Paleoclimate constraints on ENSO statistics

Transcription

1 Paleoclimate constraints on ENSO statistics Julien Emile-Geay USC Earth Sciences & Center for Applied Mathematical Sciences Workshop on ENSO diversity, Boulder, CO // Feb 8, 23

2 Three Questions. have ENSO flavors changed over the pre-instrumental era? 2. what is the relationship between ENSO and natural forcing over the past millennium? 3. can non-normal proxy records constrain changes in ENSO variability over time? Department of Earth Sciences

3 Question Coral-based SST field reconstructions With: Dominique Guillot (Stanford), Kim Cobb (GaTech), Julie Cole (Arizona), Thierry Corrège (Bordeaux), Sandy Tudhope (Edinburgh), Bala Rajaratnam (Stanford). 3

4 Annually-resolved coral network 4 o N a) Proxy representation by age (67 records) 3 o N 2 o N o N o o S 2 o S 3 o S 8 O Sr/Ca other 4 o S Most ancient age resolved b) Proxy availability over time # proxies O Sr/Ca other Data Provenance: NCDC + Cobb, Corrège, Tudhope, Cole Time Julien Emile-Geay USC 23

5 Coral 8 O network, 22 sites, EOF - 8% variance 24 o N 6 o N 8 o N o 8 o S 6 o S 24 o S Emile-Geay & Eshleman, G 3, in press EOF >, size magnitude EOF <, size magnitude Contours : DJF HadSST2i regressed onto PC Dots: EOF loadings Contours: SST (! C) regressed onto PC EOF analysis over for seasonally-resolved δ 8 O coral records.5 Principal component timeseries PC MTM spectrum PC (unitless) Power " Frequency Time -4 Julien Emile-Geay USC Frequency (cpy)

6 Climate Field Reconstruction A missing data problem backcast T from proxy observations multivariate inference A high-dimensional problem p = p i +p p = n =5 Instrumental Temperature T,, T pi Proxies P,,P pp 2 85 Covariance matrix quantifies dependencies between temperature and proxies. sample covariance matrix is rank-deficient estimation is impossible data reduction unknown P,,P pp Classically, L 2 regularization. Here: L model selection (MRFs, GGMs)

7 e Graphical EM (GraphEM) algorithm (2) Compute Dempster, Laird & Rubin, 977 Schneider, 2 () µ, Σ CI structure ˆΣ = ˆΣ G (Regularization) Update Compute regression coefficients (5) {µ, Σ} ˆB = ˆΣ + aa ˆΣ am (3) Guillot, Rajaratnam & Emile-Geay, in revision ˆx m = ˆµ m +(x a ˆµ a )ˆB (4) Estimate unknown values from the available ones Department of Earth Sciences

8 Cross-validation 3 El Niño events systematically under-represented NINO3.4 validation 2 NINO3.4 - La Niña events more faithfully -2 captured k = k = 2 k = 3 k = 4 k = 5-3 CE = +. CE = +.65 CE = +.63 CE = +.49 CE = Time Julien Emile-Geay USC 23

9 4 years of ENSO history 3 2 Coral-based NINO3.4 reconstruction, HadSST2 ggm, 5.3% sparsity Bootstrap 95% CI Instrumental NINO3.4 (HadSST2) Coral-predicted NINO3.4, CE =.7 NINO3.4( ο C) Number of corals 3 2 δ 8 O Sr/Ca Coral availability and in-sample skill CE score Julien Emile-Geay USC

10 ENSO flavors over time CP = ({NINO3 θ NINO4 θ}, NINO3 < NINO4) EP = ({NINO3 θ NINO4 θ}, NINO3 > NINO4) Yeh et al., Nature 29, Newman et al GRL 2 NINO3(K) EP, instrumental EP, reconstructed CP, instrumental CP, reconstructed ENSO flavors as seen by NINO3 θ =.5 CP False positives? CE score ENSO flavors as seen by NINO4 NINO4(K).5.5 Revenge of the CP? CE score

11 Conclusions () Corals + GraphEM = SST reconstructions extends tropical Pacific SST history for 4 years High skill in C.E.P (CE =.7), less so elsewhere (EEP) Reconstructing ENSO flavors suggest CP El Niños before 986 (2 in 7 th century) caveats: few spatial degrees of freedom (and dwindling); uncertainties in both t and p(t) coral-specific definition of EP and CP events needed In short: nothing definitive yet!

