MiKlip first phase: LiCos

Linking composition & circulation on intermediate spatio-temporal scales

LiCoS aims to investigate to what extent meteorological versus atmospheric chemical factors limit climate predictability on timescales of years to decades. Improved model formulations of radiative forcings and responses acting on small scales, i.e. related to aerosols, clouds and ozone, will reduce the uncertainty of regional climate predictions. By applying fast numerical schemes to represent these processes in the MiKlip decadal prediction system high resolution simulations can be performed. Coupling the lower and middle atmosphere, accounting for stratospheric ozone, volcanic aerosols and the solar cycle, will improve the representation of natural variability and near-term predictability. Finally, scenario uncertainties will be addressed through sensitivity studies of air pollution emissions.

Contributions of LiCoS to MiKlip include:

  • chemical submodels to evaluate the susceptibility of climate simulations to air quality
  • an improved understanding of the cloud, aerosol, chemistry (including stratosphere) model formulations (structural and parametric) that limit predictability
  • the links between these two points
  • a framework for introducing our findings into the MiKlip integrated decadal prediction system, coordinated with the other projects and modules.

Goals

LiCoS will address the four main research challenges of MiKlip by

  • contributing to a self-consistent climate modeling system
  • analyzing processes and mechanisms that limit decadal predictability
  • studying the predictive skill at the regional scale and
  • helping to test the accuracy of decadal forecasts.


We aim to help reduce three types of uncertainty in yearly and decadal climate predictions. The first refers to the better account of climate variability with a focus on dynamical links between the lower and middle atmosphere induced by radiative forcings (through ozone or aerosols). The second is model uncertainty, which will be reduced by improved and more efficient formulations of cloud, aerosol and atmospheric chemistry processes. The third is scenario uncertainty.

LiCos - Figure 1.
Figure 1. Air pollution emissions based on a Business as Usual (BAU) and Optimal Air Quality (AQC) scenario, which will be used to study their influence on climate predictability.

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Contact

Max-Planck-Insitut für Chemie, Mainz
Jos Lelieveld
Domenico Taraborrelli
Benedikt Steil

Max-Planck-Insitut für Meteorologie, Hamburg
Bjorn Stevens
Gaby Raedel

On the potential for abrupt Arctic winter sea ice loss

2016 - PNAS

Bathiany, S. | D. Notz, T. Mauritsen, G.Rädel, and V. Brovkin

Amplification of El Niño by cloud longwave coupling to atmospheric circulation

2016 - Nature Geoscience, Vol. 9, pp. 106–110

Rädel, G. | T. Mauritsen, B. Stevens, D. Dommenget, D. Matei, K. Bellomo, and A. Clement

The Atlantic Multidecadal Oscillation without a role for ocean circulation

2015 - Science, Vol. 350, (6258), pp. 320-324

Clement, A. | K. Bellomo, L. N. Murphy, M. A. Cane, T. Mauritsen, G. Rädel and B. Stevens

The Influence of Cloud Feedbacks on Equatorial Atlantic Variability

2015 - J. Climate

Bellomo, K. | A. Clement, T. Mauritsen, G. Rädel, and B. Stevens

Simulating the Role of Subtropical Stratocumulus Clouds in Driving Pacific Climate Variability

2014 - J. Climate, 27, 5119–5131

Bellomo, K. | A. Clement, T. Mauritsen, G. Rädel, and B. Stevens