1 Descriptive Section

1.1 Indicator category

[1] “Habitat-Physical”

1.2 Indicator name

Sea Surface Temperature - Ocean Model

Includes variable(s): Fall-hindcast, forecast-1, forecast-10, forecast-2, forecast-3, forecast-4, forecast-5, forecast-6, forecast-7, forecast-8, forecast-9, forecast-Ensemble Mean, Spring-hindcast, Summer-hindcast, Winter-hindcast

1.3 Indicator brief description

The data presented here are seasonal sea surface temperature anomalies for the period 1993-2019 from the MOM6 hindcast ocean model for the Northwest Atlantic. Also shown here are 10-year forecasts of sea surface temperature anomalies (2023-2033) from the MOM6 decadal forecast ocean model for the Northwest Atlantic.

1.4 Indicator visualization

Here we show seasonal sea surface temperature anomalies in the Mid Atlantic Bight (MAB), Georges Bank (GB) and Gulf of Maine (GOM) EPUs. Data from 1993 through 2019 are from NWA MOM6 ([72]) with a base period for the climatology of 1993 through 2010. We do not compare these anomalies to those from observed OISST as the base period for that dataset is 1982-2010. We also show the 10-year forecast for annual sea surface temperature anomalies in the MAB, GB, and GOM. This forecast is from the decadal forecast of NWA MOM6 ([42]), which was initialized in 2022 and ran for 10 years. The forecast indicates a stagnation in surface temperature warming over the next ten years. Retrospective forecasts, where a forecast is initiated in the past and output is compared to observations, have shown that the NWA MOM6 decadal forecast is able to correctly predict periods of temperature stability and rapid warming in this region ([42]).

2 SMART Attribute Section

2.1 Indicator documentation

2.1.1 Are indicators available for others to use (data downloadable)?

Yes

2.1.1.1 Where can indicators be found?

Data: https://noaa-edab.github.io/ecodata/index.html

Description: https://noaa-edab.github.io/catalog/surface_temp_mom6.html

Technical documentation: https://noaa-edab.github.io/tech-doc/surface_temp_mom6.html

2.1.1.2 How often are they updated? Are future updates likely?

[need sequential look at datasets for update frequency. Future requires judgement]

2.1.1.3 Who is the contact?

Laura Gruenburg (laura.gruenburg@noaa.gov)

2.1.2 Gather indicator statistics

2.1.2.1 Units

Indicator	Units
Fall-hindcast	degrees C
forecast-1	degrees C
forecast-10	degrees C
forecast-2	degrees C
forecast-3	degrees C
forecast-4	degrees C
forecast-5	degrees C
forecast-6	degrees C
forecast-7	degrees C
forecast-8	degrees C
forecast-9	degrees C
forecast-Ensemble Mean	degrees C
Spring-hindcast	degrees C
Summer-hindcast	degrees C
Winter-hindcast	degrees C

2.1.2.2 Length of time series, start and end date, periodicity

General overview: Winter (January, February, March), Spring (April, May, June), Summer (July, August, September), Fall (October, November, December), and Annual

Indicator specifics:

Indicator	EPU	StartYear	EndYear	NumYears
Fall-hindcast	GB	1993	2019	27
Fall-hindcast	GOM	1993	2019	27
Fall-hindcast	MAB	1993	2019	27
forecast-1	GB	2023	2032	10
forecast-1	GOM	2023	2032	10
forecast-1	MAB	2023	2032	10
forecast-10	GB	2023	2032	10
forecast-10	GOM	2023	2032	10
forecast-10	MAB	2023	2032	10
forecast-2	GB	2023	2032	10
forecast-2	GOM	2023	2032	10
forecast-2	MAB	2023	2032	10
forecast-3	GB	2023	2032	10
forecast-3	GOM	2023	2032	10
forecast-3	MAB	2023	2032	10
forecast-4	GB	2023	2032	10
forecast-4	GOM	2023	2032	10
forecast-4	MAB	2023	2032	10
forecast-5	GB	2023	2032	10
forecast-5	GOM	2023	2032	10
forecast-5	MAB	2023	2032	10
forecast-6	GB	2023	2032	10
forecast-6	GOM	2023	2032	10
forecast-6	MAB	2023	2032	10
forecast-7	GB	2023	2032	10
forecast-7	GOM	2023	2032	10
forecast-7	MAB	2023	2032	10
forecast-8	GB	2023	2032	10
forecast-8	GOM	2023	2032	10
forecast-8	MAB	2023	2032	10
forecast-9	GB	2023	2032	10
forecast-9	GOM	2023	2032	10
forecast-9	MAB	2023	2032	10
forecast-Ensemble Mean	GB	2023	2032	10
forecast-Ensemble Mean	GOM	2023	2032	10
forecast-Ensemble Mean	MAB	2023	2032	10
Spring-hindcast	GB	1993	2019	27
Spring-hindcast	GOM	1993	2019	27
Spring-hindcast	MAB	1993	2019	27
Summer-hindcast	GB	1993	2019	27
Summer-hindcast	GOM	1993	2019	27
Summer-hindcast	MAB	1993	2019	27
Winter-hindcast	GB	1993	2019	27
Winter-hindcast	GOM	1993	2019	27
Winter-hindcast	MAB	1993	2019	27

