Interannual Wave-Driven Shoreline Change on the California Coast

Interannual Wave-Driven Shoreline Change on the California Coast | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Interannual Wave-Driven Shoreline Change on the California Coast Mark Merrifield, William O'Reilly, Laura Cagigal, Dayeon Yoon, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6500020/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Our understanding of how wave climate variability drives shoreline evolution has advanced substantially in recent decades with increasing satellite imagery, wave buoy records, and wave hindcast models. While severe beach erosion with extreme El Niño waves is well documented on Pacific coastlines, the broader link between interannual wave energy and shoreline response has remained less clear. Here, we show nearly half of California's interannual Landsat shoreline change is a coherent response to wave power anomalies originating from a specific central North Pacific swell generation region, which in turn is weakly correlated with the Niño3.4 index. Positive wave power anomalies (beach loss) are strongly associated with El Niños, but the negative anomalies (beach recovery) are not similarly tied to La Niñas. Cumulative change in the CA statewide mean shoreline position is small over the 37-yr Landsat era but an 83-yr wave hindcast suggests a statewide wave-driven retreat of ~ 4m loss since 1941. These results provide additional insight into the role of the North Pacific wave climate in modulating beach width retreat and recovery over interannual to multi-decadal timescales, with implications for long-term coastal resilience planning. Earth and environmental sciences/Ocean sciences/Physical oceanography Earth and environmental sciences/Climate sciences/Ocean sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The empirical relationship between beach shoreline changes and incident wave energy or energy flux has been studied across many time and space scales. In California (CA), erosion occurs in response to energetic winter waves during strong El Niños 1 – 6 . In a comprehensive analysis of teleconnections between the El Niño Southern Oscillation (ENSO), wave power anomalies, and shoreline change, ref. 7 further links the wave climate to observed interannual cycles of both beach loss and recovery throughout the Pacific Basin, using Landsat satellite-derived shorelines 8 (CoastSat) and the fifth generation European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) wave hindcast 9 . Ref. 10 use CoastSat to examine long-term CA shoreline evolution, which is characterized by alternating alongshore reaches of shoreline widening and narrowing, or beach rotations, the result of decadal variations in wave-driven longshore transport 11 restricted by geologic (headlands) and engineered features (harbors, large jetties). Climate variations in North Pacific wave energy are closely linked to the intensity and position of the Aleutian Low 12 . On interannual time scales, El Niño events associate with an eastward-shifted Aleutian Low, increasing the frequency of extreme wave events in the central North Pacific, which in turn influences shoreline change trends along the California coast 13 – 15 . A similar pattern also occurs on decadal time scales with the positive (warm) phase of the Pacific Decadal Oscillation (PDO) 16 . Here, we build upon this previous work by focusing on correlated interannual shoreline and wave power changes on the CA coastline. Underlying CA's geographically complex seasonal ( > 1 year) alongshore rotation trends 10 (red and blue lines, Fig. 1 d), is strikingly coherent statewide interannual cross-shore beach widening and narrowing (black lines, Fig. 1 c,d). To highlight this interannual shoreline covariability, Landsat shoreline positions and the ERA5 wave power hindcast are reduced to annual means (October through September) and compared using empirical orthogonal function (EOF) analysis and ESTELA 19 (Evaluation of Source and Travel-time of wave Energy reaching a Local Area). Results and offshore wave power validation Tide corrected Landsat shoreline positions spanning 37 years 8 (1985-2021, CoastSat are reduced to seasonally weighted annual means (October through September), and averaged in the alongshore over contiguous beach reaches, to reduce errors and isolate interannual change ( Figure 1a , hereafter ). This approach minimizes artifacts from image digitization, wave runup, and irregular sampling. agrees with similarly averaged annual mean, mean high water (MHW) shoreline elevation location anomalies obtained from in situ surveys at five beaches in San Diego County 20-22 . Annual mean shoreline changes from Landsat and surveys have similar El Niño year erosion (2010, 2016), interannual variability, and localized widening from sand nourishments at Imperial Beach, Cardiff, and Solana Beach starting in 2012-13 ( Figure 1b ). Coherent multi-year shoreline beach loss and recovery patterns at many beaches across CA are apparent using the annual and beach-scale alongshore averaging ( Figure 1c ). Buoy observations show the CA offshore annual wave power climate increases 25% from south to north in the mean, but the interannual variation of the power anomaly is spatially homogeneous ( Figure 2c ). Offshore wave power from the ECMWF ERA5 wave hindcast model estimate (i.e. ERA5 vector wave energy flux, units m 3 /s) offshore of south-central CA is validated against 2001-2021 Datawell Directional Waverider deep water buoy observations 23 ( see Methods ). North Pacific winter swell (peak direction 300 o and peak frequency 0.07 Hz) dominate the offshore annual wave power anomaly variability ( Figure 2d ) compared to Southern Ocean summer swell power, which is an order of magnitude smaller (peak direction 185 o and peak frequency 0.065 Hz, Figure 2e ). Correlated interannual shoreline change and offshore wave power An alongshore-time diagram of shoreline change ( Figure 3a ) illustrates coherent statewide beach narrowing during El Niño years with large positive power anomalies (e.g. 1998, 2010, 2016, red years, Figure 3a) , and broadening over negative power anomaly periods between El Niños (e.g. 2011-2014, blue years). The 2015–2016 El Niño in particular generated exceptionally high waves along the CA coast, resulting in severe shoreline erosion and delayed post-storm recovery ( Figure 3a and 4 ). Shoreline change averaged statewide, and for northern CA (San Luis Obispo to Del Norte Counties, north of Pt. Conception) and southern CA (including a section of northern Baja California) emphasize the strikingly similar statewide response ( Figure 3b ). Statewide patterns of shoreline change, the difference between sequential annual means, are inversely correlated with wave power change ( r 2 = 0.72, Figure 3b ). This is consistent with broader findings that ENSO-driven wave climate anomalies significantly alter wave energy flux and drive shoreline erosion 24 . North-south differences are most evident prior to the 1997-98 El Niño. To highlight variability owing to beach rotations, the 37-yr dataset is divided into transects with cumulative widening or retreat since 1985 ( Figure 3c, d ). The cumulative mean change associated with the near-equal number of eroding (blue line, 51%) and accreting (red line, 49%) transects is substantial (+/- 15m, Figure 3c ) and consistent with the littoral cell-scale changes described by ref. 10 . When viewed as statewide means, the present era of beach rotations within the littoral cells appears to have started in earnest after the 1997-98 El Niño, or at the onset of the current PDO cold phase using the climatic North Pacific wave power delineation of ref. 14 (black arrow, Figure 3c ). The symmetric distributions of transects that have cumulatively eroded or accreted ( Figure 3d ) essentially cancel each other out in the statewide mean (black line, Figure 3c). We anticipated a negative 1985-2021 trend owing to the reduction of sand supply from rivers and increased armoring of coastal cliffs 25-28 . Instead, the Landsat data indicates that the CA-wide annual mean shoreline position has varied roughly +/-7m since 1985, with a trend that is not significantly different than zero (dotted black line, Figure 3c ). We explore this result, along with the unexpectedly coherent component of statewide shoreline recovery, with an EOF analysis of the shoreline change data depicted in Figure 3a . Dominant modes of CA shoreline variability Dominant statewide patterns of annually averaged CA shoreline change are identified with EOF analysis of time series with the time mean removed at each transect and a 5-km running mean. The first two EOF modes account for 45.8% and 5.8% of the total shoreline change variance. The mode 1 spatial pattern is mostly positive with considerable longshore variability, indicating in-phase covariations of varying amplitude ( Figure 4a,b ). Mode 1 has notably weak amplitudes in parts of Southern California, presumably due to wave shadowing caused by the shoreline orientation change south of Point Conception and sheltering by offshore islands 29,30 . Mode 1 is well-correlated with ERA5 (r 2 = 0.76), demonstrating that interannual shoreline variations are strongly correlated with wave power anomalies 31 . Mode 2 represents regional deviations from the dominant statewide shoreline response, with the strongest anomalies in Southern California. This mode primarily reflects localized effects, particularly in the early Landsat record (1988–1992), when significant