CLARION · Constraining LAnd Responses by Integrating ObservatioNs

Constraining LAnd Responses by Integrating ObservatioNs

Climate change is widely recognised as the greatest threat to our generation. Land surface models (LSMs) are essential for understanding the land-atmosphere interactions and their impact on the global climate, especially as climate change affects how much human-made CO2 can be absorbed from the atmosphere. However, because LSMs are so complex, they have a lot of uncertainty built in. Reducing this uncertainty is crucial to generate reliable and credible climate projections. One of the key sources of uncertainty are parameters used in the equations used to describe the physics of the land surface. Even though these parameters can sometimes represent real qualities, such as root depth or the temperature at which plants best photosynthesise, they can be hard to measure and vary greatly between different experiments. The uncertainties in these parameters create uncertainties in the model predictions making it hard to inform policymakers and plan for the adverse effects of climate change. To reduce the uncertainty in the parameters, we can use sophisticated mathematical methods to find the best parameter values that ensure our computer model can better simulate what we observe in real life. This is called calibration and is the central tenet of this work. CLARION capitalises on novel datasets and techniques to i) create frameworks tackling uncertain parameters and ii) translate reductions in parameter uncertainty into more accurate climate predictions. These reductions in parameter uncertainty and increasing understanding of land-atmosphere interaction will help us better prepare and mitigate climate change. The objectives of CLARION are to: O1. Identify the key climate model parameters controlling the carbon, water, and energy cycles, and their relationship with future carbon climate projections; O2. Calibrate these parameters using sophisticated Bayesian techniques and in situ and Earth Observations data available to create observationally-constrained PDFs; O3. Constrain the range of climate-carbon cycle projections by propagating the reduction in parameter uncertainty.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз