Towards using multi-dimensional structures in climate variables to detect anthropogenic changes

Precipitation (P) and evapotranspiration (ET) play a crucial role in the water cycle and have a significant impact on water resources and the energy balance of the Earth's surface. However, it remains a challenge, in particular on regional scales, to detect changes in hydrological variables and attribute them to anthropogenic or natural influences. Traditional studies that aim to detect or attribute changes in atmospheric variables often consider only a single variable at a time. This makes detecting changes in hydrological variables challenging due to large internal variability, the lack of long-term observational coverage and partly poor mechanistic understanding of land-atmosphere coupling processes in a changing climate.

 

However, because P and ET are related to various other atmospheric variables, such as temperature, humidity, and sea level pressure, the detection of anthropogenic influences may be conducted in principle within a broader multivariate space. Here, we aim at exploiting multivariate relationships to more robustly detect anthropogenic changes to the hydrological cycle at the regional or up to continental scale. We train statistical models from coupled Earth system models to learn the relationships between relatively well observed variables and more poorly observed ones, like P and ET. We demonstrate that such models can predict and extract patterns of forced change in P and ET, albeit somewhat contingent on the realism of the simulation of the Earth system model. The main advantage is that the method does not rely on sparse observations of P and ET, and instead relies on covariates which are more abundantly and reliably observed.

 

We demonstrate the effectiveness of this approach in a climate-model-as-truth framework, showing that it can capture a wide range of possible hydrological responses produced by the different climate models. We also apply the statistical model to observations to identify forced changes in P and ET that have already occurred. For example, we see an increase in ET in the northern hemisphere likely induced by a reduction in aerosol emissions. Our results show that this method can infer changes in P and ET that may have taken place, in principle even without the need for direct observations of those variables and can provide constrained projections of future water resources and energy balance.