The big data and climate FRONTIER: making sense of the explosive increase in climate data through smart designs and big data methods

The next frontier of regional climate modelling is not in producing more data, but in producing more information. Climate information is becoming increasingly important for effective planning, adaptation and mitigation to future costs and disruptions arising from climate variability and change. However, this increasing demand for climate information is driving an explosive increase in the volume of climate data. This creates obstacles for users who need to access this data. Accessing such a large volume of climate data will require highly specialised skills and tools which can be unaffordable for many users. Therefore, an emerging challenge in climate change research is ensuring that climate information remains accessible to all users and stakeholders. FRONTIER aims to address this challenge through the key issues of data production and data analysis. In this project we have developed a new metric called the Bergen Metrics to simplify the evaluation of large ensembles of regional climate simulations. Error metrics are useful for evaluating model performance and have been used extensively in climate change studies. Despite the abundance of error metrics in the literature, most studies use only one or two metrics. Since each metric evaluates a specific aspect of the relationship between the reference data and model data, restricting the comparison to just one or two metrics limits the range of insights derived from the analysis. This study proposes a new framework and composite error metrics called Bergen Metrics to summarise the overall performance of climate models and to ease interpretation of results from multiple error metrics. The framework of Bergen Metrics are based on the p-norm, and the first norm is selected to evaluate the climate models. The framework includes the application of a non-parametric clustering technique to multiple error metrics to reduce the number of error metrics with minimum information loss. An example of Bergen Metrics is provided through its application to the large ensemble of regional climate simulations available from the EURO-CORDEX initiative. This study calculates 38 different error metrics to assess the performance of 89 regional climate simulations of precipitation and temperature over Europe. The non-parametric clustering technique is applied to these 38 metrics to reduce the number of metrics to be used in Bergen Metrics for 8 different sub-regions in Europe. These provide useful information about the performance of the error metrics in different regions. Results show it is possible to observe contradictory behaviour among error metrics when examining a single model. Therefore, the study also underscores the significance of employing multiple error metrics depending on the specific use case to achieve a thorough understanding of the model behaviour. FRONTIER has developed a novel mutli object tracking tool to better understand climate extremes and their evolution. Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi-Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra-tropical cyclones, cut-off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001–2020 and associate phenomena with hourly and daily satellite-derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid-latitude land regions, while extreme precipitation in mid and high-latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide-ranging applications including training of machine learning methods, Lagrangian-based evaluation of climate models, and process-based understanding of extreme precipitation in a changing climate.

FRONTIER focuses on providing a fundamental breakthrough on how climate model data is generated and analysed, so that we can provide more reliable climate information using less data and computer resources, and thereby accelerating time to science discovery by orders of magnitude. In FRONTIER, we aim to quantitatively assess the simulation of high impact weather events in regional climate models, reduce the number of performance metrics for more efficient analysis, and constrain the size of climate ensembles through a novel approach called Design of Experiment (DoE)-based ensemble. We believe that the next frontier of regional climate modeling is not in producing more data, but in producing more information through a targeted reduction of the data volume and by increasing its representativeness. This will make a substantial contribution towards Sustainable Development Goal (SDG) 13 "Climate action" by directly influencing the production of representative and skilful climate information. The methodological approach of FRONTIER is based on: (1) Developing novel process-based model analysis metrics in Lagrangian space to identify optimal model resolutions to capture societally relevant processes (2) Developing a new reduced set of performance metrics using Big Data methods to simplify and improve efficiency in data analysis (3) Exploring a new approach to multi-model ensembles, which we call Design of Experiment (DoE)-based ensemble, and contrast it with the current 'ensemble of opportunity' In FRONTIER, we believe that the next frontier of regional climate modeling is not in producing more data, but in producing less (more representative) information and improving efficiency in data analysis. Hence, we propose three underpinning frontier questions: a) can the RCM added value be better detected in a Lagrangian framework? b) can the number of performance metrics be reduced? c) can the ensemble of opportunity be replaced by something better?