2.1.2.3 Spatial location, scale and extent

General overview: The model data is on a roughly 1/12 degree grid. Here we show the data averaged by EPU.

Indicator specifics:

Indicator	EPU
Fall-hindcast	GB
Fall-hindcast	GOM
Fall-hindcast	MAB
forecast-1	GB
forecast-1	GOM
forecast-1	MAB
forecast-10	GB
forecast-10	GOM
forecast-10	MAB
forecast-2	GB
forecast-2	GOM
forecast-2	MAB
forecast-3	GB
forecast-3	GOM
forecast-3	MAB
forecast-4	GB
forecast-4	GOM
forecast-4	MAB
forecast-5	GB
forecast-5	GOM
forecast-5	MAB
forecast-6	GB
forecast-6	GOM
forecast-6	MAB
forecast-7	GB
forecast-7	GOM
forecast-7	MAB
forecast-8	GB
forecast-8	GOM
forecast-8	MAB
forecast-9	GB
forecast-9	GOM
forecast-9	MAB
forecast-Ensemble Mean	GB
forecast-Ensemble Mean	GOM
forecast-Ensemble Mean	MAB
Spring-hindcast	GB
Spring-hindcast	GOM
Spring-hindcast	MAB
Summer-hindcast	GB
Summer-hindcast	GOM
Summer-hindcast	MAB
Winter-hindcast	GB
Winter-hindcast	GOM
Winter-hindcast	MAB

2.1.2.4 Management scale: all species, FMP level, species level, can it be aggregated or separated to different scales?

[Classify by hand, note gridded data if available could be applied to different species ranges]

2.1.2.5 Uncertainty metrics

Uncertainty is captured in these variables:

character(0)

2.1.3 Are methods clearly documented to obtain source data and calculate indicators?

Yes

2.1.3.1 Can the indicator be calculated from current documentation?

2.1.3.2 Is code publicly available? up to date?

2.1.3.3 Have methods changed over time?

2.1.4 Are indicator underlying source data linked or easy to find?

2.1.4.1 Where are source data stored?

2.1.4.2 How/by whom are source data updated? Are future updates likely?

[likelihood of source data updates requires judgement, enter by hand]

2.1.4.3 How often are they updated?

[Update by hand, look for source, may require judgement]

2.2 Indicator analysis/testing or history of use

2.2.1 What decision or advice processes are the indicators currently used in?

This indicator shows seasonal sea surface temperature anomalies from a regional ocean model for the Northwest Atlantic based on the Modular Ocean Model Version 6 (NWA MOM6). The model is at roughly 1/12 a degree horizontal resolution with data from 1993-2019 from a hindcast simulation, which has no data assimilation. Investigating the ability of this model to reproduce key indicators can help us to develop confidence in the model as well as learn where improvements can be made. This indicator also shows 10-year forecasts of annual sea surface temperature anomalies from the decadal forecast of the MOM6 Northwest Atlantic regional ocean model. Understanding how temperatures might change in the future can help us anticipate potential shifts in marine communities.

2.2.2 What implications of the indicators are currently listed?

Sea surface temperature is important for many marine species that have specific thermal preferences and tolerances. When a region becomes too warm or cool, a species may respond in many ways including by shifting its range or changing its behavior. Understanding how surface temperature may vary in the future can help us anticipate changes to marine communities and living marine resources.

2.2.3 Do target, limit, or threshold values already exist for the indicator?

2.2.4 Have the indicators been tested to ensure they respond proportionally to a change in the underlying process?

2.2.5 Are the indicators sensitive to a small change in the process, or what is the threshold of change that is detectable?

Unknown

2.2.6 Is there a time lag between the process change and the indicator change? How long?