Abstract. Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite retrieved albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE land surface model for three random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model-data fit by reducing the RMSD by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters encouraging future work using multiple data streams for optimisation.
<p>Lakes play a major role on local climate and boundary layer stratification. At global scale, they have been shown to have an impact on the energy budget, (see for example Le Moigne et al., 2016 or Bonan, 1995 ) . To represent the energy budget of lakes at a global scale, the FLake (Mironov et al, 2008) lake model has been coupled to the ORCHIDEE land surface model - the continental part of the IPSL earth system model. By including Flake in ORCHIDEE, we aim to improve the representation of land surface temperature and heat fluxes. Using the standard CMIP6 configuration of ORCHIDEE,&#160; two 40-year simulations were generated (one coupled with FLake and one without) using the CRUJRA meteorological forcing data at a spatial resolution of 0.5&#176;. We compare land surface temperatures and heat fluxes from the two ORCHIDEE simulations and assess the impacts of lakes on surface energy budgets. MODIS satellite land surface temperature products will be used to validate the simulations. We expect a better fit between the simulated land surface temperature and the MODIS data when the FLake configuration is used. The preliminary results of the comparison will be presented.</p>
Abstract. Current climate warming is accelerating mass loss from glaciers and ice sheets. In Greenland, the rates of mass changes are now dominated by changes in surface mass balance (SMB) due to increased surface melting. To improve the future sea-level rise projections, it is therefore critical to have an accurate estimate of the SMB, which depends on the representation of the processes occurring within the snowpack. The Explicit Snow (ES) scheme implemented in the land surface model Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) has not yet been adapted to ice-covered areas. Here, we present the preliminary developments we made to apply the ES model to glaciers and ice sheets. Our analysis mainly concerns the model's ability to represent ablation-related processes. At the regional scale, our results are compared to the MAR regional atmospheric model outputs and to MODIS albedo retrievals. Using different albedo parameterizations, we performed offline ES simulations forced by the MAR model over the 2000–2019 period. Our results reveal a strong sensitivity of the modelled SMB components to the albedo parameterization. Results inferred with albedo parameters obtained using a manual tuning approach present very good agreement with the MAR outputs. Conversely, with the albedo parameterization used in the standard ORCHIDEE version, runoff and sublimation were underestimated. We also tested parameters found in a previous data assimilation experiment, calibrating the ablation processes using MODIS snow albedo. While these parameters greatly improve the modelled albedo over the entire ice sheet, they degrade the other model outputs compared to those obtained with the manually tuned approach. This is likely due to the model overfitting to the calibration albedo dataset without any constraint applied to the other processes controlling the state of the snowpack. This underlines the need to perform a “multi-objective” optimization using auxiliary observations related to internal snowpack processes. Although there is still room for further improvements, the developments reported in the present study constitute an important advance in assessing the Greenland SMB with possible extension to mountain glaciers or the Antarctic ice sheet.
Abstract. Predicting the responses of terrestrial ecosystem carbon to future global change strongly relies on our ability to model accurately the underlying processes at a global scale. However, terrestrial biosphere models representing the carbon and nitrogen cycles and their interactions remain subject to large uncertainties, partly because of unknown or poorly constrained parameters. Data assimilation is a powerful tool that can be used to optimise these parameters by confronting the model with observations. In this paper, we identify sensitive model parameters from a recent version of the ORCHIDEE land surface model that includes the nitrogen cycle. These sensitive parameters include ones involved in parameterisations controlling the impact of the nitrogen cycle on the carbon cycle and, in particular, the limitation of photosynthesis due to leaf nitrogen availability. We optimise these ORCHIDEE parameters against carbon flux data collected on sites from the Fluxnet network. However, optimising against present-day observations does not automatically give us confidence in the future projections of the model, given that environmental conditions are likely to shift compared to present-day. Manipulation experiments give us a unique look into how the ecosystem may respond to future environmental changes. One such experiment, the Free Air CO2 Enrichment experiment, provides a unique opportunity to assess vegetation response to increasing CO2 by providing data at ambient and elevated CO2 conditions. Therefore, to better capture the ecosystem response to increased CO2, we add the data from two FACE sites to our optimisations, in addition to the Fluxnet data. We use data from both CO2 conditions of the Free Air CO2 Enrichment experiment, which allows us to gain extra confidence in the model simulations using this set of parameters. We find that we are able to improve the magnitude of modelled productivity. Although we are unable to correct the interannual variability fully, we start to simulate possible progressive nitrogen limitation at one of the sites. Using an idealised simulation experiment based on increasing atmospheric CO2 by 1 % per year over 100 years, we find that the rate of CO2 fertilisation is much lower when Free Air CO2 Enrichment data has been in the optimisation.
