Identification of linear response functions from arbitrary perturbation experiments in the presence of noise - Part II. Application to the land carbon cycle in the MPI Earth System Model

The response function identification method introduced in the first part of this study is applied here to investigate the land carbon cycle in the Max Planck Institute for Meteorology Earth System Model. We identify from standard C4MIP 1 % experiments the linear response functions that generalize the land carbon sensitivities β and γ. The identification of these generalized sensitivities is shown to be robust by demonstrating their predictive power when applied to experiments not used for their identification. The linear regime for which the generalized framework is valid is estimated, and approaches to improve the quality of the results are proposed. For the generalized γ sensitivity, the response is found to be linear for temperature perturbations until at least 6 K. When this sensitivity is identified from a 2×CO2 experiment instead of the 1 % experiment, its predictive power improves, indicating an enhancement in the quality of the identification. For the generalized β sensitivity, the linear regime is found to extend up to CO2 perturbations of 100 ppm. We find that nonlinearities in the β response arise mainly from the nonlinear relationship between net primary production and CO2. By taking as forcing the resulting net primary production instead of CO2, the response is approximately linear until CO2 perturbations of about 850 ppm. Taking net primary production as forcing also substantially improves the spectral resolution of the generalized β sensitivity. For the best recovery of this sensitivity, we find a spectrum of internal timescales with two peaks, at 4 and 100 years. Robustness of this result is demonstrated by two independent tests. We find that the two-peak spectrum can be explained by the different characteristic timescales of functionally different elements of the land carbon cycle. The peak at 4 years results from the collective response of carbon pools whose dynamics is governed by fast processes, namely pools representing living vegetation tissues (leaves, fine roots, sugars, and starches) and associated litter. The peak at 100 years results from the collective response of pools whose dynamics is determined by slow processes, namely the pools that represent the wood in stem and coarse roots, the associated litter, and the soil carbon (humus). Analysis of the response functions that characterize these two groups of pools shows that the pools with fast dynamics dominate the land carbon response only for times below 2 years. For times above 25 years the response is completely determined by the pools with slow dynamics. From 100 years onwards only the humus pool contributes to the land carbon respons

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise - Part I. Method development and toy model demonstration

Existent methods to identify linear response functions from data require tailored perturbation experiments, e.g., impulse or step experiments, and if the system is noisy, these experiments need to be repeated several times to obtain good statistics. In contrast, for the method developed here, data from only a single perturbation experiment at arbitrary perturbation are sufficient if in addition data from an unperturbed (control) experiment are available. To identify the linear response function for this ill-posed problem, we invoke regularization theory. The main novelty of our method lies in the determination of the level of background noise needed for a proper estimation of the regularization parameter: this is achieved by comparing the frequency spectrum of the perturbation experiment with that of the additional control experiment. The resulting noise-level estimate can be further improved for linear response functions known to be monotonic. The robustness of our method and its advantages are investigated by means of a toy model. We discuss in detail the dependence of the identified response function on the quality of the data (signal-to-noise ratio) and on possible nonlinear contributions to the response. The method development presented here prepares in particular for the identification of carbon cycle response functions in Part 2 of this study (Torres Mendonça et al., 2021a). However, the core of our method, namely our new approach to obtaining the noise level for a proper estimation of the regularization parameter, may find applications in also solving other types of linear ill-posed problems

Biophysics and vegetation cover change: A process-based evaluation framework for confronting land surface models with satellite observations

This is the final version. Available on open access from Copernicus Publications via the DOI in this recordLand use and land cover change (LULCC) alter the biophysical properties of the Earth's surface. The associated changes in vegetation cover can perturb the local surface energy balance, which in turn can affect the local climate. The sign and magnitude of this change in climate depends on the specific vegetation transition, its timing and its location, as well as on the background climate. Land surface models (LSMs) can be used to simulate such land-climate interactions and study their impact in past and future climates, but their capacity to model biophysical effects accurately across the globe remain unclear due to the complexity of the phenomena. Here we present a framework to evaluate the performance of such models with respect to a dedicated dataset derived from satellite remote sensing observations. Idealized simulations from four LSMs (JULES, ORCHIDEE, JSBACH and CLM) are combined with satellite observations to analyse the changes in radiative and turbulent fluxes caused by 15 specific vegetation cover transitions across geographic, seasonal and climatic gradients. The seasonal variation in net radiation associated with land cover change is the process that models capture best, whereas LSMs perform poorly when simulating spatial and climatic gradients of variation in latent, sensible and ground heat fluxes induced by land cover transitions. We expect that this analysis will help identify model limitations and prioritize efforts in model development as well as inform where consensus between model and observations is already met, ultimately helping to improve the robustness and consistency of model simulations to better inform land-based mitigation and adaptation policies.The study was funded by the FP7 LUC4C project (grant no. 603542

Towards net zero CO2 in 2050: an emission reduction pathway for organic soils in Germany

The Paris Agreement reflects the global endeavour to limit the increase of global average temperature to 2 °C, better 1.5 °C above pre-industrial levels to prevent dangerous climate change. This requires that global anthropogenic net carbon dioxide (CO2) emissions are reduced to zero around 2050. The German Climate Protection Plan substantiates this goal and explicitly mentions peatlands, which make up 5 % of the total area under land use and emit 5.7 % of total annual greenhouse gas emissions in Germany. Based on inventory reporting and assumptions of land use change probability, we have developed emission reduction pathways for organic soils in Germany that on a national level comply with the IPCC 1.5 °C pathways. The more gradual pathway 1 requires the following interim (2030, 2040) and ultimate (2050) milestones: Cropland use stopped and all Cropland converted to Grassland by 2030; Water tables raised to the soil surface on 15 % / 60 % / 100 % of all Grassland, on 50 % / 75 % / 100 % of all Forest land, and ultimately on 2/3 of all Settlements and on 100 % of all Wetlands. Also a more direct pathway 2 without interim ‘moist’ water tables and the climate effect (radiative forcing) of different scenarios is presente

Differences in land-based mitigation estimates reconciled by separating natural and land-use CO2 fluxes at the country level

Anthropogenic and natural CO2 fluxes on land constitute substantial CO2 emissions and removals but are usually not well distinguished in national greenhouse gas reporting. Instead, countries frequently combine natural and indirect human-induced CO2 fluxes on managed land in their reports, which diminishes their usefulness for designing policies consistent with climate mitigation targets. Here, we separate natural and land-use-related CO2 fluxes from national reports in eight countries using global models to improve the assessment of attribution of terrestrial CO2 fluxes to direct anthropogenic activities. In most investigated countries, the gap between model-based and report-based CO2 flux estimates is reduced if natural and indirect human-induced CO2 fluxes on managed land are considered. Further examinations show that remaining differences are linked to country-specific discrepancies between model-based and report-based estimates. Separating natural and land-use-related CO2 fluxes at national scales supports a fair burden sharing of climate mitigation across countries and facilitates the assessment of land-based mitigation ambitions. © 2022 The Author