Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Ulrike ...
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L A T E X Tik Zposter How detailed do cloud microphysics need to be in climate models? Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Ulrike Lohmann Institute for Atmospheric and Climate Science, ETH Z ¨ urich, Z ¨ urich, Switzerland Contact: [email protected] How detailed do cloud microphysics need to be in climate models? Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Ulrike Lohmann Institute for Atmospheric and Climate Science, ETH Z ¨ urich, Z ¨ urich, Switzerland Contact: [email protected] Clouds and their feedbacks represent one of the largest uncertainties in climate projections. The representation of cloud microphysical processes (CMP) has become increasingly detailed, but more detailed climate models do not necessarily result in improved accuracy for estimates of radiative forcing (Knutti and Sedl´ aˇ cek, 2013; Carslaw et al., 2018). At the same time, simpler formulations allow for an easier understanding of the process’ effect in the model, and decrease computational demand. How detailed/accurate do CMP process representations need to be while preserving performance in key metrics? Methods • Model set up: ECHAM-HAM global climate simulation, 1 year, 2 moment CMPs, 35 runs • Phasing the effect a process has on the output, e.g. N IC = N IC,0 + η aggr · ΔN IC,aggr , with η aggr ∈ [0, 1] • Emulate the model output using Duncan Watson-Parris’s GCEm python package for emulating geophysical models: https://github.com/duncanwp/GCEm • Validate the emulator using a set of model runs that were not used for training it • Explore the emulated response surface with sensitivity analysis (Saltelli, 2008) • The shape of the model response to the phasing allows to infer potential for simplification Results Ice water path (IWP) • η aggr controls the IWP • η accr is of secondary importance • η rime is not influential → Looking only at the sensitivity of the IWP, riming and accretion could be simplified/removed → The plateau in the response at η aggr > 0.8 suggests that aggregation may be simplified as well • Aggregation dominates the response of the shortwave and longwave radiative effect • Riming controls the response of the liquid water path • S 1,i ≈ S T,i → no interactive effects observed (see Tab. 1) Conclusions • Phasing a process’s effect yields additional information on the model’s behavior and sensitivity to that process • Using a perturbed parameter ensemble, interactions between processes can be taken into account • Model sensitivity to aggregation, accretion and riming → all three show potential for simplification, also when taking into account interactions between them Outlook • Explore effect on more model output variables • Extend to η> 1 and to more processes • Simplify processes to which the model is not very sensitive, i.e. for which the output for η< 1 is approximately equal to the output for η =1 References Carslaw, Kenneth, Lindsay Lee, Leighton Regayre, and Jill Johnson (2018). “Climate Models Are Uncertain, but We Can Do Something About It” . In: Eos 99. issn: 2324-9250. doi: 10.1029/2018EO093757. Knutti, Reto and Jan Sedl´ aˇ cek (2013). “Robustness and Uncertainties in the New CMIP5 Climate Model Projections” . In: Nature Climate Change 3.4, pp. 369–373. issn: 1758-678X, 1758-6798. doi: 10.1038/ nclimate1716. Saltelli, A., ed. (2008). Global Sensitivity Analysis: The Primer. Chichester, England ; Hoboken, NJ: John Wiley. isbn: 978-0-470-05997-5. Saltelli, Andrea (2002). “Making Best Use of Model Evaluations to Compute Sensitivity Indices” . In: Com- puter Physics Communications 145.2, pp. 280–297. issn: 00104655. doi: 10.1016/S0010-4655(02) 00280-1. Usher, Will et al. (2020). SALib/SALib: Public Beta. Zenodo. doi: 10.5281/ZENODO.598306. Fig. 1: Investigated CMP processes. Fig. 2: Going from single sensitivity studies to a thorough investigation of model sensitivity to CMP processes, taking into account interactions. Fig. 3: Visual sensitivity analysis of the global annual mean ice water path (IWP) response to a phasing of riming, aggregation, and accretion. Variable S 1,rime S T,rime S 1,aggr S T,aggr S 1,accr S T,accr IWP 0 0 9.4 × 10 -1 9.6 × 10 -1 3.6 × 10 -2 6.0 × 10 -2 LWP 7.8 × 10 -1 8.3 × 10 -1 8.2 × 10 -3 1.8 × 10 -1 2.0 × 10 -2 1.1 × 10 -1 SCRE 3.0 × 10 -3 3.1 × 10 -3 9.8 × 10 -1 9.9 × 10 -1 1.2 × 10 -2 1.2 × 10 -2 LCRE 0 0 1.0 1.0 5.8 × 10 -4 8.3 × 10 -4 Tab. 1: Variance based sensitivity indices for global annual mean values of ice water path (IWP), liquid water path (LWP), shortwave and longwave cloud radiative effect (SCRE, LCRE); after Saltelli (2002) (using Usher et al. (2020)). S 1,i is the first-order sensitivity index of process i on the output; S T,i is the respective total effect term.