spatial differences in shoreline change were observed between northern and southern California ( Figure 4d,e ). This period coincided with strong ENSO variability and potential limitations in Landsat-derived shoreline resolution, which may explain the greater prominence of Mode 2 in earlier years. Various measures of wave power variations across the state using the buoy data and ERA5 hindcasts were unable to account for mode 2 change. The statewide average does not necessarily reflect change at a specific beach or littoral cell 32 . For example, shoreline width trends in San Diego County vary from beach to beach ( Figure 1b ). The large (54% of shoreline change variance) residual change at higher spatial resolution reflects local longshore transport, sand supply (including nourishments), and grain size and beach orientation effects. Mode 1 shoreline regulated by central North Pacific swell generation CA wave hindcast backtracking with ESTELA 19 , 11 identifies the primary north and south Pacific source regions of below and above average wave power for different mode 1 and ENSO phases ( Figure 5, see methods ). We use the hourly wind sea wave partition from the Centre for Australian Weather and Climate Research (CAWCR) wave hindcast 35,36 to analyze the relationships between the wave generation areas and shoreline change. The composite generation averages (Figure 5) are based on the more significant positive and negative years (index over or below the 75th and 25th percentile) for mode 1 ( Figure 5a-c ), and similarly the Niño3.4 index ( Figure 5d-f ). The energy generation anomaly follows a clear pattern, with stronger wave generation in the northern hemisphere winter source region during negative Mode 1 phases (beach loss) and weaker wave generation during positive Mode 1 phases (beach recovery) ( Figure 5a,b ). This indicates that higher offshore wave energy leads to shoreline narrowing, while lower offshore wave energy corresponds to beach widening. Contrastingly, a composite analysis using the Niño3.4 index shows a similar well above average wave energy anomaly pattern in the North Pacific as negative mode 1 during El Niño years ( Figure 5c ), but a well below average northern hemisphere wave anomaly does not reciprocally occur during La Niña years ( Figure 5d ). The source composite of La Niñas instead shows a pattern consistent with a northward shift in more average wave generation. The correspondence of erosion with El Niño events is well known; however, we find only a weak overall correlation between shoreline change and the ENSO index (r 2 = 0.21), because non-El Niño years produce wave power anomalies with significant variability depending on ENSO-related SST anomaly distribution and intensity 37 ( Figure 5e). This suggests that the below-normal northern hemisphere energy years in ( Figure 5b ), associated with mode 1 Landsat shoreline recovery, primarily occur during ENSO neutral years. Long-term shoreline trends and the PDO The ERA5 wave hindcast (1940-2023) is used to construct an 82-yr hindcast of the statewide average annual "wave-driven" mean shoreline change based on the linear regression of 1985-2021 annual ERA5 offshore wave power against statewide averaged ( Figure 6) . The hindcast shoreline position using wave ERA5 (black dashed line, Figure 6b) compares well with the Landsat shoreline (green dotted line), but underestimates 1984-1987 shoreline retreat and 2012-2015 recovery. Landsat shoreline imagery was relatively low spatial resolution and temporally sparse in the 1980's and 1990's, perhaps contributing to larger model errors. The 82yr shoreline trends negative (black solid line, -0.054 ± 0.03 m/yr, Figure 6b ) with a statewide average shoreline retreat of ~ 4m since 1940, despite the more recent 1985-2021 period of mean shoreline position stability or slight widening derived from the Landsat images (green lines, Figure 6b ). Retreat is more consistent with the perception of long-term shoreline erosion, particularly after the extensive pre-1960 artificial widening of CA beaches with harbor constructions 38 , 27 . This wave-driven estimate does not consider sand supply changes (river inputs, nourishments). Alternating multi-decadal time periods of modeled statewide accretive and erosive shoreline trends (negative and positive ERA5 wave power trends) align with cold (1947-1976, 2000-present) and warm (1977-1999) phases of the PDO ( Figure 6 ). Longer-term shifts in atmospheric circulation and ocean heat content may also modulate wave power trends, influencing shoreline evolution beyond PDO-driven variability 39-43 . Following refs. 13,14 by parsing the hindcast into PDO cold and warm phases (blue and red shaded time periods, Figure 6 ), the power and shoreline trends are positive/negative in cold/warm phases (purple trends lines, Figure 6 ). The hindcast of the recent (2000-2021) cold phase trends compare favorably with the observed buoy (red solid line, Figure 6a ) and (green solid line, Figure 6b ) trends. The PDO cold/warm phases are defined by the sign of the PDO index after applying a 5-yr running mean to the monthly PDO index to suppress ENSO modulation of the PDO signal 44,45 , 13 . Analogous to seasonal beach change in CA 17,18 , mode 1 represents the cross-shore transport and storage of sand on interannual time scales. During energetic wave years sand moves offshore and the shoreline narrows until subsequent annual mean wave conditions favor shoreward transport. The Landsat estimates of more extreme annual beach loss (1985-1987) and recovery (2011-2013), associated with consecutive years of above or below average wave power, are underpredicted by an ERA5 regression hindcast model ( Figure 6) . These errors are possibly an interannual cross-shore sand distribution analog to shoreline evolution model errors owing to missing seasonal bar dynamics 46 . The finding that this onshore-offshore exchange has a mode 1 statewide coherent expression tied to the offshore wave climate, despite significant nearshore wave climate mean energy variation owing to island sheltering and coastal orientations, is notable. This suggests that offshore power anomalies lead to nearshore anomalies of the same sign but different magnitudes about their local means. The larger/smaller self-selected grain sizes associated with the higher/lower mean energy beaches 47 would further contribute to more similar cross-shore transport rates and more coherent interannual beach loss and recovery. These findings have direct implications for coastal management and long-term hazard mitigation along the CA coast. A shoreline change climatic index 11 based on the sign and strength of the central North Pacific winter west wind anomaly could provide annual guidance for CA shoreline managers in conjunction with seasonal to interannual statistical and machine learning models like ref. 48 . Anticipated beach recovery phases during neutral ENSO conditions could guide the timing of shoreline protection and restoration projects. Future work should further investigate the variability of recovery dynamics, given that swell magnitude propagating from the Southern Ocean storm track shows a strong positive correlation with the Southern Annular Mode particularly during Northern Hemisphere summer months 49 . While our results indicate no significant net statewide shoreline loss over the past 37 years, the long-term wave-driven retreat trend underscores the vulnerability of California’s beaches to changing climatic conditions. As sea level rise accelerates beach erosion through the end of the century 50 , integrating interannual wave-driven shoreline behavior into resilience planning will be critical for protecting coastal infrastructure, ecosystems, and recreational spaces. Methods CoastSat Shoreline Location Time Series The CoastSat 7 multi-decadal time series of shoreline positions greatly expands the historical database. Online CoastSat 1985-2021 shoreline position time series (http://coastsat.wrl.unsw.edu.au/), determined by algorithmically detecting the instantaneous water-sand and/or predominant wet-dry sand interface in individual satellite images for coastal reaches with sand or gravel beaches. The shoreline positions ( e.g. upper panel, Figure 1a ) are tide-corrected relative to MSL (but not wave runup corrected) using a single, long-term mean beach slope estimated from the uncorrected position time series and estimated tide levels, and spatially averaged onto 100m-spaced cross-shore transects. Surveyed Mean High Water Contour Location Time Series The MSL tide-corrected CoastSat annual mean positions represent approximately the surveyed mean high water (MHW) elevation contour, higher than MSL owing to wave runup 51 . From 2002-2010, the San Diego County coastline was surveyed in spring and fall using airborne topographic LiDAR. From 2001-2021, various San Diego beaches were surveyed with ATV, truck-mounted Lidar and push dolly-mounted mobile GPS. The mobile GPS surveys overlap in time and space with both the airborne and mobile LiDAR data sets and have been used to ensure survey accuracy and consistency between data collection methods 52 , 21 , 53-56 . From 2017-2021, truck and ATV-mounted mobile LiDAR surveys were monthly or quarterly at many San Diego beaches. Time series of the mean high water contour location on 100m-spaced cross-shore transects 57 (MOP lines) were derived from the survey data ( e.g. lower panel, Figure 1a ). The alongshore locations of the MOP and CoastSat 100m-spaced transects are interlaced, but their