Combining data from observations and models in coupled systems is vital for understanding and attributing the Earth system's past changes. This knowledge is crucial for grasping both the nature and consequences of climate change. To achieve this, we need better comprehension of processes, reliable detection of signals, and consistent quantification of uncertainties in observational and reanalysis datasets.

Methods and Activities

To advance the monitoring, understanding and attribution of climate system changes, concerted efforts are needed across various fronts. New and revitalised activities on observations and reanalysis under ESMO will be required to deliver our goals. The team aims also to join forces with GCOS and WWRP to bring forward key actions.

Strategic activities for this objective include the homogenizing observational datasets used as time-varying boundary forcings for reanalyses and models; continuous curation of observation-space datasets, including data rescue, quality control, and integration of recent observations; establishing a joint community effort on the coordination of Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs) for climate; developing a common understanding of observational and reanalysis uncertainties; coordinating the solicitation and collection of observational requirements for carbon cycle monitoring.


    These strongly link with objectives and WGs under the LHAs Explaining and Predicting Earth System Change and Digital Earth as well as WWRP-DAOS.

    • Improved monitoring and forecasting capabilities of the Earth system with enhanced methods for robust signal detection and uncertainty quantification
    • Advanced data assimilation methodology for climate.
    • Data selection, understanding of their requirements, and curation of data sets.
    • Written guidance on how to derive meaningful observational requirements in support of GCOS and expert groups advising agencies in ground-based network design or the definition of a space mission. 
    • Education of the upcoming generations of climate scientists to data assimilation. 
    • Provision of improved climate information for adaptation, serving societal needs. 
    • Facilitated exploitation of interface observations, and co-design of observing systems. 
    • Improved quantification of budgets in the energy, fresh water and specifically carbon cycles.
    • Observations and products become increasingly fit-for-purpose for ESMO science.