EGU23-13608
https://doi.org/10.5194/egusphere-egu23-13608
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Evaluation of Precipitation Emergent Constraints in CMIP5 and CMIP6

Olivia Ferguglia1, Elisa Palazzi2, and Jost von Hardenberg3
Olivia Ferguglia et al.
  • 1Università di Torino, Dipartimento di Fisica, Torino, Italy (olivia.ferguglia@unito.it) and ISAC-CNR, Torino, Italy (o.ferguglia@isac.cnr.it)
  • 2Università di Torino, Dipartimento di Fisica, Torino, Italy (elisa.palazzi@unito.it) and ISAC-CNR, Torino, Italy (e.palazzi@isac.cnr.it)
  • 3DIATI, Politecnico di Torino, Torino, Italy (jost.hardenberg@polito.it) and ISAC-CNR, Torino, Italy (j.vonhardenberg@isac.cnr.it)

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). By constraining the predictor through observations, it is possible to narrow future model projections, if a significant correlation between the predictor and the predictand exists. In our work, the EC technique has been applied to the analysis of precipitation and precipitation extremes, variables that are strongly affected by model uncertainties and still insufficiently analyzed in the context of ECs. One of the main challenges in determining an EC is establishing if the relationship found is physically meaningful and if it is robust to changes in the composition of the model ensemble. Four ECs already documented in the literature and so far tested only with CMIP3 or CMIP5, have been reconsidered in our study. Their existence and robustness are evaluated by developing a systematic methodology that involves different subsets and different scenarios of CMIP5 and CMIP6 models, verifying if an EC found in CMIP3/CMIP5 is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Three out of the four ECs considered in our work did not pass the test, being robust in CMIP5 but not in CMIP6, or (in one case) being not robust in both CMIP5 and CMIP6. Only one EC is verified and robust in both model ensembles. These results show the difficulty of identifying robust precipitation ECs and cast doubts on the usability of such ECs as a tool to  reduce uncertainties in future projections of precipitation change. At the same time, this work highlights the importance of the EC technique  as a way to improve our understanding  of climate phenomena and their drivers and to investigate precipitation-related feedbacks, providing evidence of connections between precipitation and different climate variables. This observation lays the path to further explore original ECs.

How to cite: Ferguglia, O., Palazzi, E., and von Hardenberg, J.: Evaluation of Precipitation Emergent Constraints in CMIP5 and CMIP6, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13608, https://doi.org/10.5194/egusphere-egu23-13608, 2023.