orientations typically differ by less than a few degrees. The five San Diego County beaches with the most comprehensive coverage were used for CoastSat validation. Annual Mean Shoreline Change Estimation To minimize biases owing to temporally irregular sampling, both survey MHW and CoastSat transect time series are averaged in time and alongshore space (,). Based on the CoastSat-survey comparisons in San Diego County (Figure 1), any seasonal biases and random errors associated with unresolved wave runup appear to be minimized in the annual averages. First, the time series are reduced to annual means (October-September) by progressively time averaging to monthly, quarterly, semi-annual, and finally annual values. Second, the time series are further reduced to annual mean anomalies, by removing their respective long-term means, to minimize any spatial offsets between the CoastSat and survey (GPS) geographic reference frames ( Figure 1b) . Finally, for the survey-CoastSat comparisons at each beach, a single space-time averaged annual shoreline anomaly time series is derived by spatially averaging over 10-30 transects depending on beach length. For the statewide EOF analysis, a fixed 5km (50 transect) running mean is used. Empirical Orthogonal Function analysis of shoreline change data At each grid point, a singular value decomposition (using svd.m in MATLAB) is applied to the Landsat annual shoreline change data, i.e., annual shoreline position first differenced in time. A temporal mean is removed from the first-differenced time series, equivalent to removing a linear trend from the shoreline position data. Each location is smoothed with a 5km alongshore running mean. The modal spatial patterns in Figure 4b are proportional to the temporal standard deviation of each mode. Offshore Annual Mean Wave Power Anomaly Estimates Wave forcing (periods 2-40 sec) is characterized as the offshore wave energy flux, or power (units m 3 /s), using Coastal Data Information Program (CDIP) directional wave buoy observations from 2000-2022 23 . Temporal fluctuations in the observed annual wave power (energy flux) anomalies covary along the CA offshore boundary. Mean wave power is 25% higher in north- than south-central CA ( Figure 2b ) but annual mean anomalies (relative to means) are of similar magnitude and well correlated ( Figure 2c). Smaller positive wave power anomalies are associated with (summer) Southern Ocean swell ( Figure 2e ) Historical 2001-2021 annual mean offshore wave power anomalies are derived from a single continuous wave power record of blended deep-water buoy observations. CDIP buoys are used preferentially, followed by nearby NOAA buoys, with any remaining time gaps filled by long-term monthly means. For central and southern CA, 96% of the wave power time series is from the CDIP Harvest buoy station west of Pt Conception, with the limited data gaps filled by data from the nearby CDIP Harvest Southeast buoy, the San Nicolas Island buoy or nearby NOAA buoys. Remaining data gaps (~ 1%) were filled with the time series mean for the same time of year (30 day running average). For north-central CA, waves are mostly from the CDIP Pt Reyes buoy station (93%), with data gaps filled by CDIP Monterey Bay West buoy (4%), and with the CDIP Pt Sur, NOAA 46042 and 46013 buoys. The ECMWF ERA5 wave hindcast annual mean wave power estimates The wave ERA5 reanalysis 9 (Copernicus Climate Change Service [C3S], 2017) from the European Centre for Medium-Range Weather Forecast (ECMWF) provides hourly, global waves with 0.5º spatial resolution from 1940 to present. Year to year changes in annual wave power are similar in ERA5 and at the Harvest and Pt Reyes Buoys ( Figure 3c ). Harvest and ERA5 annual power anomalies, and therefore power change, are significantly correlated ( r 2 = 0.84). The statewide mean is regressed on to ERA5 wave power, yielding an estimate for the mean CA shoreline anomaly based on the wave power anomaly times -0.61 (+/- 0.27) m (m 3 /s) -1 . ESTELA analysis The ESTELA (Evaluation of Source and Travel-time of wave Energy reaching a Local Area) method 19 estimates the source location and propagation time of waves approaching a specific location. Unlike previous studies that used both the sea and swell wave partitions 58,59 , 19 , we use the hourly wind sea wave partition from the Centre for Australian Weather and Climate Research (CAWCR) wave hindcast 35,36 to better isolate wave generation regions. To analyze the relationship between the wave generation areas and the shoreline response, composite averages are computed using the mode 1 index (over or below the 75th and 25th percentile). The anomaly of the energy generation with respect to the mean ( Figure 5a,b ) displays a clear positive/negative anomaly in the northern hemisphere during the negative/positive phase of the index, i.e., more energetic waves in the generation region correspond to narrower CA shorelines and vice versa. A composite using Oct-Sep years when the Niño3.4 index was above/below 1/-1°C for more the 5 months shows a similar pattern as mode 1 during El Niño years ( Figure 5c ), but well below normal wave conditions do not generally occur during La Niña Years ( Figure 5c ) leading to a poor overall correlation between mode 1 shoreline change and the Niño3.4 index. 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Global wave hindcast with Australian and Pacific Island focus: From past to present. Geoscience Data Journal , (May), 1–10. https://doi.org/10.1002/gdj3.104 (2020). Kumar, A. & Hoerling, M. P. Interpretation and implications of observed inter-El Niño variability. J. Clim. 10 , 83-91 (1997). Flick, R. E. The myth and reality of southern California beaches, Shore and Beach , 61 (3), 3-13 (1993). Trenberth KE, et al. Observations: Surface and Atmospheric Climate Change. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. (2007). Gemmrich, J., Thomas, B., & Bouchard, R. Observational changes and trends in northeast Pacific wave records. Geophys. Res. Lett. , 38 (22), L22601, https://doi.org/10.1029/2011GL049518 (2011). Reguero, B. G., Losada, I. J., & Mendez, F. J. A recent increase in global wave power as a consequence of oceanic warming. Nat. Comm., 10 (1), 205. https://doi.org/10.1038/s41467-018-08066-0 (2019). Stopa, J. E., Ardhuin, F., Stutzmann, E., & Lecocq, T. Sea state trends and variability: Consistency between models, altimeters, buoys, and seismic data (1979-2016). J. Geophys. Res.: Oceans , 124 (6), 3923–3940, https://doi.org/10.1029/2018JC014607 (2019). Timmermans, B. W., Gommenginger, C. P., Dodet, G., & Bidlot, J.-R. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys . Geophys. Res. Lett., 47 (9), e2019GL086880, https://doi.org/10.1029/2019gl086880 (2020). Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M., and Francis, R. C. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc. , 78 , 1069–1080, https://doi.org/10.1175/15200477(1997)0782.0.CO;2. (1997). Bromirski, P. D, Flick, R. E, & Cayan, D. R. Storminess variability along the California coast: 1858–2000. J. Clim. , 16 (6), 982-993. Retrieved from https://escholarship.org/uc/item/7x3137d1(2003). (2003). Ferreira, A. M., Coelho C., & Silva, P. A. Numerical evaluation of the impact of sandbars on cross-shore sediment transport and shoreline evolution, J. Environ. Manag. , 370 , https://doi.org/10.1016/j.jenvman.2024.122835 (2024). Christensen, D. F., Hughes, M. G., & Aagaard, T. Wave period and grain size controls on short-wave suspended sediment transport under shoaling and breaking waves. J. Geophys. Res.: Earth Surface , 124 , 3124–3142, https://doi.org/10.1029/2019JF005168 (2019). Davidson, M. A., Turner, I. L., Splinter, K. D. & Harley, M. D. Annual prediction of shoreline erosion and subsequent recovery. Coast. Eng . 130 , 14–25 (2017). Hemer, M. A., Church, J. A. & Hunter, J.R. Variability and trends in the directional wave climate of the Southern Hemisphere. Int. J. Climatol . 30 , 475-491, https://doi.org/10.1002/joc.1900 (2010). Sweet, W.V., B. D. Hamlington, R. E. Kopp, C. P., et al. Global and regional sea level rise scenarios for the United States: Updated mean projections and extreme water level probabilities along U.S. coastlines. NOAA Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp. https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nostechrpt01-global-regional-SLR-scenarios-US.pdf (2022). Castelle, B., Masselink, G., Scott, T., Stokes, C., Konstantinou, A., Marieu, V. & Bujan, S. Satellite-derived shoreline detection at a high-energy meso-macrotidal beach, Geomorphology , 383 , https://doi.org/10.1016/j.geomorph.2021.107707, (2021). Yates, M. L, Guza, R. T., & O’Reilly, W.C. Beach shoreline change: Observations and equilibrium modeling. J. Geophys Res , 114 , C09014, doi:10.1029/2009JC005359 (2009). Matsumoto, H., Young, A. P., & Guza, R. T. Observations of surface cobbles at two southern California beaches. Mar. Geol ., 419 , 106049 (2020a). Matsumoto, H., Young, A. P., & Guza, R. T. Cusps and mega cusps on a mixed sediment beach. Earth and Space Sci., 7 , e2020EA001366. https://doi.org/10.1029/2020EA001366-T (2020b). Matsumoto, H., & Young, A. P. Quantitative regional observations of gravel and bedrock influence on beach morphologies, Geomorphology , 419 , 108491 (2022). Siegelman, M. N., McCarthy, R. A., Young, A. P., et al. Subaerial profiles at two beaches: Equilibrium and machine learning. J. Geophys. Res.: Earth Surface , 129 , e2023JF007524. https://doi.org/10.1029/2023JF007524 (2024). O’Reilly, W. C., Olfe, C. B., Thomas, J., Seymour, R. J., & Guza, R. T. The California coastal wave monitoring and prediction system, Coastal Eng. , 116 , 118-132 (2016). Cagigal, L., Rueda, A., Anderson, D., et al. A multivariate, stochastic, climate-based wave emulator for shoreline change modelling. Ocean Modelling , 154 , 101695. https://doi.org/10.1016/j.ocemod.2020.101695 (2020). Camus, P., Méndez, F. J., Losada, I. J., et al. A method for finding the optimal predictor indices for local wave climate conditions. Ocean Dyn. , 64 (7), 1025–1038. https://doi.org/10.1007/s10236-014-0737-2 (2014). Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6500020","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450160270,"identity":"c4bba885-58df-4979-b9f5-afe2eb049847","order_by":0,"name":"Mark Merrifield","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYPACCX42BuYGiQ8g9gEglxgtkm0MjA2SM0jQwiDZANQizUOMFt3204mPK/dYSPCxNzbetm2zs+c7wHzwNg8eLWZncjcbnnkmIcHGc7DZOrctOXHmAbZka7xaDuRuk2w4IFHHJpHYJp3bdiDB4ACPmTReLeffbv8J1CLBJv+wTdqy7YC9wQH+b/i13MjdxgjWIsHYJs3YdoBxwwEeNgJa3m6WBGvhSWy27DkH9MthNmPLOXgdlrvxY8OBOgn59sMHb/woA4bY8eaHN97g0YIFMJOmfBSMglEwCkYBFgAAzCFNIzgqKTkAAAAASUVORK5CYII=","orcid":"","institution":"Scripps Institution of Oceanography, University of California San Diego","correspondingAuthor":true,"prefix":"","firstName":"Mark","middleName":"","lastName":"Merrifield","suffix":""},{"id":450160271,"identity":"74eee60c-d33f-4ab5-a573-4b4ca63450ff","order_by":1,"name":"William O'Reilly","email":"","orcid":"","institution":"Scripps Institution of Oceanography, University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"O'Reilly","suffix":""},{"id":450160272,"identity":"b482efe9-4e55-49be-9044-bc09340f8679","order_by":2,"name":"Laura Cagigal","email":"","orcid":"","institution":"Universidad de Cantabria","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Cagigal","suffix":""},{"id":450160273,"identity":"33b11dbe-97a7-4767-93ee-6165342415a4","order_by":3,"name":"Dayeon Yoon","email":"","orcid":"","institution":"Scripps Institution of Oceanography, University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Dayeon","middleName":"","lastName":"Yoon","suffix":""},{"id":450160274,"identity":"71e9fe0b-16d0-44da-8dbd-264fc8ec53b3","order_by":4,"name":"Holden Leslie-Bole","email":"","orcid":"","institution":"Scripps Institution of Oceanography, University of California San Diego","correspondingAuthor":false,"prefix":"","firstName":"Holden","middleName":"","lastName":"Leslie-Bole","suffix":""},{"id":450160275,"identity":"344be8da-c1c7-44cd-9d97-570603edac92","order_by":5,"name":"Susheel Adusumilli","email":"","orcid":"","institution":"University of Oregon","correspondingAuthor":false,"prefix":"","firstName":"Susheel","middleName":"","lastName":"Adusumilli","suffix":""},{"id":450160276,"identity":"45a211d3-0179-4714-8cda-2181279b9e99","order_by":6,"name":"Adam Young","email":"","orcid":"","institution":"Scripps Institution of Oceanography","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Young","suffix":""},{"id":450160277,"identity":"db66940b-366f-4af1-947a-02ee6baf7e14","order_by":7,"name":"Robert Guza","email":"","orcid":"","institution":"Scripps Institution of Oceanography","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Guza","suffix":""}],"badges":[],"createdAt":"2025-04-22 04:05:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6500020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6500020/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-65944-0","type":"published","date":"2025-11-17T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82134690,"identity":"8c277f29-04ac-4d79-990f-e6106a8e1276","added_by":"auto","created_at":"2025-05-07 06:04:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":506955,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Shoreline position at Solana Beach (dots) versus time from CoastSat (upper panel) and survey transects (lower panel). \u0026nbsp;Solid curves are seasonally-weighted, alongshore-averaged, annual mean shoreline positions (\u0026lt;CAcoastSat\u003ca href=\"#_msocom_1\"\u003e[MM1]\u003c/a\u003e\u0026nbsp;\u0026gt;, \u0026lt;Survey MHW\u0026gt;) relative to their respective long-term means (\u003cstrong\u003esee Methods\u003c/strong\u003e). (b) \u0026lt;CAcoastSat\u0026gt; and \u0026lt;Survey MHW\u0026gt; time series at five San Diego County beaches. Beach lengths (span of alongshore-averaged transects), RMSE and correlations r\u003csup\u003e2 \u003c/sup\u003eare given. (c)\u0026nbsp; \u0026lt;CAcoastSat\u0026gt; time series at five beaches spanning the State highlight the strong CA-wide beach recovery period (green shading) between the 2010 and 2016 El Niños (gray shaded years). \u0026nbsp;Sites are north to south, top to bottom (\u003cstrong\u003eFigure 2a\u003c/strong\u003e).\u0026nbsp; Alongshore averaging is between 2.5 and 25km (see legend).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003ca href=\"#_msoanchor_1\"\u003e[MM1]\u003c/a\u003eIn main text (line 73), \u0026lt;..\u0026gt; defined as annual mean only.\u0026nbsp; Does \u0026lt;..\u0026gt; mean time and space average?\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/ee0e613101c72a4a747777e8.png"},{"id":82134692,"identity":"e5dc5272-35a4-419b-b49e-b8a9f25c663c","added_by":"auto","created_at":"2025-05-07 06:04:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":395139,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Locations of observed north-central (Pt. Reyes Buoy), observed south-central (Harvest Buoy), and modeled south-central CA (ERA5) wave power.\u0026nbsp; (b) Offshore annual mean wave power (i.e. energy flux, units m\u003csup\u003e3\u003c/sup\u003e/s) versus time; south-central CA hindcast (ERA5, magenta) and observed with buoys in south-central (black) and north-central (green) CA.\u0026nbsp; (c) Annual mean anomalies (relative to means) of north- and south-central CA buoys have similar magnitudes and are highly correlated r\u003csup\u003e2 \u003c/sup\u003e=0.75.\u0026nbsp; South-central CA buoy wave power anomalies and ERA5 also are correlated (r\u003csup\u003e2 \u003c/sup\u003e=0.84).\u0026nbsp; (d) Buoy Maximum Entropy Method (MEM) south-central CA offshore annual wave power density anomalies (color bar) versus (upper) wave direction and (lower) frequency. Dashed lines show 22-yr mean wave power MEM peak direction (300\u003csup\u003eo\u003c/sup\u003e) and peak frequency (0.07Hz). (e) Observed power anomalies with southern MEM directions only (note reduced wave power color scales).\u0026nbsp; Dashed lines are 22-yr mean MEM peak south direction (185\u003csup\u003eo\u003c/sup\u003e) and peak south frequency (0.065Hz).\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/c721eef4334358d99ec05b89.png"},{"id":82134696,"identity":"75369cd3-77be-4bea-94b9-8081a75a6e05","added_by":"auto","created_at":"2025-05-07 06:04:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":770094,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Annual shoreline change (color bar, difference of successive annual mean \u0026lt;CAcoastSat\u0026gt; shorelines) versus alongshore location and time.\u0026nbsp; Annual change is coherent across CA.\u0026nbsp; Locations lacking sand beaches denoted by white horizontal stripes. (b) Annual change averaged over all CA, North CA (San Luis Obispo to Del Norte County) and South CA (Ensenada, Mexico to Santa Barbara County). El Niño years are shaded in gray.\u0026nbsp; CA-wide shoreline change is inversely correlated with south-central CA ERA5 annual offshore wave power\u003cstrong\u003e \u003c/strong\u003echange\u003cstrong\u003e (\u003c/strong\u003epurple\u003cstrong\u003e, \u003c/strong\u003eright y-axis, r\u003csup\u003e2\u003c/sup\u003e = 0.72).\u0026nbsp; Increasing wave power is associated with beach loss.\u003cstrong\u003e \u003c/strong\u003e(c)\u003cstrong\u003e \u003c/strong\u003eCumulative change in shoreline position (since 1985) versus time for accreting (blue, 4313 transects), eroding (red, 4147 transects), and all (black, 8460 transects).\u0026nbsp; Grey shading is standard deviation for all transects. Evolution of beaches into accreting and eroding transects (beach rotation) accelerated after the 1997-98 El Niño (black arrow). The statewide average 37-yr trend is small (black dashed line) with\u003cstrong\u003e \u003c/strong\u003e(d)\u003cstrong\u003e \u003c/strong\u003ea markedly symmetric transect change distribution.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/f0ee379e6f58ec32e0383d7f.png"},{"id":82134694,"identity":"170167ef-7f31-4daa-8dcb-f08f8c3de96d","added_by":"auto","created_at":"2025-05-07 06:04:47","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":939250,"visible":true,"origin":"","legend":"\u003cp\u003e(a) EOF mode 1 of Figure 3a.\u0026nbsp; (b, c) mode 1, 2 spatial amplitudes at 8460 alongshore locations (5-km running mean) multiplied by the standard deviation of the modal temporal expansion. (d) Mode 1 captures statewide average CoastSat shoreline change.\u0026nbsp; Mode 2 statewide mean is small. (e) Mode 2 represents change differences between N. and S. CA not captured by Mode 1. (f) Largest Mode 2 North versus South change differences occur early in the record (1988, 1991 shaded years).\u0026nbsp; Jan 17-18, 1988 was the largest wave event in the ERA5 1940-2021 record and primarily impacted S. CA\u003ca href=\"#Ref_32shak1989\"\u003e\u003csup\u003e33,34\u003c/sup\u003e\u003c/a\u003e. Vertical scales differ in (d-f).