DOI: 10.5061/dryad.z08kprrjr
title: Ecological forecasts for marine resource management during climate extremes

description: Forecasting weather has become commonplace, but as society faces novel and
 uncertain environmental conditions there is a critical need to forecast
 ecology. Forewarning of ecosystem conditions during climate extremes can
 support proactive decision-making, yet applications of ecological
 forecasts are still limited. We showcase the capacity for existing marine
 management tools to transition to a forecasting configuration and provide
 skilful ecological forecasts up to 12 months in advance. The management
 tools use ocean temperature anomalies to help mitigate whale entanglements
 and sea turtle bycatch, and we show that forecasts can forewarn of
 human-wildlife interactions caused by unprecedented climate extremes. We
 further show that regionally downscaled forecasts are not a necessity for
 ecological forecasting and can be less skilful than global forecasts if
 they have fewer ensemble members. Our results highlight capacity for
 ecological forecasts to be explored for regions without the infrastructure
 or capacity to regionally downscale, ultimately helping to improve marine
 resource management and climate adaptation globally.
description: Summary
 We configure two existing resource management tools, originally configured to use observed (historical) ocean temperatures, to a forecasting system and conduct a retrospective forecast to test their skill. We first conducted a retrospective forecast using global forecasts (73 ensemble members) across the full historically available period (1981-2020) – termed the ‘Global’ model. Global forecasts of monthly sea surface temperature were obtained from the North American Multimodel Ensemble (NMME; Table S1; https://www.cpc.ncep.noaa.gov/products/NMME/). We then compared the performance of three forecast configurations: First, we used global forecasts (73 ensemble members) across a reduced historical period (1981-2010) - termed the ‘Global Full Ensemble’. Second, we used forecasts regionally downscaled (3 ensemble members) to the CCE for the same reduced historical period (1981-2010) - termed the ‘Downscaled Ensemble’. Third, we used a reduced subset of the global forecasts (3 ensemble members) for the same reduced historical period (1981-2010) - termed the ‘Global Reduced Ensemble’.   All forecasts are compared to SST observations, extracted from a CCE regional reanalysis. This reanalysis is based on the Regional Ocean Modeling System (ROMS) and covers the west coast of the U.S. (30-48˚N, 134-115.5˚W) with 0.1 degree (~10 km) horizontal resolution and 42 terrain-following vertical levels. Case Study 1: Habitat Compression Index  The Habitat Compression Index (HCI) is a regionally resolved measure of cool thermal habitat along the U.S. West Coast; the index presented here monitors surface water conditions off California (35-40°N). The HCI is used to assess the degree to which upwelling habitat (indicated by cool water) is compressed against the coast, as nutrient-rich upwelled waters attract whales seeking enhanced foraging opportunities. The HCI was calculated as the number of grid cells with SST lower than a monthly SST threshold within 150 km of the coastline. The HCI was normalized by the total number of grid cells of the 150 km domain to scale values from 0 to 1.  Monthly SST thresholds are the mean monthly SST from 1981-2010 from the coast to 75 km offshore. Low HCI values represent high compression, or reduction of cool thermal habitat, and are the primary interest to resource managers tasked with mitigating whale entanglement risk. The long-term mean of the HCI is used to identify a high compression event (i.e. values below the mean. Case Study 2: TOTAL Tool The Temperature Observations to Avoid Loggerheads (TOTAL) tool monitors anomalously high SST in the Southern California Bight (31-34°N, 120-116°W) as an indicator of turtle bycatch risk and to recommend potential implementation of a fishery closure. TOTAL was calculated as the six-month rolling mean of SST anomalies in the Southern California Bight domain. The spatial closure is potentially enacted during three months of the year (June, July, August) based on SSTA of the preceding six months. If SSTA exceeds a threshold, calculated as the minimum monthly anomaly value preceding three historical closure periods (Aug 2014, Jun-Aug 2015, & Jun-Aug 2016), a closure is recommended.  Skill assessment Forecast skill of each management tool was assessed by comparing observed and forecast values using three metrics: (1) correlation coefficient, which provides a statistical measure of the strength of a linear relationship between observed and forecast values; (2) forecast accuracy, which indicates the fraction of correct forecasts; and (3) the Symmetric Extremal Dependence Index (SEDI) which has several properties that make it well suited to quantifying skill for rare events. Details and equations for metrics are described in the manuscript.
