Analysis Section CGD NCAR USA Detection and attribution of extreme temperature and drought using an analoguebased dynamical adjustment technique ID: 421980
Download Presentation The PPT/PDF document "Climate" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Climate
Analysis
Section, CGD, NCAR, USA
Detection and attribution of extreme temperature and drought using an analogue-based dynamical adjustment technique
Flavio Lehner
Clara Deser
Laurent
TerraySlide2
Outline
Motivation for dynamical adjustmentApplication
in a model frameworkFirst results
for high temperature eventsSlide3
Dynamics are
important – February 20153Slide4
Dynamics are
important – February 20154
ΔT = 18 °CSlide5
The problem of internal variability
5
Deser
et al. (in review)DJF temperature trend 1963-2012Slide6
The problem of internal variability
6
Deser et al. (in review)
DJF temperature trend 1963-2012ModelCESM1 (CAM5) – fully coupled GCMDJF temperature trend 1963-2012Slide7
The problem of internal variability
7
Deser et al. (in review)
DJF temperature trend 1963-2012Run #1 from the 30-member CESM Large EnsembleDJF temperature trend 1963-2012Slide8
The problem of internal variability
8
Deser et al. (in review)
DJF temperature trend 1963-2012Slide9
The problem of internal variability
9
Deser et al. (in review)
DJF temperature trend 1963-2012Slide10
The problem of internal
variability10
Deser
et al. (in review)DJF temperature trend 1963-2012Slide11
The problem of internal
variability11
Deser
et al. (in review)DJF temperature trend 1963-2012Slide12
The problem of internal
variability12
Deser
et al. (in review)DJF temperature trend 1963-2012No forced circulation change!Slide13
Dynamical adjustment with analogues
13Select a monthly mean field (SAT and SLP) from historical simulation
raw fieldSlide14
Dynamical adjustment with analogues
14Select a monthly mean field (SAT and SLP) from historical simulation
Search analogue of SLP in a long control simulation (no forcing)
l
ong control simulation
raw fieldSlide15
Dynamical adjustment with analogues
15Select a monthly mean field (SAT and SLP) from historical simulation
Search analogue of SLP in a long control simulation (no forcing)Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulation
l
ong control simulation
raw field
coefficientsSlide16
Dynamical adjustment with analogues
16Select a monthly mean field (SAT and SLP) from historical simulation
Search analogue of SLP in a long control simulation (no forcing)Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulationUse the same linear coefficients to reconstruct SAT, now using the SLP from the respective month in the historical simulation
l
ong control simulation
raw field
c
onstructed SAT field (dynamically induced)
raw field
coefficientsSlide17
Dynamical adjustment with analogues
17Select a monthly mean field (SAT and SLP) from historical simulation
Search analogue of SLP in a long control simulation (no forcing)Reconstruct the historical SLP pattern from a linear combination of the closest analogues found in the control simulationUse the same linear coefficients to reconstruct SAT, now using the SLP from the respective month in the historical simulation
This tells us how much of the SAT field comes from SLP variability, i.e., dynamics; the residual is assumed to come from thermodynamics
l
ong control simulation
raw field
c
onstructed SAT field (dynamically induced)
raw field
coefficients
−
=
raw field
dynamics
thermodynamicsSlide18
Dynamical adjustment with analogues
18
Deser et al. (in review)
DJF temperature trend 1963-2012 [°C/50 years]Run #7TotalDynamical contributionThermodynamical contributionSlide19
Application to high temperature events
19
Raw
Dynamical contributionThermo-dynamical contributionLehner et al. (in preparation)Slide20
Application to high temperature events
20
Raw
Dynamical contributionThermo-dynamical contributionConstructed SLP
Lehner et al. (in preparation)Slide21
Application to high temperature events
21
Raw
Dynamical contributionThermo-dynamical contributionLehner et al. (in preparation)Slide22
Application
to high temperature events22
Raw
Dynamical contributionThermo-dynamical contributionLehner et al. (in preparation)Slide23
Application to high temperature events
23
RawDynamical contribution
Thermo-dynamical contributionLehner et al. (in preparation)Slide24
Application to high temperature events
24
5-yr averages
Lehner et al. (in preparation)Slide25
Application to high temperature events
25
Partitioning ~50/50
Lehner et al. (in preparation)Slide26
Application to high temperature events
26
Partitioning ~50/50
Increase in thermodynamical contribution becomes detectable (theoretically)
Lehner et al. (in preparation)Slide27
Conclusions and outlook
Removal of dynamical contribution to
surface temperature trends and anomalies
Increased signal-to-noise for climate change studiesEasier to get at mechanisms for thermodynamic temperature changes (land-atmosphere interactions)Next steps:ObservationsGloballyDaily data?Precipitation?
Drought?Slide28
Thank you!Slide29
Dynamical adjustment with analogues
29
Deser
et al. (in review)Slide30
30