35 Bottom temperature - Seasonal Anomaly

Description: The data are seasonal bottom temperature anomaly time series for each EPU

Indicator family:

Contributor(s): Joseph Caracappa, Hubert du Pontavice, Vincent Saba, Zhuomin Chen

Affiliations: NEFSC

35.1 Introduction to Indicator

The raw bottom temperature product is in a horizontal 1/12 degree grid between 1959 and 2023 and is made of daily bottom temperature estimates from:

Bias-corrected ROMS-NWA (ROMScor) between 1959 and 1992 which was regridded in the same 1/12degree grid as GLORYS using bilinear interpolation; GLORYS12v1 in its original 1/12 degree grid between 1993 and 2023-08-29; and PSY in it’s 1/12 degree grid from 2023-08-29 to 2023-12-31.

Anomalies are calculated using the 1990-2020 reference period.

35.2 Key Results and Visualizations

Time series plots for seasonal bottom temperature anomaly for each EPU shows a long-term warming trend. PSY forecasted data also included for comparison and indicates varying skill in predicting regional bottom temperature.

35.2.1 MidAtlantic

35.2.2 NewEngland

35.3 Indicator statistics

Spatial scale: EPU

Temporal scale: annual-seasonal

Synthesis Theme:

35.4 Implications

Bottom temperature is an important driver for benthic and demersal species growth, metabolism, and reproduction. Changes in seasonal bottom temperature have implications for species’ phenology

35.5 Get the data

Point of contact:

ecodata name: ecodata::bottom_temp_comp

Variable definitions

Season: 1 = winter (January – March), 2 = spring (April – June), 3 = summer (July – September), 4 = fall (October – December) Subarea: EPU name

Source: ROMS (bias-corrected ROMS-NWA bottom temperature [59]), GLORYS (CMEM’s GLORYS12V1 global reanalysis bottom temperature), PSY (CMEM’s PSY global forecast bottom temperature)

bt_temp : mean bottom temperature for each year/season across entire EPU ref_bt: bottom temperature climatology for season/EPU based on 1990-2020

Indicator Category:

35.6 Public Availability

Source data are publicly available.

35.7 Accessibility and Constraints

No response

tech-doc link https://noaa-edab.github.io/tech-doc/bottom_temp_comp.html

References

59.
Pontavice H du, Miller TJ, Stock BC, Chen Z, Saba VS. Ocean model-based covariates improve a marine fish stock assessment when observations are limited. Hidalgo M, editor. ICES Journal of Marine Science. 2022;79: 1259–1273. doi:10.1093/icesjms/fsac050