12 Question 2 Relationship to natural forcing With: Kim Cobb (GaTech), Michael Mann (Penn State), Andrew Wittenberg (GFDL) Toby Ault (NCAR), Clara Deser (NCAR) & Matt Newman (NOAA) 2

13 A millennial history of ENSO 3 2 NINO34 hybrid RegEM reconstruction (ttls) with uncertainties RegEM solution,ttls Same, 2-year lowpass RegEM 95% C.I. Jackknife C.I. NINO34( ο C) MCA LIA Emile-Geay et al, JClim 22 a,b Multiproxy, RegEM-TTLS, index Time

14 Comparison to other reconstructions Reconstruction of ENSO Indices (3-year lowpass).5 ENSO Index La Niña-like MCA? Importance Very large decadal of SST target! variability ERSSTv3 RegEM NINO34 HadSST2i RegEM NINO34 Kaplan RegEM NINO34 Avg Mann et al. [29] NINO3 Wilson et al. [2] NINO34 Palmyra δ 8 O Julien Emile-Geay Time USC

15 Spectral constraints on NINO3.4!"#$%#&'() )*+#',"-.)/#&,(!!" #!!" #$ &"" $""!"" Best agreement obtained with forced GCMs!"!#$%&'()('*+,-./ &" $"!" Newman et al, 2 Emile-Geay et al, 22a,b Ault et al, in revision * ) '! $!" #(!" #'! "#$ %&'()!%*++,-. %&'()!/++,(2 %&'()!3456!"#$%#&'()2'('.#3(#-"4 answer for the right reasons? Reconstructions significantly above the LIM null Are those PMIP3 GCMs getting the right 77+$8)!9# 77+$8)!"$ &#++:%(:*)!9# &#++:%(:*)!"$ #9+":7$;<:"*)!9# #9+":7$;<:"*)!"$ $#*=7:%+$)!9# $#*=7:%+$)!"$ $9#:%+$:9)!9# $9#:%+$:9)!"$!" #$!" #! "%& Department of Earth Sciences

16 Wavelet Transform Coherency Analysis Cross Wavelet Transform: Vieira & Solanki TSI-IPSL-CM5A-LR LM NINO3.4 Period (y) Wavelet Transform Coherency: Vieira & Solanki TSI-IPSL-CM5A-LR LM NINO3.4 Period (y) NINO3.4 roughly in phase with the forcing, lagging by a few decades Time CE /2 /4 /8 /6 Phase angle in 25-year band = -44± /8 /6 /32 /64 Torrence & Compo, BAMS 998 Liu et al, JAOT 27 Grinsted et al, NPG, 24

17 Phase constraints on tropical Pacific response to solar forcing NINO3.4 forcing phase angle CCSM4 Vieira & Solanki TSI 2 ± 6 GISS-E2-R Steinhilber et al. TSI 38 ± 28 IPSL-CM5A-LR Vieira & Solanki TSI 44 ± 23 MIROC-ESM Steinhilber et al. TSI - MPI-ESM-P Vieira & Solanki TSI 3 ± 8 EG2 HadSST2i Steinhilber et al. TSI 7 ± 9 EG2 HadSST2i Vieira & Solanki TSI 74 ± 26 PMIP3 CGCMs mostly in-phase weaker Walker response Reconstructions out-of-phase thermostat response [ Knutson & Manabe 995; Clement et al, 996 ; Held & Soden 26; Vecchi et al. 26] Department of Earth Sciences

