Constraining the Large Uncertainties in Earth System Model Projections with a Big Data Approach

Growing awareness of anthropogenic climate change has led to an exponential increase in climate data from both observations and models over the past decades. This introduces new challenges to the research community to efficiently analyze and interpret them. The project COLUMBIA (Constraining the Large Uncertainties in Earth System Model Projections with Big Data Approach) aims to address this by adopting advance statistical methods and novel machine learning tools on climate model projections. The interdisciplinary team, consists of natural and computational scientists, was successful in applying machine learning to provide more accurate estimates of ocean carbon sinks. Biases in the model representation of mixing in the North Atlantic and the Southern Ocean led to divergence in the projected carbon fluxes. The Gulf Stream and the subantarctic front were identified as key regions where future observations will be crucial to improve our understanding of how the ocean carbon sink will response to future CO2 emissions. We developed a new clustering method that allows us to evaluate model performance in simulating the most important mode of global climate variability: ENSO (El Nino Southern Oscillation). Despite increases in complexity and computational resources, many models still have difficulties in reproducing the observed climatic characteristics in the tropical Pacific. Our method indicates that future climate events associated with the ENSO such as droughts and fires will become more frequent and stronger in amplitude. Continued environmental monitoring is essential to improve our understanding of the climate system and to better predict future changes. Due to the vast ocean area and limited resources, it is impossible to observe all variables over the entire ocean. We use machine learning and model outputs to identify ocean regions where the physical and biogeochemical properties will be largely affected by future climate change: the Southern Ocean, the tropical Pacific, and the North Atlantic. Long-term climate monitoring networks in these hotspot regions will provide valuable benefit for constraining future climate projections.

The project has produced a number of key results in the field of ocean carbon cycle and future climate projections. These findings have been published in high-level peer-reviewed journals (25 scientific publication at the time of reporting with more in the pipeline). In addition, the team has been active in disseminating results in key conferences, meetings, and workshops (40 presentations and more scheduled beyond the project period). The project recruited five early-career researchers, who have all gained research training in relatively novel field, combining climate and computational sciences. Each of the young researcher have been involved or led at least one publication associated with the project results. As they are well integrated to the Bjerknes Centre for Climate Research in Bergen, they will have the opportunity to apply their novel skills on broader field of climate change research after the project ends. An example is postdoc Timothee Bourgeois, who gained new skills in analyzing high volume of Earth system model outputs. His new competence allows him to be hired permanently at NORCE (as Forsker II) and to participate and contribute to the ongoing H2020-COMFORT and H2020-OceanNETs projects. Some senior members of the project teams have been invited to present key project findings at high-level meetings. A few examples are: (i) Science Day: AI in the fight against climate change, organized by the British Embassy in Oslo (Johannsen), (ii) invited to join the European Marine Board Forum working group on Marine Big Data (Tjiputra), (iii) invited to a Bilateral Artificial Intelligence Forum, join-organized by the Norwegian Ministry of Education and Research and the US Department of Energy (Tjiputra), and (iv) invited to lead/co-lead synthesis tasks in model evaluation (Goris), North Atlantic carbon sink (Tjiputra), and seasonal cycle of ocean CO2 fluxes (Schwinger) of the ongoing international RECCAP2 (Regional Carbon Cycle Assessment and Processes) synthesis project. The project activities have established new collaborations and opportunities to accelerate progress in both natural and computational fields. Several initiatives are in the pipeline to pursue more join research beyond COLUMBIA. Our most significant contribution to the field is by better elucidating the source of model uncertainty in projecting future ocean carbon sinks and other climate projections. These have provided essential new knowledge for both the modeling and observing communities, i.e. (i) guidance on how to focus future Earth system model development, in preparation for the IPCC-AR7 and (ii) strategy for future marine monitoring network to better understand the consequences of anthropogenic climate change. Moving forward, the project team are invited to contribute to the newly funded Horizon-Europe project (OceanICU), which will use the knowledge gained from COLUMBIA to better constrain future ocean carbon sink associated with biological activity.

The increasing volume of climate model data makes the use of traditional analysis tool impractical. This hinders the discovery of new crucial knowledge for society. COLUMBIA initiates a multidisciplinary research activity that will result in a cutting-edge customizable methodology that aims to facilitate rigorous evaluation of large ensemble of Earth system models. The innovative assessment tool will be based on combining Machine Learning with robust statistics and multiple process-oriented assessments, and aims to filter 'good' models from the rest and therefore increasing our confidence when synthesizing future climate change projections. The proposed work is well timed as the process for upcoming IPCC-AR6 has just began and the first batch of new Earth system model simulations will become available in early 2018. The proposed integrated evaluation method is at the forefront of international efforts in understanding sources of model uncertainty and producing optimized future climate projections. The project outcome will therefore have a broad international relevance for both climate modeling and observational community. Customizing existing Machine Learning techniques for climate science will also brings new opportunities and values to the computer science community. New scientific knowledge gained from the project will provide critical support for the climate science community as well important inputs for the IPCC-AR6. A unique close collaboration between climate modellers and computational scientists exists within Uni Research and the Bjerknes Centre, ensuring seamless collaboration in developing the new method. Ultimately, method developed in COLUMBIA will produce a new generation of optimized climate change projections that will give the best advice to policy makers and help society to find the best adaption strategy for climate change.