robustly modelled across Brazil. However, benchmark uncertainty is too high on net ecosystem exchange to provide an accurate evaluation of the models. The LSMs have significant differences in internal carbon allocation and the dynamics of the different C pools. JULES models dead C stocks more accurately while living C stocks are best resolved for INLAND. JULES' over‐estimate of the C wood pool results from over‐estimation of both inputs to wood and the transit time of wood. INLAND's under‐estimate of dead C stocks arises from an under‐estimate of the transit time of dead organic matter. The models are better at simulating annual averages than seasonal variation of fluxes. Analyses of monthly net C exchanges show that INLAND correctly simulates seasonality, but over‐estimates amplitudes, whereas JULES correctly simulates the annual amplitudes, but is out of phase with the benchmark.

Importance of including soil moisture in drought monitoring over the Brazilian semiarid region: An evaluation using the JULES model, in situ observations, and remote sensing

AbstractSoil moisture information is essential to monitoring of the intensity of droughts, the start of the rainy season, planting dates and early warnings of yield losses. We assess spatial and temporal trends of drought over the Brazilian semiarid region by combining soil moisture observations from 360 stations, root zone soil moisture from a leading land surface model, and a vegetation health index from remote sensing. The soil moisture dataset was obtained from the network of stations maintained by the National Center of Monitoring and Early Warning of Natural Disasters (Cemaden), in Brazil. Soil water content at 10 to 35 cm depth, for the period 1979–2018, was obtained from running the JULES land surface model (the Joint UK Land Environment Simulator). The modelled soil moisture was correlated with measurements in the common period of 2015–2018, resulting in an average correlation coefficient of 0.48 across the domain. The standardized soil moisture anomaly (SMA) was calculated for the long‐term modelled soil moisture and revealed strong negative values during well‐known drought periods in the region, especially during El‐Niño years. The performance of SMA in identifying droughts during the first 2 months of the raining and cropping season was similar to the Standardized Precipitation Index (SPI), commonly used for drought assessment: 12–14 events were identified by both indices. Finally, the temporal relationship between both SMA and SPI with the Vegetation Health Index (VHI) was assessed using the cross‐wavelet transform. The results indicated lagged correlations of 1 to 1.5 months in the annual scale, suggesting that negative trends in SMA and SPI can be an early warning to yield losses during the growing season. Public policies on drought assessment should consider the combination of multiple drought indices, including soil moisture anomaly.

Quantifying the observational requirements of a space-borne LiDAR snow mission

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements

Abstract. Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.

Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections.

JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1)

Abstract. Understanding future changes in the terrestrial carbon cycle is important for reliable projections of climate change and impacts on ecosystems. It is well known that nitrogen (N) could limit plants' response to increased atmospheric carbon dioxide and it is therefore important to include a representation of the N cycle in Earth system models. Here we present the implementation of the terrestrial nitrogen cycle in the Joint UK Land Environment Simulator (JULES) – the land surface scheme of the UK Earth System Model (UKESM). Two configurations are discussed – the first one (JULES-CN) has a bulk soil biogeochemical model and the second one is a development configuration that resolves the soil biogeochemistry with depth (JULES-CNlayer). In JULES the nitrogen (N) cycle is based on the existing carbon (C) cycle and represents all the key terrestrial N processes in a parsimonious way. Biological N fixation is dependent on net primary productivity, and N deposition is specified as an external input. Nitrogen leaves the vegetation and soil system via leaching and a bulk gas loss term. Nutrient limitation reduces carbon-use efficiency (CUE – ratio of net to gross primary productivity) and can slow soil decomposition. We show that ecosystem level N limitation of net primary productivity (quantified in the model by the ratio of the potential amount of C that can be allocated to growth and spreading of the vegetation compared with the actual amount achieved in its natural state) falls at the lower end of the observational estimates in forests (approximately 1.0 in the model compared with 1.01 to 1.38 in the observations). The model shows more N limitation in the tropical savanna and tundra biomes, consistent with the available observations. Simulated C and N pools and fluxes are comparable to the limited available observations and model-derived estimates. The introduction of an N cycle improves the representation of interannual variability of global net ecosystem exchange, which was more pronounced in the C-cycle-only versions of JULES (JULES-C) than shown in estimates from the Global Carbon Project. It also reduces the present-day CUE from a global mean value of 0.45 for JULES-C to 0.41 for JULES-CN and 0.40 for JULES-CNlayer, all of which fall within the observational range. The N cycle also alters the response of the C fluxes over the 20th century and limits the CO2 fertilisation effect, such that the simulated current-day land C sink is reduced by about 0.5 Pg C yr−1 compared to the version with no N limitation. JULES-CNlayer additionally improves the representation of soil biogeochemistry, including turnover times in the northern high latitudes. The inclusion of a prognostic land N scheme marks a step forward in functionality and realism for the JULES and UKESM models.