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/908b40349333565e4ad1cbc5.jpeg"},{"id":82136418,"identity":"f298a0a9-0249-4ead-a6ad-3ec69512c80d","added_by":"auto","created_at":"2025-05-07 06:12:47","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1274708,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b) Wave generation anomaly regions associated with the shoreline EOF mode 1 (M1) and (c, d) ENSO Niño3.4 index (n34), estimated from the CAWCR wave hindcast using the ESTELA method.\u0026nbsp; Central North Pacific wave generation directed at CA is (a) well above average when M1 is negative (M1 \u0026lt; 25th percentile, coherent beach loss), and (b) well below average when the M1 amplitude is positive (M1 \u0026gt; 75th percentile, strong coherent beach recovery).\u0026nbsp; Contrastingly, North Pacific wave generation is above average during (c) El Niño years (Oct-Sep n34 with \u0026gt; 5 months above 1) but (d) a mix of weaker above and below average generation during La Niña years (Oct-Sep n34 with \u0026gt; 5 months below -1).\u0026nbsp; As a result, (e) time series correlation between mode 1 shoreline change and the annual mean n34 index is weak (blue dotted line, r\u003csup\u003e2 \u003c/sup\u003e= 0.21). \u0026nbsp;The highest correlation is found using just the January n34 index value each year (red dotted line, r\u003csup\u003e2 \u003c/sup\u003e= 0.32) where individual El Niño years (grey shading) correlate well, but there is still poor correlation in the years between the El Niños. The more highly correlated robust shoreline recovery period between 2010-2016 (see Figure 1c) is an exception (green shading).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/ab76c02e2da0ed0e1cc9d88a.jpeg"},{"id":82134697,"identity":"ecefa935-b447-41cb-b4a6-49babf91cf63","added_by":"auto","created_at":"2025-05-07 06:04:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":291415,"visible":true,"origin":"","legend":"\u003cp\u003e1941-2023 wave-driven shoreline change hindcast. \u003cstrong\u003e(a)\u003c/strong\u003e ERA5 annual mean wave power (black dashed line) exhibits a positive long-term trend (black solid line, 0.009 m\u003csup\u003e3\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e/yr).\u0026nbsp; Multi-decadal wave power trends (purple lines) are negative/positive in cold/warm PDO phases.\u0026nbsp; The ERA5 2000-2021 PDO cold phase power trend (right purple line) is consistent with the observed CA offshore trend (red solid line).\u0026nbsp; \u003cstrong\u003e(b)\u003c/strong\u003e Estimated CA statewide average shoreline change since 1941 using ERA5 annual wave power and a linear regression of annual wave power against the \u0026lt;CAcoastSat\u0026gt; estimate of statewide annual relative shoreline position from 1985-2021 (green dotted line, vertically offset from 0 by the 1985-2021 mean of the modeled shoreline position for clarity).\u0026nbsp; The 83-yr shoreline hindcast model (black dotted line) has a negative trend (black solid line, -0.054 m/yr) with a statewide average shoreline retreat of ~4m since 1940. Model trends are positive/negative in cold/warm PDO phases (blue/red shaded time periods, purple lines).\u0026nbsp; The ERA5 2000-2021 PDO cold phase shoreline trend (right purple line) is consistent with the observed \u0026lt;CAcoastSat\u0026gt; trend (green solid line).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/81fa2f7b56200198253fa4e8.png"},{"id":96153795,"identity":"4d8ea3f1-9c18-4db3-9a6a-9b8ba64ec4cf","added_by":"auto","created_at":"2025-11-18 08:06:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5285308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6500020/v1/459561b6-85ff-47b5-894b-2de59c729974.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Interannual Wave-Driven Shoreline Change on the California Coast","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe empirical relationship between beach shoreline changes and incident wave energy or energy flux has been studied across many time and space scales. In California (CA), erosion occurs in response to energetic winter waves during strong El Ni\u0026ntilde;os\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In a comprehensive analysis of teleconnections between the El Ni\u0026ntilde;o Southern Oscillation (ENSO), wave power anomalies, and shoreline change, ref.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e further links the wave climate to observed interannual cycles of both beach loss and recovery throughout the Pacific Basin, using Landsat satellite-derived shorelines\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e (CoastSat) and the fifth generation European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) wave hindcast\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Ref.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e use CoastSat to examine long-term CA shoreline evolution, which is characterized by alternating alongshore reaches of shoreline widening and narrowing, or beach rotations, the result of decadal variations in wave-driven longshore transport\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e restricted by geologic (headlands) and engineered features (harbors, large jetties).\u003c/p\u003e \u003cp\u003eClimate variations in North Pacific wave energy are closely linked to the intensity and position of the Aleutian Low\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. On interannual time scales, El Ni\u0026ntilde;o events associate with an eastward-shifted Aleutian Low, increasing the frequency of extreme wave events in the central North Pacific, which in turn influences shoreline change trends along the California coast\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A similar pattern also occurs on decadal time scales with the positive (warm) phase of the Pacific Decadal Oscillation (PDO)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Here, we build upon this previous work by focusing on correlated interannual shoreline and wave power changes on the CA coastline.\u003c/p\u003e \u003cp\u003eUnderlying CA's geographically complex seasonal (\u0026lt;\u0026thinsp;1\u0026nbsp;year) shoreline variations\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and superimposed on decadal (\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026nbsp;year) alongshore rotation trends\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (red and blue lines, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), is strikingly coherent statewide interannual cross-shore beach widening and narrowing (black lines, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec,d). To highlight this interannual shoreline covariability, Landsat shoreline positions and the ERA5 wave power hindcast are reduced to annual means (October through September) and compared using empirical orthogonal function (EOF) analysis and ESTELA\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e (Evaluation of Source and Travel-time of wave Energy reaching a Local Area).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e\u0026lt;CAcoastSat\u0026gt; and offshore wave power validation \u003c/h3\u003e\n\u003cp\u003eTide corrected Landsat shoreline positions spanning 37 years\u003csup\u003e8\u003c/sup\u003e (1985-2021, CoastSat are reduced to seasonally weighted annual means (October through September), and averaged in the alongshore over contiguous beach reaches, to reduce errors and isolate interannual change (\u003cstrong\u003eFigure 1a\u003c/strong\u003e, hereafter \u0026lt;CAcoastSat\u0026gt;). This approach minimizes artifacts from image digitization, wave runup, and irregular sampling. \u0026lt;CAcoastSat\u0026gt; agrees with similarly averaged annual mean, mean high water (MHW) shoreline elevation location anomalies obtained from in situ surveys at five beaches in San Diego County\u003csup\u003e20-22\u003c/sup\u003e. Annual mean shoreline changes from Landsat and surveys have similar El Ni\u0026ntilde;o year erosion (2010, 2016), interannual variability, and localized widening from sand nourishments at Imperial Beach, Cardiff, and Solana Beach starting in 2012-13 (\u003cstrong\u003eFigure 1b\u003c/strong\u003e). Coherent \u0026lt;CAcoastSat\u0026gt; multi-year shoreline beach loss and recovery patterns at many beaches across CA are apparent using the annual and beach-scale alongshore averaging (\u003cstrong\u003eFigure 1c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eBuoy observations show the CA offshore annual wave power climate increases 25% from south to north in the mean, but the interannual variation of the power anomaly is spatially homogeneous (\u003cstrong\u003eFigure 2c\u003c/strong\u003e). Offshore wave power from the ECMWF ERA5 wave hindcast model estimate (i.e. ERA5 vector wave energy flux, units m\u003csup\u003e3\u003c/sup\u003e/s) offshore of south-central CA is validated against 2001-2021 Datawell Directional Waverider deep water buoy observations\u003csup\u003e23\u003c/sup\u003e (\u003cstrong\u003esee Methods\u003c/strong\u003e). North Pacific winter swell (peak direction 300\u003csup\u003eo\u003c/sup\u003e and peak frequency 0.07 Hz) dominate the offshore annual wave power anomaly variability (\u003cstrong\u003eFigure 2d\u003c/strong\u003e) compared to Southern Ocean summer swell power, which is an order of magnitude smaller (peak direction 185\u003csup\u003eo\u003c/sup\u003e and peak frequency 0.065 Hz, \u003cstrong\u003eFigure 2e\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003eCorrelated interannual shoreline change and offshore wave power \u003c/h3\u003e\n\u003cp\u003eAn alongshore-time diagram of shoreline change (\u003cstrong\u003eFigure 