Transcript of Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Ulrike ...
LATEX TikZposter
How detailed do cloud microphysics need to be in climate models?
Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Ulrike Lohmann
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandContact: [email protected]
How detailed do cloud microphysics need to be in climate models?
Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Ulrike Lohmann
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandContact: [email protected]
Clouds and their feedbacks represent one of the largest uncertainties in climate projections. The representationof cloud microphysical processes (CMP) has become increasingly detailed, but more detailed climate models donot necessarily result in improved accuracy for estimates of radiative forcing (Knutti and Sedlacek, 2013; Carslawet al., 2018). At the same time, simpler formulations allow for an easier understanding of the process’ effect inthe model, and decrease computational demand. How detailed/accurate do CMP process representations needto be while preserving performance in key metrics?
Methods
•Model set up: ECHAM-HAM global climate simulation, 1 year, 2 moment CMPs, 35 runs
•Phasing the effect a process has on the output, e.g. NIC = NIC,0 + ηaggr ·∆NIC,aggr, with ηaggr ∈ [0, 1]
•Emulate the model output using Duncan Watson-Parris’s GCEm python package for emulating geophysical models: https://github.com/duncanwp/GCEm
•Validate the emulator using a set of model runs that were not used for training it
•Explore the emulated response surface with sensitivity analysis (Saltelli, 2008)
•The shape of the model response to the phasing allows to infer potential for simplification
Results
Ice water path (IWP)
• ηaggr controls the IWP
• ηaccr is of secondary importance
• ηrime is not influential
→ Looking only at the sensitivity of the IWP, riming and accretion could be simplified/removed→ The plateau in the response at ηaggr > 0.8 suggests that aggregation may be simplified as well
•Aggregation dominates the response of the shortwave and longwave radiative effect
•Riming controls the response of the liquid water path
•S1,i ≈ ST,i→ no interactive effects observed (see Tab. 1)
Conclusions
•Phasing a process’s effect yields additional information on the model’s behavior andsensitivity to that process
•Using a perturbed parameter ensemble, interactions between processes can be taken intoaccount
•Model sensitivity to aggregation, accretion and riming → all three show potential forsimplification, also when taking into account interactions between them
Outlook
•Explore effect on more model output variables
•Extend to η > 1 and to more processes
• Simplify processes to which the model is not very sensitive, i.e. for which the output forη < 1 is approximately equal to the output for η = 1
References
Carslaw, Kenneth, Lindsay Lee, Leighton Regayre, and Jill Johnson (2018). “Climate Models Are Uncertain,but We Can Do Something About It”. In: Eos 99. issn: 2324-9250. doi: 10.1029/2018EO093757.
Knutti, Reto and Jan Sedlacek (2013). “Robustness and Uncertainties in the New CMIP5 Climate ModelProjections”. In: Nature Climate Change 3.4, pp. 369–373. issn: 1758-678X, 1758-6798. doi: 10.1038/nclimate1716.
Saltelli, A., ed. (2008). Global Sensitivity Analysis: The Primer. Chichester, England ; Hoboken, NJ: JohnWiley. isbn: 978-0-470-05997-5.
Saltelli, Andrea (2002). “Making Best Use of Model Evaluations to Compute Sensitivity Indices”. In: Com-puter Physics Communications 145.2, pp. 280–297. issn: 00104655. doi: 10.1016/S0010-4655(02)00280-1.
Usher, Will et al. (2020). SALib/SALib: Public Beta. Zenodo. doi: 10.5281/ZENODO.598306.
Fig. 1: Investigated CMP processes.
Fig. 2: Going from single sensitivity studies to a thorough investigation of model sensitivity to CMP processes, taking into account interactions.
Fig. 3: Visual sensitivity analysis of the global annual mean ice water path (IWP) response to a phasing of riming,
aggregation, and accretion.
Variable S1,rime ST,rime S1,aggr ST,aggr S1,accr ST,accr
IWP 0 0 9.4× 10−1 9.6× 10−1 3.6× 10−2 6.0× 10−2
LWP 7.8× 10−1 8.3× 10−1 8.2× 10−3 1.8× 10−1 2.0× 10−2 1.1× 10−1
SCRE 3.0× 10−3 3.1× 10−3 9.8× 10−1 9.9× 10−1 1.2× 10−2 1.2× 10−2
LCRE 0 0 1.0 1.0 5.8× 10−4 8.3× 10−4
Tab. 1: Variance based sensitivity indices for global annual mean values of ice water path (IWP), liquid water path (LWP),
shortwave and longwave cloud radiative effect (SCRE, LCRE); after Saltelli (2002) (using Usher et al. (2020)). S1,i is the
first-order sensitivity index of process i on the output; ST,i is the respective total effect term.
