Climate Analysis Section, CGD, NCAR, USA Detection and attribution of extreme temperature and drought using an analogue-based dynamical adjustment technique Flavio Lehner Clara DeserLaurent Terray

Outline 1.Motivation for dynamical adjustment 2.Application in a model framework 3.First results for high temperature events

Dynamics are important – February

4 ΔT = 18 °C

The problem of internal variability 5 Deser et al. (in review) DJF temperature trend

The problem of internal variability 6 Deser et al. (in review) DJF temperature trend Model CESM1 (CAM5) – fully coupled GCM DJF temperature trend

The problem of internal variability 7 Deser et al. (in review) DJF temperature trend Run #1 from the 30-member CESM Large Ensemble DJF temperature trend

The problem of internal variability 8 Deser et al. (in review) DJF temperature trend

The problem of internal variability 9 Deser et al. (in review) DJF temperature trend

The problem of internal variability 10 Deser et al. (in review) DJF temperature trend

The problem of internal variability 11 Deser et al. (in review) DJF temperature trend

The problem of internal variability 12 Deser et al. (in review) DJF temperature trend No forced circulation change!

Dynamical adjustment with analogues 13 1.Select a monthly mean field (SAT and SLP) from historical simulation raw field

Dynamical adjustment with analogues 14 1.Select a monthly mean field (SAT and SLP) from historical simulation 2.Search analogue of SLP in a long control simulation (no forcing) long control simulation raw field

Dynamical adjustment with analogues 15 1.Select a monthly mean field (SAT and SLP) from historical simulation 2.Search analogue of SLP in a long control simulation (no forcing) 3.Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation long control simulation raw field coefficients

Dynamical adjustment with analogues 16 1.Select a monthly mean field (SAT and SLP) from historical simulation 2.Search analogue of SLP in a long control simulation (no forcing) 3.Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation 4.Use the same linear coefficients to reconstruct SAT, now using the SLP from the respective month in the historical simulation long control simulation raw field constructed SAT field (dynamically induced) raw field coefficients

Dynamical adjustment with analogues 17 1.Select a monthly mean field (SAT and SLP) from historical simulation 2.Search analogue of SLP in a long control simulation (no forcing) 3.Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation 4.Use the same linear coefficients to reconstruct SAT, now using the SLP from the respective month in the historical simulation 5.This tells us how much of the SAT field comes from SLP variability, i.e., dynamics; the residual is assumed to come from thermodynamics long control simulation raw field constructed SAT field (dynamically induced) raw field coefficients −= raw fielddynamicsthermodynamics

Dynamical adjustment with analogues 18 Deser et al. (in review) DJF temperature trend [°C/50 years] Run #7 Total Dynamical contribution Thermodynamical contribution

Application to high temperature events 19 Raw Dynamical contribution Thermo- dynamical contribution Lehner et al. (in preparation)

Application to high temperature events 20 Raw Dynamical contribution Thermo- dynamical contribution Constructed SLP Lehner et al. (in preparation)

Application to high temperature events 21 Raw Dynamical contribution Thermo- dynamical contribution Lehner et al. (in preparation)

Application to high temperature events 22 Raw Dynamical contribution Thermo- dynamical contribution Lehner et al. (in preparation)

Application to high temperature events 23 Raw Dynamical contribution Thermo- dynamical contribution Lehner et al. (in preparation)

Application to high temperature events 24 5-yr averages Lehner et al. (in preparation)

Application to high temperature events 25 Partitioning ~50/50 Lehner et al. (in preparation)

Application to high temperature events 26 Partitioning ~50/50 Increase in thermodynamical contribution becomes detectable (theoretically) Lehner et al. (in preparation)

Conclusions and outlook Removal of dynamical contribution to surface temperature trends and anomalies Increased signal-to-noise for climate change studies Easier to get at mechanisms for thermodynamic temperature changes (land-atmosphere interactions) Next steps: Observations Globally Daily data? Precipitation? Drought?

Thank you!

Dynamical adjustment with analogues 29 Deser et al. (in review)

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