3a\u003c/strong\u003e) illustrates coherent statewide beach narrowing during El Ni\u0026ntilde;o years with large positive power anomalies (e.g. 1998, 2010, 2016, red years, \u003cstrong\u003eFigure 3a)\u003c/strong\u003e, and broadening over negative power anomaly periods between El Ni\u0026ntilde;os (e.g. 2011-2014, blue years). The 2015\u0026ndash;2016 El Ni\u0026ntilde;o in particular generated exceptionally high waves along the CA coast, resulting in severe shoreline erosion and delayed post-storm recovery (\u003cstrong\u003eFigure 3a\u003c/strong\u003e and\u003csup\u003e4\u003c/sup\u003e). Shoreline change averaged statewide, and for northern CA (San Luis Obispo to Del Norte Counties, north of Pt. Conception) and southern CA (including a section of northern Baja California) emphasize the strikingly similar statewide response (\u003cstrong\u003eFigure 3b\u003c/strong\u003e). Statewide patterns of \u0026lt;CAcoastSat\u0026gt; shoreline change, the difference between sequential annual means, are inversely correlated with wave power change (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e \u003c/em\u003e= 0.72, \u003cstrong\u003eFigure 3b\u003c/strong\u003e). This is consistent with broader findings that ENSO-driven wave climate anomalies significantly alter wave energy flux and drive shoreline erosion\u003csup\u003e24\u003c/sup\u003e . North-south differences are most evident prior to the 1997-98 El Ni\u0026ntilde;o. \u003c/p\u003e\n\u003cp\u003eTo highlight variability owing to beach rotations, the 37-yr \u0026lt;CAcoastSat\u0026gt; dataset is divided into transects with cumulative widening or retreat since 1985 (\u003cstrong\u003eFigure 3c, d\u003c/strong\u003e). The cumulative mean change associated with the near-equal number of eroding (blue line, 51%) and accreting (red line, 49%) transects is substantial (+/- 15m, \u003cstrong\u003eFigure 3c\u003c/strong\u003e) and consistent with the littoral cell-scale changes described by ref.\u003csup\u003e10\u003c/sup\u003e. When viewed as statewide means, the present era of beach rotations within the littoral cells appears to have started in earnest after the 1997-98 El Ni\u0026ntilde;o, or at the onset of the current PDO cold phase using the climatic North Pacific wave power delineation of ref.\u003csup\u003e14\u003c/sup\u003e (black arrow, \u003cstrong\u003eFigure 3c\u003c/strong\u003e). \u003c/p\u003e\n\u003cp\u003eThe symmetric distributions of transects that have cumulatively eroded or accreted (\u003cstrong\u003eFigure 3d\u003c/strong\u003e) essentially cancel each other out in the statewide mean (black line, \u003cstrong\u003eFigure 3c). \u003c/strong\u003e We anticipated a negative 1985-2021 trend owing to the reduction of sand supply from rivers and increased armoring of coastal cliffs\u003csup\u003e25-28\u003c/sup\u003e. Instead, the Landsat data indicates that the CA-wide annual mean shoreline position has varied roughly +/-7m since 1985, with a trend that is not significantly different than zero (dotted black line, \u003cstrong\u003eFigure 3c\u003c/strong\u003e). We explore this result, along with the unexpectedly coherent component of statewide shoreline recovery, with an EOF analysis of the \u0026lt;CAcoastSat\u0026gt; shoreline change data depicted in \u003cstrong\u003eFigure 3a\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003eDominant modes of CA shoreline variability\u003c/h3\u003e\n\u003cp\u003eDominant statewide patterns of annually averaged CA shoreline change are identified with EOF analysis of time series with the time mean removed at each transect and a 5-km running mean. The first two EOF modes account for 45.8% and 5.8% of the total shoreline change variance. The mode 1 spatial pattern is mostly positive with considerable longshore variability, indicating in-phase covariations of varying amplitude (\u003cstrong\u003eFigure 4a,b\u003c/strong\u003e). Mode 1 has notably weak amplitudes in parts of Southern California, presumably due to wave shadowing caused by the shoreline orientation change south of Point Conception and sheltering by offshore islands\u003csup\u003e29,30\u003c/sup\u003e. Mode 1 is well-correlated with ERA5 (r\u003csup\u003e2 \u003c/sup\u003e= 0.76), demonstrating that interannual shoreline variations are strongly correlated with wave power anomalies\u003csup\u003e31\u003c/sup\u003e. Mode 2 represents regional deviations from the dominant statewide shoreline response, with the strongest anomalies in Southern California. This mode primarily reflects localized effects, particularly in the early Landsat record (1988\u0026ndash;1992), when significant spatial differences in shoreline change were observed between northern and southern California (\u003cstrong\u003eFigure 4d,e\u003c/strong\u003e). This period coincided with strong ENSO variability and potential limitations in Landsat-derived shoreline resolution, which may explain the greater prominence of Mode 2 in earlier years. Various measures of wave power variations across the state using the buoy data and ERA5 hindcasts were unable to account for mode 2 change. \u003c/p\u003e\n\u003cp\u003eThe statewide average does not necessarily reflect change at a specific beach or littoral cell\u003csup\u003e32\u003c/sup\u003e . For example, shoreline width trends in San Diego County vary from beach to beach (\u003cstrong\u003eFigure 1b\u003c/strong\u003e). The large (54% of shoreline change variance) residual change at higher spatial resolution reflects local longshore transport, sand supply (including nourishments), and grain size and beach orientation effects. \u003c/p\u003e\n\u003ch3\u003eMode 1 shoreline regulated by central North Pacific swell generation\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eCA wave hindcast backtracking with ESTELA\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e identifies the primary north and south Pacific source regions of below and above average wave power for different \u0026lt;CAcoastSat\u0026gt; mode 1 and ENSO phases (\u003cstrong\u003eFigure 5, see methods\u003c/strong\u003e). We use the hourly wind sea wave partition from the Centre for Australian Weather and Climate Research (CAWCR) wave hindcast\u003csup\u003e35,36\u003c/sup\u003e to analyze the relationships between the wave generation areas and shoreline change. The composite generation averages (Figure 5) are based on the more significant positive and negative years (index over or below the 75th and 25th percentile) for mode 1 (\u003cstrong\u003eFigure 5a-c\u003c/strong\u003e), and similarly the Ni\u0026ntilde;o3.4 index (\u003cstrong\u003eFigure 5d-f\u003c/strong\u003e). \u003c/p\u003e\n\u003cp\u003eThe energy generation anomaly follows a clear pattern, with stronger wave generation in the northern hemisphere winter source region during negative Mode 1 phases (beach loss) and weaker wave generation during positive Mode 1 phases (beach recovery) (\u003cstrong\u003eFigure 5a,b\u003c/strong\u003e). This indicates that higher offshore wave energy leads to shoreline narrowing, while lower offshore wave energy corresponds to beach widening. Contrastingly, a composite analysis using the Ni\u0026ntilde;o3.4 index shows a similar well above average wave energy anomaly pattern in the North Pacific as negative mode 1 during El Ni\u0026ntilde;o years (\u003cstrong\u003eFigure 5c\u003c/strong\u003e), but a well below average northern hemisphere wave anomaly does not reciprocally occur during La Ni\u0026ntilde;a years (\u003cstrong\u003eFigure 5d\u003c/strong\u003e). The source composite of La Ni\u0026ntilde;as instead shows a pattern consistent with a northward shift in more average wave generation. The correspondence of erosion with El Ni\u0026ntilde;o events is well known; however, we find only a weak overall correlation between shoreline change and the ENSO index (r\u003csup\u003e2 \u003c/sup\u003e= 0.21), because non-El Ni\u0026ntilde;o years produce wave power anomalies with significant variability depending on ENSO-related SST anomaly distribution and intensity\u003csup\u003e37\u003c/sup\u003e (\u003cstrong\u003eFigure 5e). \u003c/strong\u003eThis suggests that the below-normal northern hemisphere energy years in (\u003cstrong\u003eFigure 5b\u003c/strong\u003e), associated with mode 1 Landsat shoreline recovery, primarily occur during ENSO neutral years.\u003c/p\u003e\n\u003ch3\u003eLong-term shoreline trends and the PDO\u003c/h3\u003e\n\u003cp\u003eThe ERA5 wave hindcast (1940-2023) is used to construct an 82-yr hindcast of the statewide average annual \u0026quot;wave-driven\u0026quot; mean shoreline change based on the linear regression of 1985-2021 annual ERA5 offshore wave power against statewide averaged \u0026lt;CAcoastSat\u0026gt; (\u003cstrong\u003eFigure 6)\u003c/strong\u003e. The hindcast shoreline position using wave ERA5 (black dashed line, \u003cstrong\u003eFigure 6b)\u003c/strong\u003e compares well with the Landsat shoreline (green dotted line), but underestimates 1984-1987 shoreline retreat and 2012-2015 recovery. Landsat shoreline imagery was relatively low spatial resolution and temporally sparse in the 1980\u0026apos;s and 1990\u0026apos;s, perhaps contributing to larger model errors. \u003c/p\u003e\n\u003cp\u003eThe 82yr shoreline trends negative (black solid line, -0.054 \u0026plusmn; 0.03 m/yr, \u003cstrong\u003eFigure 6b\u003c/strong\u003e) with a statewide average shoreline retreat of ~ 4m since 1940, despite the more recent 1985-2021 period of mean shoreline position stability or slight widening derived from the Landsat images (green lines, \u003cstrong\u003eFigure 6b\u003c/strong\u003e). Retreat is more consistent with the perception of long-term shoreline erosion, particularly after the extensive pre-1960 artificial widening of CA beaches with harbor constructions\u003csup\u003e38\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e. This wave-driven estimate does not consider sand supply changes (river inputs, nourishments). Alternating multi-decadal time periods of modeled statewide accretive and erosive shoreline trends (negative and positive ERA5 wave power trends) align with cold (1947-1976, 2000-present) and warm (1977-1999) phases of the PDO (\u003cstrong\u003eFigure 6\u003c/strong\u003e). Longer-term shifts in atmospheric circulation and ocean heat content may also modulate wave power trends, influencing shoreline evolution beyond PDO-driven variability\u003csup\u003e39-43\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFollowing refs.