Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010]. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0.5 • × 0.5 • spatial resolution), the annual average global GPP simulated by JULES for 2001-2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CAR-DAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0.5 • × 0.5 • , 1 • × 1 • and 2 • × 2 • ) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year −1 . Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year −1 . On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5 • N-5 • S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30-60 • N).

Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. Recent studies have highlighted a poor understanding particularly of C pool transit times and of whether productivity or biomass dominate these biases. The Arctic, accounting for approximately 50 % of the global soil organic C stocks, has an important role in the global C cycle. Here, we use the CARbon DAta MOdel (CARDAMOM) data-assimilation system to produce pan-Arctic terrestrial C cycle analyses for 2000–2015. This approach avoids using traditional plant functional type or steady-state assumptions. We integrate a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely state of the high-latitude C cycle at a 1∘ × 1∘ resolution and also to provide general guidance about the controlling biases in transit times. On average, CARDAMOM estimates regional mean rates of photosynthesis of 565 g C m−2 yr−1 (90 % confidence interval between the 5th and 95th percentiles: 428, 741), autotrophic respiration of 270 g C m−2 yr−1 (182, 397) and heterotrophic respiration of 219 g C m−2 yr−1 (31, 1458), suggesting a pan-Arctic sink of −67 (−287, 1160) g Cm−2 yr−1, weaker in tundra and stronger in taiga. However, our confidence intervals remain large (and so the region could be a source of C), reflecting uncertainty assigned to the regional data products. We show a clear spatial and temporal agreement between CARDAMOM analyses and different sources of assimilated and independent data at both pan-Arctic and local scales but also identify consistent biases between CARDAMOM and validation data. The assimilation process requires clearer error quantification for leaf area index (LAI) and biomass products to resolve these biases. Mapping of vegetation C stocks and change over time and soil C ages linked to soil C stocks is required for better analytical constraint. Comparing CARDAMOM analyses to global vegetation models (GVMs) for the same period, we conclude that transit times of vegetation C are inconsistently simulated in GVMs due to a combination of uncertainties from productivity and biomass calculations. Our findings highlight that GVMs need to focus on constraining both current vegetation C stocks and net primary production to improve a process-based understanding of C cycle dynamics in the Arctic.

Abstract. There is a significant knowledge gap in the current state of the terrestrial carbon (C) budget. The Arctic accounts 15 for approximately 50% of the global soil organic C stock, emphasizing the important role of Arctic regions in the global C cycle. Recent studies have pointed to the poor understanding of C pools turnover, although remain unclear as to whether productivity or biomass dominate the biases. Here, we use an improved version of the CARDAMOM data-assimilation system, to produce pan-Arctic terrestrial C-related variables without using traditional plant functional type or steady-state assumptions.Our approach integrates a range of data (soil organic C, leaf area index, biomass, and climate) to determine the most likely 20 state of the high latitude C cycle at a 1° x 1° resolution for the first 15 years of the 21 st century, but also to provide general guidance about the controlling biases in the turnover dynamics. As average, CARDAMOM estimates 513 (456, 579), 245(208, 290) and 204 (109, 427) g C m -2 yr -1 (90% confidence interval) from photosynthesis, autotrophic and heterotrophic respiration respectively, suggesting that the pan-Arctic region acted as a likely sink -55 (-152, 157) g C m -2 yr -1 , weaker in tundra and stronger in taiga, but our confidence intervals remain large (and so the region could be a source of C). In general, 25we find a good agreement between CARDAMOM and different sources of assimilated and independent data at both pan-Arctic and local scale. Using CARDAMOM as a benchmarking tool for global vegetation models (GVM), we also conclude that turnover time of vegetation C is weakly simulated in vegetation models and is a major component of error in their forecasts.Our findings highlight that GVM modellers need to focus on the vegetation C stocks dynamics, but also their respiratory losses, to improve our process-based understanding of internal C cycle dynamics in the Arctic. 30Earth Syst. Dynam. Discuss., https://doi