description: Details for each dataset are provided in the README
 file. .rds and raster files can be opened in R
 statistical software.  netcdf files can be opened in
 multiple softwares.  Datasets included: (1) Regionally downscaled Sea Surface Temperature Forecasts Files used to calculate the downscaled HCI and TOTAL forecasts (2) HCI: Habitat Compression Index Files used to calculate the HCI forecast, and the HCI forecasts from1985-2020 (3) TOTAL: Temperature Observations to Avoid Loggerheads Files used to calculate the TOTAL forecast, and the TOTAL forecasts from1985-2020 (4) Source_data Source data for each published figure in the manuscript
description: This README file was generated on 2023-10-17 by Stephanie Brodie **GENERAL
 INFORMATION** 1.Title of Dataset: Ecological forecasts for marine resource
 management during climate extremes 2.Lead author Information: Stephanie
 Brodie, University of California Santa Cruz, sbrodie@ucsc.edu 3.Study
 area: California Current System, USA 4.Funding: Funding was provided by
 the NOAA Climate Program Office Modeling, Analysis, Prediction, and
 Projections program (NA17OAR4310108), the California Current Integrated
 Ecosystem Assessment (no grant number), and the California Ocean
 Protection Council (2021-242-UCSC). **SHARING/ACCESS INFORMATION** 1\.
 Data used and generated are from this publication: Brodie et al\.\, \(in
 press\)\. Ecological forecasts for marine resource management during
 climate extremes\. Nature Communications\. 2\. Links to ancillary data
 sets: * Global SST forecasts can be accessed at the North American
 Multi-Model Ensemble
 ([http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/](http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/)). * Output from the UCSC CCS reanalysis is available from [https://oceanmodeling.ucsc.edu](https://oceanmodeling.ucsc.edu). * The real-time HCI (https://oceanview.pfeg.noaa.gov/whale\_indices/) and TOTAL (https://coastwatc h.pfeg.noaa.gov/loggerheads/index.html) management tools are publicly accessible. * Corresponding code used to generate forecasts is on GitHub: https://github.com/stephbrodie1/Ecological\_Forecast. Cited as: Brodie, Stephanie, Pozo Buil, Mercedes, Welch, Heather, Bograd, Steven, Hazen, Elliott, Santora, Jarrod, Seary, Rachel, Schroeder, Isaac, and Jacox, Michael (2023). Ecological forecasts for marine resource management during climate extremes. https://doi.org/10.5281/zenodo.8429074. * Additional details on downscaling methods are described in: Jacox, M., Pozo Buil, M., Brodie, S., Alexander, M., Amaya, D., Bograd, S., Edwards, C.A., Fiechter, J., Hazen, E., Hervieux, G., et al. (in press). Downscaled seasonal forecasts for the California Current System: Skill assessment and prospects for living marine resource applications. PlosOne. 10.1371/journal.pclm.0000245 3\. Recommended citation for this dataset: Brodie, Stephanie, Pozo Buil, M., Welch, Heather, Bograd, Steven, Hazen, Elliott, Santora, Jarrod, Seary, Rachel, Schroeder, Isaac, and Jacox, Michael (2023). Ecological forecasts for marine resource management during climate extremes. doi:10.5061/dryad.z08kprrjr. **DATA AND FILE OVERVIEW** 1\. Folder: HCI * Global\_hci\_forecast folder: forecast HCI for each global model 'forecast\_allmembers\_HCI\_1981-2021.rds'. The ensemble mean of all global models and their ensembles ('forecast\_ensmean\_HCI\_1981-2021.rds') and the ensemble of only 3 ensemble members from CanCM4 ('forecast\_ensmean\_only3\_HCI\_1981-2010.rds') are provided as separate files. * Downscaled\_hci\_forecast folder: forecast HCI for the downscaled models at each initialisation time ('downscaled\_forecast\_allmembers\_HCI\_1981-2010.rds'). The ensemble mean is provided as a separate file ('downscaled\_forecast\_ensmean\_HCI\_1981-2010.rds'). * Column name description for these files: * glob\_model: the global model (see below for full names fro acronyms) * ensemble: numeric ensemble number * init\_month: initialisation month * lead\_month: forecast lead month * forecast\_date: forecast month * sst\_area: number of grid cells used in the calculation of the habitat compression index * area: the habitat compression index value 2\. Folder: TOTAL * Global\_total\_forecast\_1981-2020 folder: forecast TOTAL for each global model ('total\_metric\_globalforecasts\_1980-2020\_bestcase.rds') (1981-2020) and the ensemble mean ('total\_metric\_globalforecasts\_1980-2020\_bestcase\_ensemble.rds') (1981-2020) * Global\_total\_forecast\_1981-2010 folder: forecast TOTAL for each global model ('total\_metric\_allforecasts\_1980-2010\_apples.rds') (1981-2010) and the ensemble mean of TOTAL forecast ('total\_metric\_globalforecasts\_1980-2010\_apples\_ensemble.rds') (1981-2010) * Global\_total\_forecast\_3ens\_1981-2010: ensemble mean of TOTAL forecast (1981-2010) for the 3 ensemble members of CanCm4 ('total\_metric\_globalforecasts\_1980-2010\_only3\_ensemble.rds') * Downscaled\_total\_forecast\_1981-2010: downscaled