Abstract. Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite-retrieved snow albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) land surface model for 3 random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet, since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model–data fit by reducing the root-mean-square deviation by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We also show the optimisation successfully improves the model–data fit at 87.5 % of in situ sites from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) network. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters, encouraging future work using multiple data streams when optimising these parameters.
Abstract. Land surface temperature (LST) plays an essential role in water and energy exchanges between Earth’s surface and atmosphere. Recent advancements in high-quality satellite-derived LST data and land data assimilation systems present a unique opportunity to bridge the gap between global observational data and land surface models (LSMs) to better constrain the water/energy budgets in a changing climate. In this vein, this study focuses on the assimilation of the ESA-CCI LST product into the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) with the aim of optimizing key parameters to improve the simulations of LST and surface energy fluxes. We use the land data assimilation system for the ORCHIDEE model (ORCHIDAS) to conduct a series of synthetic twin data assimilation experiments accounting for real data availability and uncertainty from ESA-CCI LST to find an optimal strategy for assimilating LST. Here, we test different strategies of assimilation, notably investigating: i) two optimization methods (random-search and gradient-based) and ii) different ways to assimilate LST using the only raw data and/or different characteristics of the LST diurnal cycle (e.g. mean daily, daily amplitude, maximum and minimum temperatures, morning and afternoon gradients). Upon identifying the optimal approach, we use it to assimilate ESA-CCI LST data across 34 sites in Europe provided by the WarmWinter database. Our results demonstrate the effectiveness of assimilating 3-hourly CCI-LST data over a single year in 2018, improving the accuracy of simulated LST and fluxes. This improvement, assessed against CCI-LST and in situ observations, reaches up to 60 % reduction in root mean square deviation, with a median reduction of 20 % over the entire validation period (2009–2020). Furthermore, we evaluate the effectiveness of optimized parameters for application at larger scales by using the median of optimized parameters per vegetation type across sites. Notably, the performances for both LST and fluxes exhibit not only consistent stability over the years, comparable to using site-specific parameters, but also indicate a significant improvement in the modeled fluxes. Future works will be focused on refining the utilization of the observation uncertainties provided by the ESA-CCI LST product (e.g. decomposed uncertainties and spatio-temporal variability) in the assimilation process.
Abstract. With the growing complexity of land surface models used to represent the terrestrial part of wider Earth system models, the need for sophisticated and robust parameter optimisation techniques is paramount. Quantifying parameter uncertainty is essential for both model development and more accurate projections. In this study, we assess the power of history matching by comparing results to variational data assimilation, commonly used in land surface models for parameter estimation. Although both approaches have different setups and goals, we can extract posterior parameter distributions from both methods and test the model-data fit of ensembles sampled from these distributions. Using a twin experiment, we test whether we can recover known parameter values. Through variational data assimilation, we closely match the observations. However, the known parameter values are not always contained in the posterior parameter distribution, highlighting the equifinality of the parameter space. In contrast, while more conservative, history matching still gives a reasonably good fit and provides more information about the model structure by allowing for non-Gaussian parameter distributions. Furthermore, the true parameters are contained in the posterior distributions. We then consider history matching's ability to ingest different metrics targeting different physical parts of the model, helping to reduce parameter space further and improve model-data fit. We find the best results when history matching is used with multiple metrics; not only is the model-data fit improved, but we also gain a deeper understanding of the model and how the different parameters constrain different parts of the seasonal cycle. We conclude by discussing the potential of history matching in future studies.
Abstract. Land-atmosphere (L-A) interactions encompass the co-evolution of the land surface and overlying planetary boundary layer, primarily during daylight hours. However, many studies have been conducted using monthly or entire-day-mean time series due to the lack of sub-daily data. It has been unclear whether the inclusion of nighttime data alters the assessment of L-A coupling or obscures L-A interactive processes. To address this question, we generate monthly (M), entire-day-mean (E), and daytime-only-mean (D) data based on the ERA5 (5th European Centre for Medium-Range Weather Forecasts reanalysis) product, and evaluate the strength of L-A coupling through two-legged metrics, which partition the impact of the land states on surface fluxes (the land leg) from the impact of surface fluxes on the atmospheric states (the atmospheric leg). Here we show that the spatial patterns of strong L-A coupling regions among the M-, D- and E-based diagnoses can differ by as much as 84.8 %. The signal loss from E- to M-based diagnoses is determined by the memory of local L-A states. The differences between E- and D-based diagnoses can be driven by physical mechanisms or the averaging algorithms. To improve understanding of L-A interactions, we call attention to the urgent need for more high-frequency data from both simulations and observations for relevant diagnoses. Regarding model outputs, two approaches are proposed to resolve the storage dilemma for high-frequency data: (1) integration of L-A metrics within Earth System Models, and (2) producing alternative daily datasets based on different averaging algorithms.
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