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Temporal variability in the impacts of particulate matter on crop yields on the North China Plain

The North China Plain (NCP) is a major agricultural region, producing 45% of China's maize. It is also vital to the Chinese economy, encompassing the Beijing-Tianjin-Hebei megacity region. Anthropogenic factors increasingly impact crop yields on the NCP, and globally. Particulate matter (PM) pollution is a significant problem in this region, where annual average PM concentrations over three times the Chinese national air quality standard were recorded for the Beijing-Tianjin-Hebei megacity region between 2013 and 18. PM absorbs light, reducing total shortwave radiation (SW), thereby limiting plant productivity. However, PM also scatters incoming SW, increasing the diffuse fraction, which has been shown to increase growth and biomass assimilation.The Joint UK Land Environment Simulator (JULES) crop model was used to assess the net impact of these competing changes in light on NCP maize yields. In contrast to some previous analyses, we find that PM-associated decreases in SW outweigh any positive impact on yield from an increasing proportion of diffuse radiation.Furthermore, carbon allocation to different portions of the growing cropchanges during the development cycle. We find significant differences between the effect on final yield of identical changes to diffuse fraction and total SW occurring during different development stages. The greatest simulated yield gains from increased SW and reduced diffuse fraction, consistent with reductions in PM, are observed during the early reproductive stage of development (July–August), when the simulated gain of yield is as much as 12.9% more than in other periods.To further assess the impact of PM-linked changes in SW and diffuse fraction on NCP crop yields, radiation profiles from different city regions were then applied across the NCP. The changes in SW associated with these city regions could increase maize yields across China by ~8 Mt. This would completely offset China's annual maize imports, increasing both national and global food security.

Implementation of sequential cropping into JULESvn5.2 land-surface model

Abstract. Land-surface models (LSMs) typically simulate a single crop per year in a field or location. However, actual cropping systems are characterized by a succession of distinct crop cycles that are sometimes interspersed with long periods of bare soil. Sequential cropping (also known as multiple or double cropping) is particularly common in tropical regions, where the crop seasons are largely dictated by the main wet season. In this paper, we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at sites in France and India. We simulate all the crops grown within a year in a field or location in a seamless way to understand how sequential cropping influences the surface fluxes of a land-surface model. We evaluate JULES with sequential cropping in Avignon, France, providing over 15 years of continuous flux observations (a point simulation). We apply JULES with sequential cropping to simulate the rice–wheat rotation in a regional 25 km resolution gridded simulation for the northern Indian states of Uttar Pradesh and Bihar and four single-grid-box simulations across these states, where each simulation is a 25 km grid box. The inclusion of a secondary crop in JULES using the sequential cropping method presented does not change the crop growth or development of the primary crop. During the secondary crop growing period, the carbon and energy fluxes for Avignon and India are modified; they are largely unchanged for the primary crop growing period. For India, the inclusion of a secondary crop using this sequential cropping method affects the available soil moisture in the top 1.0 m throughout the year, with larger fluctuations in sequential crops compared with single-crop simulations even outside the secondary crop growing period. JULES simulates sequential cropping in Avignon, the four India locations and the regional run, representing both crops within one growing season in each of the crop rotations presented. This development is a step forward in the ability of JULES to simulate crops in tropical regions where this cropping system is already prevalent. It also provides the opportunity to assess the potential for other regions to implement sequential cropping as an adaptation to climate change.

Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter

A land data assimilation system is developed to merge satellite soil moisture retrievals into the Joint U.K. Land Environment Simulator (JULES) land surface model (LSM) using the Local Ensemble Transform Kalman Filter (LETKF). The system assimilates microwave soil moisture retrievals from the Soil Moisture Active Passive (SMAP) radiometer and the Advanced Scatterometer (ASCAT) after bias correction based on cumulative distribution function fitting. The soil moisture assimilation estimates are evaluated with ground-based soil moisture measurements over the continental U.S. for five consecutive warm seasons (May–September of 2015–2019). The result shows that both SMAP and ASCAT retrievals improve the accuracy of soil moisture estimates. Especially, the SMAP single-sensor assimilation experiment shows the best performance with the increase of temporal anomaly correlation by ΔR ~ 0.05 for surface soil moisture and ΔR ~ 0.03 for root-zone soil moisture compared with the LSM simulation without satellite data assimilation. SMAP assimilation is more skillful than ASCAT assimilation primarily because of the greater skill of the assimilated SMAP retrievals compared to the ASCAT retrievals. The skill improvement also depends significantly on the region; the higher skill improvement in the western U.S. compared to the eastern U.S. is explained by the Kalman gain in the two experiments. Additionally, the regional skill differences in the single-sensor assimilation experiments are attributed to the number of assimilated observations. Finally, the soil moisture assimilation estimates provide more realistic land surface information than model-only simulations for the 2015 and the 2016 western U.S. droughts, suggesting the advantage of using satellite soil moisture retrievals in the current drought monitoring system.

