Final response to the referees' comments

Abstract

The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well errorcharacterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO 2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties). We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005), emphasising the rapid advance in relevant space-based observations.

Constraining estimates of terrestrial carbon uptake: new opportunities using long‐term satellite observations and data assimilation

“…DA provides a statistical framework to use observations to estimate model states and parameters, evaluate alternative model structures, and quantify and reduce uncertainties in model predictions. In particular, confronting ecosystem models with satellite observations using DA offers many benefits that could help constrain CFE, including: initialization of model states and parameters with high spatiotemporal frequency observations, which thus inform and constrain predictions (Dietze et al, 2018;Fox et al, 2018;Reichstein et al, 2019); implicit approximation of unobservable variables, for example LUE and WUE, that are constrained through process-based relationships within the model (Moore et al, 2008;Richardson et al, 2010;Fox et al, 2018); integration of multiple data streams at different spatial and temporal resolutions to provide constraints greater than the sum of individual data streams (Bacour et al, 2015;MacBean et al, 2016;Peylin et al, 2016); and systematic confrontation of models with observational data to drive cyclical and rapid model development (Parazoo et al, 2014;Scholze et al, 2017;Fischer et al, 2019;Reichstein et al, 2019). DA is a powerful approach for integrating satellite data where there are many overlapping observations that inform CFE, but where gaps are introduced because of sensor limitations and/or where only some carbon pools can be credibly observed (Fig.…”