\u003csup\u003e13,14\u003c/sup\u003e by parsing the hindcast into PDO cold and warm phases (blue and red shaded time periods, \u003cstrong\u003eFigure 6\u003c/strong\u003e), the power and shoreline trends are positive/negative in cold/warm phases (purple trends lines, \u003cstrong\u003eFigure 6\u003c/strong\u003e). The hindcast of the recent (2000-2021) cold phase trends compare favorably with the observed buoy (red solid line, \u003cstrong\u003eFigure 6a\u003c/strong\u003e) and \u0026lt;CAcoastSat\u0026gt; (green solid line, \u003cstrong\u003eFigure 6b\u003c/strong\u003e) trends. The PDO cold/warm phases are defined by the sign of the PDO index after applying a 5-yr running mean to the monthly PDO index to suppress ENSO modulation of the PDO signal\u003csup\u003e44,45\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalogous to seasonal beach change in CA\u003csup\u003e17,18\u003c/sup\u003e , mode 1 represents the cross-shore transport and storage of sand on interannual time scales. During energetic wave years sand moves offshore and the shoreline narrows until subsequent annual mean wave conditions favor shoreward transport. The Landsat estimates of more extreme annual beach loss (1985-1987) and recovery (2011-2013), associated with consecutive years of above or below average wave power, are underpredicted by an ERA5 regression hindcast model (\u003cstrong\u003eFigure 6)\u003c/strong\u003e. These errors are possibly an interannual cross-shore sand distribution analog to shoreline evolution model errors owing to missing seasonal bar dynamics\u003csup\u003e46\u003c/sup\u003e . The finding that this onshore-offshore exchange has a mode 1 statewide coherent expression tied to the offshore wave climate, despite significant nearshore wave climate mean energy variation owing to island sheltering and coastal orientations, is notable. This suggests that offshore power anomalies lead to nearshore anomalies of the same sign but different magnitudes about their local means. The larger/smaller self-selected grain sizes associated with the higher/lower mean energy beaches\u003csup\u003e47\u003c/sup\u003e would further contribute to more similar cross-shore transport rates and more coherent interannual beach loss and recovery. \u003c/p\u003e\n\u003cp\u003eThese findings have direct implications for coastal management and long-term hazard mitigation along the CA coast. A shoreline change climatic index\u003csup\u003e11\u003c/sup\u003e based on the sign and strength of the central North Pacific winter west wind anomaly could provide annual guidance for CA shoreline managers in conjunction with seasonal to interannual statistical and machine learning models like ref.\u003csup\u003e48\u003c/sup\u003e. Anticipated beach recovery phases during neutral ENSO conditions could guide the timing of shoreline protection and restoration projects. Future work should further investigate the variability of recovery dynamics, given that swell magnitude propagating from the Southern Ocean storm track shows a strong positive correlation with the Southern Annular Mode particularly during Northern Hemisphere summer months\u003csup\u003e49\u003c/sup\u003e. While our results indicate no significant net statewide shoreline loss over the past 37 years, the long-term wave-driven retreat trend underscores the vulnerability of California\u0026rsquo;s beaches to changing climatic conditions. As sea level rise accelerates beach erosion through the end of the century\u003csup\u003e50\u003c/sup\u003e, integrating interannual wave-driven shoreline behavior into resilience planning will be critical for protecting coastal infrastructure, ecosystems, and recreational spaces.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eCoastSat Shoreline Location Time Series\u003c/h3\u003e\n\u003cp\u003eThe CoastSat\u003csup\u003e7\u003c/sup\u003e multi-decadal time series of shoreline positions greatly expands the historical database. Online CoastSat 1985-2021 shoreline position time series (http://coastsat.wrl.unsw.edu.au/), determined by algorithmically detecting the instantaneous water-sand and/or predominant wet-dry sand interface in individual satellite images for coastal reaches with sand or gravel beaches. The shoreline positions (\u003cem\u003ee.g. \u003c/em\u003eupper panel, \u003cstrong\u003eFigure 1a\u003c/strong\u003e) are tide-corrected relative to MSL (but not wave runup corrected) using a single, long-term mean beach slope estimated from the uncorrected position time series and estimated tide levels, and spatially averaged onto 100m-spaced cross-shore transects.\u003c/p\u003e\n\u003ch3\u003eSurveyed Mean High Water Contour Location Time Series\u003c/h3\u003e\n\u003cp\u003eThe MSL tide-corrected CoastSat annual mean positions represent approximately the surveyed mean high water (MHW) elevation contour, higher than MSL owing to wave runup\u003csup\u003e51\u003c/sup\u003e\u003cem\u003e. \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom 2002-2010, the San Diego County coastline was surveyed in spring and fall using airborne topographic LiDAR. From 2001-2021, various San Diego beaches were surveyed with ATV, truck-mounted Lidar and push dolly-mounted mobile GPS. The mobile GPS surveys overlap in time and space with both the airborne and mobile LiDAR data sets and have been used to ensure survey accuracy and consistency between data collection methods\u003csup\u003e52\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e53-56\u003c/sup\u003e. From 2017-2021, truck and ATV-mounted mobile LiDAR surveys were monthly or quarterly at many San Diego beaches. \u003c/p\u003e\n\u003cp\u003eTime series of the mean high water contour location on 100m-spaced cross-shore transects\u003csup\u003e57\u003c/sup\u003e (MOP lines) were derived from the survey data (\u003cem\u003ee.g. \u003c/em\u003elower panel,\u003cem\u003e \u003c/em\u003e\u003cstrong\u003eFigure 1a\u003c/strong\u003e). The alongshore locations of the MOP and CoastSat 100m-spaced transects are interlaced, but their orientations typically differ by less than a few degrees. The five San Diego County beaches with the most comprehensive coverage were used for CoastSat validation. \u003c/p\u003e\n\u003ch3\u003eAnnual Mean Shoreline Change Estimation\u003c/h3\u003e\n\u003cp\u003eTo minimize biases owing to temporally irregular sampling, both survey MHW and CoastSat transect time series are averaged in time and alongshore space (\u0026lt;CAcoastSat\u0026gt;,\u0026lt;Survey MHW\u0026gt;). Based on the CoastSat-survey comparisons in San Diego County (Figure 1), any seasonal biases and random errors associated with unresolved wave runup appear to be minimized in the annual averages. First, the time series are reduced to annual means (October-September) by progressively time averaging to monthly, quarterly, semi-annual, and finally annual values. Second, the time series are further reduced to annual mean anomalies, by removing their respective long-term means, to minimize any spatial offsets between the CoastSat and survey (GPS) geographic reference frames (\u003cstrong\u003eFigure 1b)\u003c/strong\u003e. Finally, for the survey-CoastSat comparisons at each beach, a single space-time averaged annual shoreline anomaly time series is derived by spatially averaging over 10-30 transects depending on beach length. For the statewide EOF analysis, a fixed 5km (50 transect) running mean is used.\u003c/p\u003e\n\u003ch3\u003eEmpirical Orthogonal Function analysis of shoreline change data\u003c/h3\u003e\n\u003cp\u003eAt each grid point, a singular value decomposition (using svd.m in MATLAB) is applied to the Landsat annual shoreline change data, i.e., annual shoreline position first differenced in time. A temporal mean is removed from the first-differenced time series, equivalent to removing a linear trend from the shoreline position data. Each location is smoothed with a 5km alongshore running mean. The modal spatial patterns in \u003cstrong\u003eFigure 4b \u003c/strong\u003eare proportional to the temporal standard deviation of each mode. \u003c/p\u003e\n\u003ch3\u003eOffshore Annual Mean Wave Power Anomaly Estimates\u003c/h3\u003e\n\u003cp\u003eWave forcing (periods 2-40 sec) is characterized as the offshore wave energy flux, or power (units m\u003csup\u003e3\u003c/sup\u003e/s), using Coastal Data Information Program (CDIP) directional wave buoy observations from 2000-2022\u003csup\u003e23\u003c/sup\u003e . Temporal fluctuations in the observed annual wave power (energy flux) anomalies covary along the CA offshore boundary. Mean wave power is 25% higher in north- than south-central CA (\u003cstrong\u003eFigure 2b\u003c/strong\u003e) but annual mean anomalies (relative to means) are of similar magnitude and well correlated (\u003cstrong\u003eFigure 2c). \u003c/strong\u003eSmaller positive wave power anomalies are associated with (summer) Southern Ocean swell (\u003cstrong\u003eFigure 2e\u003c/strong\u003e) \u003c/p\u003e\n\u003cp\u003eHistorical 2001-2021 annual mean offshore wave power anomalies are derived from a single continuous wave power record of blended deep-water buoy observations. CDIP buoys are used preferentially, followed by nearby NOAA buoys, with any remaining time gaps filled by long-term monthly means. For central and southern CA, 96% of the wave power time series is from the CDIP Harvest buoy station west of Pt Conception, with the limited data gaps filled by data from the nearby CDIP Harvest Southeast buoy, the San Nicolas Island buoy or nearby NOAA buoys. Remaining data gaps (~ 1%) were filled with the time series mean for the same time of year (30 day running average). For north-central CA, waves are mostly from the CDIP Pt Reyes buoy station (93%), with data gaps filled by CDIP Monterey Bay West buoy (4%), and with the CDIP Pt Sur, NOAA 46042 and 46013 buoys.