[A-CG42] Projection and detection of global environmental change

[ACG42-06] Radiative Forcing, Earth's Energy Imbalance, and Deacdal-Scale Extremes: Past to Present to Future

*V Ramaswamy¹ (1.NOAA GFDL)

Keywords:Climate variations and change, Radiative forcing and feedbacks, Earth's Energy Imbalance, Weather Extremes

Satellite observations indicate that the Earth's Energy Imbalance (EEI) has been increasing over the past two decades. This is evident from both spectral and spectrally-integrated measurements. Model simulations indicate that this increase has arisen from changes in atmospheric composition and land surface, as well as climate feedbacks. The increasing energy imbalance is associative with the occurrence of climate anomalies. Investigation with models delineate the physical mechanisms that lead to changes in atmospheric circulation and build up heat and water extremes. What has occurred over the past and its diagnosis suggests the concept of a continuous monitoring strategy into the future, with attribution, wherein the radiative effect of changes in atmospheric composition can be verified against observed changes in the planetary energy imbalance and, in turn, along with the application of a climate model predict/project the associated occurrence of decadal-scale extremes. In essence: atmospheric composition to EEI to prediction of extremes using satellite observations and climate model predictions/projections. Towards this objective and for the participation in CMIP7, GFDL is developing its 5^th-generation coupled climate model, CM5, to project, understand, and monitor the atmospheric composition and radiative effects on the climate evolution over the next several decades based on the CMIP scenarios. A critical focus will be on the regional weather extremes on the backdrop of climate variations and change. The analysis of extremes will examine the role of the changes in oceanic state, and the impacts on regional heat and humidity, too much or too little water, and Arctic sea-ice, together with quantification of uncertainties. The model simulations will be integrated with data management science to convey accessible, interpretable and actionable information to stakeholders (e.g., infrastructural and civil engineers, water resource managers, and health institutions).