The climate sensitivity of carbon (C) cycling in Arctic terrestrial ecosystems is a major unknown in the Earth system. There is a lack of knowledge about the mechanisms that drive the interactions between photosynthesis, respiration, and changes in C stocks across full annual cycles in Arctic tundra. We use a calibrated and validated model (soil-plant-atmosphere; SPA) to estimate net ecosystem exchange (NEE), gross primary production (GPP), ecosystem respiration (R eco ), and internal C processing across eight full years. SPA's carbon flux estimates are validated with observational data obtained from the Greenland Ecosystem Monitoring program in West Greenland tundra. Overall, the model explained 73%, 73%, and 50% of the variance in NEE, GPP, and R eco , respectively, and 85% of the plant greenness variation. Flux data highlighted the insensitivity of growing season NEE to interannual meteorological variability, due to compensatory responses of photosynthesis and ecosystem respiration. In this modelling study, we show that this NEE buffering is the case also for full annual cycles. We show through a sensitivity analysis that plant traits related to nitrogen are likely key determinants in the compensatory response, through simulated links to photosynthesis and plant respiration. Interestingly, we found a similar temperature sensitivity of the trait-flux couplings for GPP and R eco , suggesting that plant traits drive the stabilization of NEE. Further, model analysis indicated that wintertime periods decreased the C sink by 60%, mostly driven by litter heterotrophic respiration. This result emphasizes the importance of wintertime periods and allows a more comprehensive understanding of full annual C dynamics.

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16 % across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30 % across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7 % reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.

Abstract. Drought is predicted to increase in the future due to climate change, bringing with it a myriad of 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/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 ten different representations of stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high latitudes/cold region sites, while LE was best simulated in temperate and high latitude/cold sites. Errors not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savannah 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 3m to 10.8m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation, when the onset of stress was delayed, and when roots extended deeper into the soil. 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 made the simulation worse. Further evaluation into 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.

Abstract. Changes in atmospheric carbon dioxide and water vapour change the energy balance of the atmosphere and thus climate. One important influence on these greenhouse gases is the land surface. Land Surface Models (LSMs) represent the interaction between the atmosphere and terrestrial biosphere in Global Climate Models (GCMs). As LSMs become more advanced, there is a need to test their accuracy. Uncertainty from LSMs contributes towards uncertainty in carbon cycle simulations and thus uncertainty in future climate change. In this study, we evaluate the ability of the JULES LSM to simulate photosynthesis using local and global datasets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, we compare Gross Primary Productivity (GPP) estimates from driving JULES with data derived from local site measurements with driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so). We find that when using local data, a negative bias is introduced into model simulations with yearly GPP underestimated by 16% on average compared to observations while when using global data, model performance decreases further with yearly GPP underestimated by 30% on average. Secondly, we drive the model using global meteorological data and local parameters and find that global data can be used in place of FLUXNET data with only a 7% reduction in total annual simulated GPP. Thirdly, we compare the global meteorological datasets, WFDEI and PRINCETON, to local data and find that the WATCH dataset more closely matches the local meteorological measurements (FLUXNET). Finally, we compare the results from forcing JULES with the remote sensing product MODIS Leaf Area Index (LAI). JULES was modified to accept MODIS LAI at daily timesteps. We show that forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites compared to using the default phenology model.

Abstract. This study evaluates the ability of the JULES Land Surface Model (LSM) to simulate Gross Primary Productivity (GPP) at regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, JULES was found to simulate interannual variability (IAV) at global scales. When JULES was driven with the WFDEI-GPCC dataset (at 0.5º × 0.5º spatial resolution), it was found that the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 % and 8 %, respectively, and CARDAMOM estimates by 23 %. Secondly, GPP fluxes simulated by JULES for various biomes (forests, grasslands and shrubs) at global and regional scales were compared. It was found that differences between JULES, FLUXNET-MTE, MODIS and CARDAMOM at global scales were mostly due to differences in the tropics with CARDAMOM performing better than JULES in this region. Thirdly, it was shown that spatial resolution (0.5º × 0.5º, 1º × 1º and 2º × 2º) had no impact on simulated GPP on these large scales. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 4 PgC year−1. At regional scales, differences between two were observed with the WFDEI-GPCC driven model simulations estimating higher GPP in the tropics (at 5º N–5º S) and the PRINCETON driven model simulations estimating higher GPP in the extratropics (at 30º N–60º N).