\u003c/p\u003e\n\u003ch3\u003eThe ECMWF ERA5 wave hindcast annual mean wave power estimates\u003c/h3\u003e\n\u003cp\u003eThe wave ERA5 reanalysis\u003csup\u003e9\u003c/sup\u003e (Copernicus Climate Change Service [C3S], 2017) from the European Centre for Medium-Range Weather Forecast (ECMWF) provides hourly, global waves with 0.5\u0026ordm; spatial resolution from 1940 to present. Year to year changes in annual wave power are similar in ERA5 and at the Harvest and Pt Reyes Buoys (\u003cstrong\u003eFigure\u003c/strong\u003e \u003cstrong\u003e3c\u003c/strong\u003e). Harvest and ERA5 annual power anomalies, and therefore power change, are significantly correlated (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e = 0.84). \u003c/p\u003e\n\u003cp\u003eThe statewide mean \u0026lt;CAcoastSat\u0026gt; is regressed on to ERA5 wave power, yielding an estimate for the mean CA shoreline anomaly based on the wave power anomaly times -0.61 (+/- 0.27) m (m\u003csup\u003e3\u003c/sup\u003e/s)\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eESTELA analysis\u003c/h3\u003e\n\u003cp\u003eThe ESTELA (Evaluation of Source and Travel-time of wave Energy reaching a Local Area) method\u003csup\u003e19\u003c/sup\u003e estimates the source location and propagation time of waves approaching a specific location. Unlike previous studies that used both the sea and swell wave partitions\u003csup\u003e58,59\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e, we use the hourly wind sea wave partition from the Centre for Australian Weather and Climate Research (CAWCR) wave hindcast\u003csup\u003e35,36\u003c/sup\u003e to better isolate wave generation regions. \u003c/p\u003e\n\u003cp\u003eTo analyze the relationship between the wave generation areas and the shoreline response, composite averages are computed using the mode 1 index (over or below the 75th and 25th percentile). The anomaly of the energy generation with respect to the mean (\u003cstrong\u003eFigure 5a,b\u003c/strong\u003e) displays a clear positive/negative anomaly in the northern hemisphere during the negative/positive phase of the index, i.e., more energetic waves in the generation region correspond to narrower CA shorelines and vice versa. A composite using Oct-Sep years when the Ni\u0026ntilde;o3.4 index was above/below 1/-1\u0026deg;C for more the 5 months shows a similar pattern as mode 1 during El Ni\u0026ntilde;o years (\u003cstrong\u003eFigure 5c\u003c/strong\u003e), but well below normal wave conditions do not generally occur during La Ni\u0026ntilde;a Years (\u003cstrong\u003eFigure 5c\u003c/strong\u003e) leading to a poor overall correlation between mode 1 shoreline change and the Ni\u0026ntilde;o3.4 index.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThis study was funded by the U.S. Army Corps of Engineers (W912HZ1920020), the California Department of Parks and Recreation, Natural Resources Division Oceanography Program (C19E0026), and the Office of Naval Research (N00014-23-1-2170).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eStorlazzi, C. D., \u0026amp; Griggs, G. B. 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Satellite-derived shoreline detection at a high-energy meso-macrotidal beach, \u003cem\u003eGeomorphology\u003c/em\u003e, \u003cstrong\u003e383\u003c/strong\u003e, https://doi.org/10.1016/j.geomorph.2021.107707, (2021).\u003c/li\u003e\n \u003cli\u003eYates, M. L, Guza, R. T., \u0026amp; O\u0026rsquo;Reilly, W.C. Beach shoreline change: Observations and equilibrium modeling. \u003cem\u003eJ. Geophys Res\u003c/em\u003e, \u003cstrong\u003e114\u003c/strong\u003e, C09014, doi:10.1029/2009JC005359 (2009).\u003c/li\u003e\n \u003cli\u003eMatsumoto, H., Young, A. P., \u0026amp; Guza, R. T. Observations of surface cobbles at two southern California beaches. \u003cem\u003eMar. Geol\u003c/em\u003e., \u003cstrong\u003e419\u003c/strong\u003e, 106049 (2020a).\u003c/li\u003e\n \u003cli\u003eMatsumoto, H., Young, A. P., \u0026amp; Guza, R. T. Cusps and mega cusps on a mixed sediment beach. \u003cem\u003eEarth and Space Sci.,\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, e2020EA001366. https://doi.org/10.1029/2020EA001366-T (2020b).\u003c/li\u003e\n \u003cli\u003eMatsumoto, H., \u0026amp; Young, A. P. Quantitative regional observations of gravel and bedrock influence on beach morphologies, \u003cem\u003eGeomorphology\u003c/em\u003e, \u003cstrong\u003e419\u003c/strong\u003e, 108491 (2022).\u003c/li\u003e\n \u003cli\u003eSiegelman, M. N., McCarthy, R. A., Young, A. P., \u003cem\u003eet al.\u003c/em\u003e Subaerial profiles at two beaches: Equilibrium and machine learning. \u003cem\u003eJ. Geophys. Res.: Earth Surface\u003c/em\u003e, \u003cstrong\u003e129\u003c/strong\u003e, e2023JF007524. https://doi.org/10.1029/2023JF007524 (2024).\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Reilly, W. C., Olfe, C. B., Thomas, J., Seymour, R. J., \u0026amp; Guza, R. T. The California coastal wave monitoring and prediction system, \u003cem\u003eCoastal Eng.\u003c/em\u003e, \u003cstrong\u003e116\u003c/strong\u003e, 118-132 (2016).\u003c/li\u003e\n \u003cli\u003eCagigal, L., Rueda, A., Anderson, D., \u003cem\u003eet al.\u003c/em\u003e A multivariate, stochastic, climate-based wave emulator for shoreline change modelling. \u003cem\u003eOcean Modelling\u003c/em\u003e, \u003cstrong\u003e154\u003c/strong\u003e, 101695. https://doi.org/10.1016/j.ocemod.2020.101695 (2020).\u003c/li\u003e\n \u003cli\u003eCamus, P., M\u0026eacute;ndez, F. J., Losada, I. J., \u003cem\u003eet al.\u003c/em\u003e A method for finding the optimal predictor indices for local wave climate conditions. \u003cem\u003eOcean Dyn.\u003c/em\u003e, \u003cstrong\u003e64\u003c/strong\u003e(7), 1025\u0026ndash;1038. https://doi.org/10.1007/s10236-014-0737-2 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6500020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6500020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOur understanding of how wave climate variability drives shoreline evolution has advanced substantially in recent decades with increasing satellite imagery, wave buoy records, and wave hindcast models. While severe beach erosion with extreme El Ni\u0026ntilde;o waves is well documented on Pacific coastlines, the broader link between interannual wave energy and shoreline response has remained less clear. Here, we show nearly half of California's interannual Landsat shoreline change is a coherent response to wave power anomalies originating from a specific central North Pacific swell generation region, which in turn is weakly correlated with the Ni\u0026ntilde;o3.4 index. Positive wave power anomalies (beach loss) are strongly associated with El Ni\u0026ntilde;os, but the negative anomalies (beach recovery) are not similarly tied to La Ni\u0026ntilde;as. Cumulative change in the CA statewide mean shoreline position is small over the 37-yr Landsat era but an 83-yr wave hindcast suggests a statewide wave-driven retreat of ~\u0026thinsp;4m loss since 1941. These results provide additional insight into the role of the North Pacific wave climate in modulating beach width retreat and recovery over interannual to multi-decadal timescales, with implications for long-term coastal resilience planning.\u003c/p\u003e","manuscriptTitle":"Interannual Wave-Driven Shoreline Change on the California Coast","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:04:42","doi":"10.21203/rs.3.rs-6500020/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7b232101-edd1-4ca5-8201-763e045155ef","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47902999,"name":"Earth and environmental sciences/Ocean sciences/Physical oceanography"},{"id":47903000,"name":"Earth and environmental sciences/Climate sciences/Ocean sciences"}],"tags":[],"updatedAt":"2025-11-18T08:06:32+00:00","versionOfRecord":{"articleIdentity":"rs-6500020","link":"https://doi.org/10.1038/s41467-025-65944-0","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-11-17 05:00:00","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-05-07 06:04:42","video":"","vorDoi":"10.1038/s41467-025-65944-0","vorDoiUrl":"https://doi.org/10.1038/s41467-025-65944-0","workflowStages":[]},"version":"v1","identity":"rs-6500020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6500020","identity":"rs-6500020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}