forecast TOTAL ('total\_metric\_globalforecasts\_1980-2010\_apples\_ensemble.rds') (1981-2010) * Column name description for these files: * glob\_model: the global model (see below for full names fro acronyms) * ensemble: numeric ensemble number * lead year: year of forecast * june\_fcast: TOTAL forecast for the june closure month * june\_obs: TOTAL observed for the june closure month * jul\_fcast: TOTAL forecast for the july closure month * jul\_obs: TOTAL observed for the july closure month * aug\_fcast: TOTAL forecast for the august closure month * aug\_obs: TOTAL observed for the august closure month Global Model Acronyms: * CanCM4i: Canadian Center for Climate Modeling Analysis * GEM\_NEMO: Global Environmental Multiscale - Nucleus for 10 European Modelling of the Ocean * GFDL-SPEAR: Geophysical Fluid Dynamics Laboratory- Seamless System for Prediction and Earth System Research * NASA-GEOSS2S: National Aeronautics and Space Administration - Goddard Earth Observing System Subseasonal to Seasonal * COLA-RSMAS-CCSM4: Center for Ocean Land Atmosphere Research - Rosenstiel School of Marine, Atmospheric, and Earth Science - Community Climate System Model version 4.0 * NCEP-CFSv2: National Centers for Environmental Prediction - Climate Forecast System Version 2 3\. Folder: Source\_data File name correspond to figures in the published manuscript. Column name description for these files: fig1\_bottomleft\_ts.csv * year: year * month: month * area: observed habitat compression index * date: dated to be the first day of the month-year for plotting fig1\_bottomright\_ts.csv * year: year * month: month * day: first day of the month * date: dated to be the first day of the month-year for plotting * sst: sea surface temperature * sst\_clim: sst climatology * obs\_anom: sst anomaly * roll\_obs\_anom: 6 month rolling mean of the sst anomaly fig1\_topleft\_map.csv * x: longitude in 0-360 degrees * y: latitude * value: sea surface temperature * lon: longitude in -180-180 degrees fig1\_topright\_map.csv * x: longitude * y: latitude * value: sea surface temperature fig2\_bottom.csv * month: month * lead: forecast lead month * cor: correlation value of the retrospective forecasts * cor\_sig: binary value of whether correlation is significant (1) or not (0) * acc: accuracy of the retrospective forecasts * acc\_sig: binary value of whether accuracy is significant (1) or not (0) * sedi: Symmetric Extremal Dependence Index (sedi) of the retrospective forecasts * sedi\_sig: binary value of whether sedi is significant (1) or not (0) fig2\_top.csv * forecast\_date: habitat compression index date * type: observed or forecast * area: habitat compression index value * lead\_month: lead months for forecast * mean: threshold used to define habitat compression * group: a binary value indicating high compression (1) or not (0) fig3\_bottom.csv * lead: forecast lead month * correlation\_months: TOTAL closure month corresponding to correlation\_value * correlation\_values: correlation value of the retrospective forecasts * cor\_sig\_months: TOTAL closure month corresponding to cor\_sig\_value * cor\_sig\_values: binary value of whether correlation is significant (1) or not (0) * acc\_months: TOTAL closure month corresponding to acc\_value * acc\_values: accuracy value of the retrospective forecasts * acc\_sig\_months: TOTAL closure month corresponding to acc\_sig\_value * acc\_sig\_values: binary value of whether accuracy is significant (1) or not (0) * sedi\_months: TOTAL closure month corresponding to sedi\_value * sedi\_values: sedi value of the retrospective forecasts * sedi\_sig\_months: TOTAL closure month corresponding to sedi\_sig\_value * sedi\_sig\_values: binary value of whether accuracy is significant (1) or not (0) fig3\_top.csv * forecast\_date: forecast date * type: observed or forecast * sst\_anom\_rolling: sea surface temperature anomaly 6 month rolling mean * lead: lead month * ssta\_quantile: threshold used to identify potential closures fig4.csv * month: forecast month * lead: lead time * model: type of restrospective forecast test time period used * values: habitat compression index * tool: habitat compression tool * metric: skill metric (correlation, accuracy, and sedi) 4\. NETCDF FILES The regionally downscaled sea surface temperature files are provided here. File name structure: sst\_forecset\_ens\_initialisation\_monthavg\_1982\_2010.nc, where: ens: indicates the ensemble member (either 2, 8, 10, or the ensemble mean) initialisation: indicates whether forecasts were initialised in January (init1) or July (init7). Files contain sea surface temperature (sst) for every grid cell (lat and lon) for each lead time, ensemble member, and initialisation time. 5\. HOW TO OPEN FILES RDS files can be opened in R, such as: readRDS(filename.rds) Netcdf files can be viewed through Panoply, or opened in R. For example: install.packages("ncdf4") library(ncdf4) data &lt;- nc\_open(‘FILENAME.nc’) print(data) <br>