Statistical Reconstruction and Projection of Ocean Waves
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1 Statistical Reconstruction and Projection of Ocean Waves Xiaolan L. Wang, Val R. Swail, and Y. Feng Climate Research Division, Science and Technology Branch, Environment Canada 12th Wave Workshop, Hawaii, Oct 31 Nov. 4, 2011
2 Outline Methodologies and Datasets Some characteristics of the observed SWH-SLP relationships Preliminary results on North Atlantic wave height trends over (138 yr) Preliminary results on changes as projected in CMIP5 simulations Ongoing/future work
3 Methodologies Statistical downscaling use an observed predictor-predictand relationship - Conventional linear and quantile regressions means, extremes (both need high resolution data for extremes) - Extreme value (EV) model with covariates (predictors) extremes Both the location and scale parameters vary with the predictors? In order to diminish climate model biases: - Use normalized predictor quantities in statistical downscaling P t normalized anomalies of seasonal mean SLP G t normalized anomalies of seasonal mean squared SLP gradient (geo-wind energy index) - Use a quantile-matching algorithm to adjust (CMIP5) model simulated predictor values, so that the adjusted simulations for a baseline period share the same distribution as the corresponding observations (reanalysis data).
4 Datasets 1. ERA40 SLP predictors 2. ERA40 waves (SWH) - predictand observed SLP-SWH relationships - different statistical downscaling methods e.g., linear regression versus Quantile regression - different temporal resolutions, e.g., seasonal versus 6-hourly Important - find good predictors that are also well simulated by climate models! 3. Predictors from 6-hourly SLP of 20CRv2, The 20 th Century Reanalysis (56 runs) the relationships to reconstruct the past ( ) wave climate - temporal homogeneity issues, homogenization - ongoing - basically homogeneous for the North Atlantic focus of this presentation 4. Predictors from CMIP5 model simulations the relationships to project future wave climates
5 For extremes, scale is also varying with the covariates in a large portion of the oceans Compare EV1: only location par. varies with the predictors, but scale & shape are constant with EV2: both location & scale par s vary with the predictors, but shape is constant JFM: EV0: parameters are not significantly related to the predictors (EV1 is used) AMJ: JAS: OND:
6 New: use 6-hourly data to calibrate predictand-predictors relationships Using ERA40 6-hourly SLP on 2 x2 lat-lon grid 6-hourly Hs on 1.5 x1.5 lat-lon grid for (baseline period) 6-hourly SLP on 2 x2 lat-lon grid Will also use ERA-Interim for hourly Hs on 0.7 x0.7 lat-lon grid Calibrate a linear regression Ht ~ (Pt, Gt) relationship for each season separately. Evaluate the models that are calibrated from data in a calibration period with data in an evaluation period that does not overlap with the calibration period, e.g., calibration: ERA40 for evaluation: ERA40 for allows us to check statistical models time-transferability
7 Anomaly correlation skill scores JFM AMJ JAS OND Lower skill in the lower latitudes, especially in the cold seasons (JFM, OND)
8 Predicted-minus-observed wave height climate ( mean, in m) JFM AMJ JAS OND The stat. model overestimates the wave height climate, especially in high latitudes in winter - It overestimates mainly the low quantiles of wave heights, for example
9 It systematically over-predicts wave heights that are below 2 m, but under-predicts the extremes. Predicted values To improve model skill: Will explore new models, such as quantile regression different predictor-predictand relationships for different quantiles (e.g, one for the lowest 10%, one for 10-20%,, and one for %, respectively.) Will add a few predictors that can represent swell components.
10 Reconstructed trends in wave heights For now, just show trends in the North Atlantic, in which 20CR is homogeneous; it suffers from inhomogeneity in other basins
11 The trends in seasonal mean SWH in the North Atlantic ( 6-hourly relationships) JFM H_mean AMJ H_mean Yellow: Cyan: JAS H_mean OND H_mean Contours: linear trends Crosses: location of significant (at least 5%) linear trends grid-hatching: locations of significant quadratic trends
12 Trends from 6-hourly relationships JFM H_mean Trends from seasonal relationships JFM H_mean JAS H_mean JAS H_mean Yellow: Cyan: Contours: linear trends Crosses: location of significant (at least 5%) linear trends grid-hatching: locations of significant quadratic trends
13 The trends in seasonal max SWH in the North Atlantic ( 6-hourly relationships) JFM H_max AMJ H_max Yellow: Cyan: JAS H_max OND H_max Contours: linear trends Crosses: location of significant (at least 5%) linear trends grid-hatching: locations of significant quadratic trends
14 Trends from 6-hourly relationships JFM H_max Trends from seasonal relationships JFM H20yr JAS H_max JAS H20yr Yellow: Cyan: Contours: linear trends Crosses: location of significant (at least 5%) linear trends grid-hatching: locations of significant quadratic trends
15 Examples of changes in the distribution of JFM seasonal maximal significant wave heights Location parameter time series at (55.5N, 6W) (from seasonal GEV relationships) JFM H20yr PDF A 20-yr event has become a 14-yr event during the past century. The increase is mainly in the last 30 years. Hsig (m)
16 Examples of changes in the distribution of JFM seasonal maximal significant wave heights Location parameter time series at (55.5N, 13.5W) (from seasonal GEV relationships) JFM H20yr PDF A 20-yr event has become a 17-yr event during the past century. The increase is mainly in the last 30 years. Hsig (m)
17 Examples of changes in the distribution of JFM seasonal maximal significant wave heights Location parameter time series at (24N, 64.5W) (from seasonal GEV relationships) JFM H20yr PDF There seems to be a significant decrease in the early decades but no trend since early 20C. Hsig (m)
18 Trends in wave heights as downscaled from the CanESM2 simulations - historical simulations for (5 ensemble members) - RCP 2.6 simulations for (95 yrs) - RCP 4.5 simulations for (95 yrs) - RCP 8.5 simulations for (95 yrs)
19 CanESM2 simulated trends in JFM mean wave heights 20CR ( ) RCP 4.5 ( ) 160 yrs historical ( ) Opposite trends RCP 8.5 ( ) 160 yrs Crosses: location of significant (at least 5%) linear trends grid-hatching: locations of significant quadratic trends
20 CanESM2 simulated trends in JAS mean wave heights 20CR ( ) RCP 4.5 ( ) 160 yrs Historical ( ) RCP 8.5 ( ) 160 yrs Opposite trends same pattern Crosses: location of significant (at least 5%) linear trends grid-hatching: locations of significant quadratic trends
21 Ongoing/future work - Develop, apply, and evaluate different statistical downscaling methods - ongoing - Reconstruct the global wave climate using the 20CRv2 SLP - ongoing - Characterize global wave climate trends over the 138-year period since 1871, with temporal homogeneity assessment - Conduct statistical projections of global wave climate using CMIP5 simulations - Analyze the COWCliP wave projections to characterize climate change signal and various uncertainty associated with wave climate change projections Thank you very much!
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