Calibrating soybean parameters in JULES 5.0 from the US-Ne2/3 FLUXNET sites and the SoyFACE-O<sub>3</sub> experiment

Abstract. Tropospheric ozone (O3) is the third most important anthropogenic greenhouse gas. O3 is detrimental to plant productivity, and it has a significant impact on crop yield. Currently, the Joint UK Land Environment Simulator (JULES) land surface model includes a representation of global crops (JULES-crop) but does not have crop-specific O3 damage parameters and applies default C3 grass O3 parameters for soybean that underestimate O3 damage. Physiological parameters for O3 damage in soybean in JULES-crop were calibrated against leaf gas-exchange measurements from the Soybean Free Air Concentration Enrichment (SoyFACE) with O3 experiment in Illinois, USA. Other plant parameters were calibrated using an extensive array of soybean observations such as crop height and leaf carbon and meteorological data from FLUXNET sites near Mead, Nebraska, USA. The yield, aboveground carbon, and leaf area index (LAI) of soybean from the SoyFACE experiment were used to evaluate the newly calibrated parameters. The result shows good performance for yield, with the modelled yield being within the spread of the SoyFACE observations. Although JULES-crop is able to reproduce observed LAI seasonality, its magnitude is underestimated. The newly calibrated version of JULES will be applied regionally and globally in future JULES simulations. This study helps to build a state-of-the-art impact assessment model and contribute to a more complete understanding of the impacts of climate change on food production.

Urban signals in high-resolution weather and climate simulations: role of urban land-surface characterisation

Two urban schemes within the Joint UK Land Environment Simulator (JULES) are evaluated offline against multi-year flux observations in the densely built-up city centre of London and in suburban Swindon (UK): (i) the 1-tile slab model, used in climate simulations; (ii) the 2-tile canopy model MORUSES (Met Office–Reading Urban Surface Exchange Scheme), used for numerical weather prediction over the UK. Offline, both models perform better at the suburban site, where differences between the urban schemes are less pronounced due to larger vegetation fractions. At both sites, the outgoing short- and longwave radiation is more accurately represented than the turbulent heat fluxes. The seasonal variations of model skill are large in London, where the sensible heat flux in autumn and winter is strongly under-predicted if the large city centre magnitudes of anthropogenic heat emissions are not represented. The delayed timing of the sensible heat flux in the 1-tile model in London results in large negative bias in the morning. The partitioning of the urban surface into canyon and roof in MORUSES improves this as the roof tile is modelled with a very low thermal inertia, but phase and amplitude of the grid box-averaged flux critically depend on accurate knowledge of the plan-area fractions of streets and buildings. Not representing non-urban land cover (e.g. vegetation, inland water) in London results in severely under-predicted latent heat fluxes. Control runs demonstrate that the skill of both models can be greatly improved by providing accurate land cover and morphology information and using representative anthropogenic heat emissions, which is essential if the model output is intended to inform integrated urban services.

The impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to drought

Abstract. Accurately representing the response of ecosystems to environmental change in land surface models (LSMs) is crucial to making accurate predictions of future climate. Many LSMs do not correctly capture plant respiration and growth fluxes, particularly in response to extreme climatic events. This is in part due to the unrealistic assumption that total plant carbon expenditure (PCE) is always equal to gross carbon accumulation by photosynthesis. We present and evaluate a simple model of labile carbon storage and utilisation (SUGAR) designed to be integrated into an LSM, which allows simulated plant respiration and growth to vary independent of photosynthesis. SUGAR buffers simulated PCE against seasonal variation in photosynthesis, producing more constant (less variable) predictions of plant growth and respiration relative to an LSM that does not represent labile carbon storage. This allows the model to more accurately capture observed carbon fluxes at a large-scale drought experiment in a tropical moist forest in the Amazon, relative to the Joint UK Land Environment Simulator LSM (JULES). SUGAR is designed to improve the representation of carbon storage in LSMs and provides a simple framework that allows new processes to be integrated as the empirical understanding of carbon storage in plants improves. The study highlights the need for future research into carbon storage and allocation in plants, particularly in response to extreme climate events such as drought.