18 What could CGCMs possibly get wrong? 5 JUNE 22 LLOYD ET AL. 42 CGCM TP SST response to external forcing is an emergent behavior resulting from the algebraic sum of compensating feedbacks Some of them are severely biased, as is their sum FIG. 6. Percentage overestimate/underestimate in the asw feedback decomposition method terms (SSTA. ) for the 2 CMIP3 models. When quantifying the model biases, dv 5 /dsst, dtcc / dv5, and dsw/dtcc are compared to ERA-4, ERA-4/ISCCP, and ISCCP, respectively. be explained by the nonlinearity in the observed high-level Lloyd et al,(fig. JClim 22 cloud cover response to SST 8). For SSTA., the mean Nin o-3 ISCCP high-level cloud cover increases (maximum positive anomalies of over 2%), efficiently reflecting incoming SW flux [high clouds tend to have FIG. 8. ISCCP high-level cloud cover anomalies against anomalies in Nin o-3 (984 2). Each point represents month, colored according to season (JFM, AMJ, JAS, and O seasons are colored black, red, green, and blue, respectively). Paleoclimate reconstructions may help constrain this Having shown that the dynamical response plays important role in the modeled asw nonlinearities, biases in the dynamical, cloud, or SW flux responses 2 count for the modeled a SW and asw errors? Tabl presents the linear correlations between the feedba and individual responses for the 2 CMIP3 models. /dv (i.e., the total cloud co SSTA., only dtccdepartment of Earth Sciences

19 Conclusions (2) Hi-res proxies + RegEM = NINO3.4 reconstructions extends central Pacific SST history for 85 years largest LF uncertainty comes from SST target... Spectral constraints on tropical Pacific dynamics amplitude: suggests most PMIP3 models are deficient at LF unless forced phase: suggests out-of-phase relationship to solar forcing 4/5 PMIP3 models analyzed so far get the reverse. (5th one inconclusive)

20 Question 3 Inferring ENSO variability from skewed proxy archives With: Martin Tingley (Harvard) 2

21 Motivation 25 Lake Pallcacocha Sediment Color (annual).3 Distribution of annual values 2.25 Red Color Intensity 5 5 PDF Time (years CE) Red Color Intensity 5 Transformed Pallcacocha Color (annual) 5.5 Distribution of transformed values Copula method (GEV) Box-Cox Transform, =.2323 PDF Time (CE) Z scores Moy et al, Nature, 22 Department of Earth Sciences

22 A toy model of non-normality in proxies y = 6 xp 2 + noise, xp = x(x>) x = NINO34 index (HadSSST2i) Toy model of a non-linear ENSO proxy p(t) := αn(t) β N(t) otherwise 3 NINO34(C) Fake sedimentary record (arbitrary units) (DJF) Department of Earth Sciences

23 The price of ignoring non-normality.8.6 Standard Deviation ratio, N = Regression, raw Regression, Box-Cox Bayesian posterior mean Probability density Treating skewed records as normal may result in a drastic overestimation of variance changes Emile-Geay & Tingley, in prep Estimated ratio Department of Earth Sciences

24 Box-Cox transform: a simple fix 4 Variance on -year windows Red Color Intensity 3.5 Box-Cox transformed intensity More gradual changes in ENSO variance are 3 inferred once nonnormality is taken into account Variance (arbitray units) Moy et al, Time (CE) (the main conclusions of Moy et al, 22, still stand) Department of Earth Sciences

25 Three Questions, Three Answers. have ENSO flavors changed over the past 4y? evidence for CP events before 97s ; real or false positives? too few spatial d.o.f to say anything confidently 2. relationship with natural forcing over the past y? LF: NINO3.4 reconstructions provide spectral constraints spectrum can only be reproduced by forced PMIP3 models anti-phasing to solar forcing (DeVries cycle, ~25 y) opposite relationship seen in PMIP3 models. HF: no convincing evidence for volcanic expression 3. can non-normal proxy records constrain changes in ENSO variability over time? yes, provided non-normality is explicitly taken into account Department of Earth Sciences

26 questions, comments, papers:

27 Coral contributions to flavors GraphEM reconstructed SST & coral contributions to NINO3, 65 CE SST anomaly (K) -.5 GraphEM reconstructed SST & coral contributions to NINO4, 65 CE SST anomaly (K) Positive contribution. Negative contribution. Area magnitude -.5 Julien Emile-Geay USC 22