Alaska

Values correspond to Index scores

Description of time series:

Positive PDO values typically mean cool surface water conditions in the interior of the North Pacific Ocean and warm surface waters along the North American Pacific Coast while negative PDO conditions typically mean warm surface water conditions in the interior to the North Pacific Ocean and cool surface waters along the North American Pacific Coast. During the last five years, the PDO indicator has trended downward, shifting from positive phase to negative phase in 2019.

Description of gauge:

The unitless two-way gauge depicts whether the average of the last 5 years of data for the climate indicator is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.

Description of Pacific Decadal Oscillation (PDO):

The Pacific Decadal Oscillation (PDO) is a long-term pattern of Pacific climate variability. The extreme phases of this climatic condition are classified as warm or cool, based on deviations from average ocean temperature in the northeast and central North Pacific Ocean. When the PDO has a positive value, sea surface temperatures are below average (cool) in the interior North Pacific and warm along the Pacific Coast. When the PDO has a negative value, the climate patterns are reversed, with above average sea surface temperatures in the interior and sea surface temperatures below average along the North American coast. The PDO waxes and wanes; warm and cold phases may persist for decades. Major changes in northeast Pacific marine ecosystems have been correlated with phase changes in the PDO. Warm phases have seen enhanced coastal ocean biological productivity in Alaska and inhibited productivity off the west coast of the United States, while cold PDO phases have seen the opposite, north-south pattern of marine ecosystem productivity. We present data from the Pacific Islands, Alaska, and California Current regions.

Data Background:

Climate indicator data was accessed from the NOAA NCEI (https://www.ncei.noaa.gov/pub/data/cmb/ersst/v5/index/ersst.v5.pdo.dat). The data plotted are unitless and based on Sea Surface Temperature anomalies averaged across a given region

Values correspond to Index scores

Description of time series:

Positive EP-NP values mean above-average surface temperatures over the eastern North Pacific, and below-average temperatures over the central North Pacific and eastern North America and the opposite for negative EP-NP values. During the last five years, the EP-NP indicator has shifted phase from mostly positive to mostly negative.

Description of gauge:

The unitless two-way gauge depicts whether the average of the last 5 years of data for the climate indicator is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.

Description of East Pacific/ North Pacific Teleconnection Pattern Index:

The East Pacific/ North Pacific Teleconnection Pattern Index is a measure of climate variability. Positive EP-NP values mean above-average surface temperatures over the eastern North Pacific, and below-average temperatures over the central North Pacific and eastern North America and the opposite for negative EP-NP values.

This climate condition impacts people and ecosystems across the globe and each of the indicators presented here. Interactions between the ocean and atmosphere alter weather around the world and can result in severe storms or mild weather, drought, or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences. The positive phase of the EP-NP pattern is associated with above-average surface temperatures over the eastern North Pacific, and below-average temperatures over the central North Pacific and eastern North America. The main precipitation anomalies associated with this pattern reflect above-average precipitation in the area north of Hawaii and below-average precipitation over southwestern Canada.

Data Background:

Climate indicator data was accessed from Columbia University (https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.Indices/.NHTI…). The data plotted are unitless anomalies and averaged across a given region. 

Values correspond to Index scores

Description of time series:

The Oceanic Niño Index (ONI) is NOAA’s primary index for monitoring the El Niño-Southern Oscillation climate pattern. It is based on Sea Surface Temperature values in a particular part of the central equatorial Pacific, which scientists refer to as the Niño 3.4 region. Positive values of this indicator, greater than +0.5, indicate warm El Niño conditions, while negative values, less than -0.5, indicate cold La Niña conditions. The ONI shows a shift from El Niño to La Niña conditions in 2020, followed by a brief period of ENSO-neutral conditions, and then another dip to La Niña conditions in 2021. This pattern is sometimes called a “double dip” La Niña. In early 2023 there was a shift to ENSO-neutral conditions. Additional information can be found in the Climate Prediction Center’s Official ENSO Briefing and the climate.gov ENSO blog.

Description of gauge:

The unitless two-way gauge depicts the most recent seasonal value for the ONI showing how far it is above or below the median value of the entire time series. High or low gauge values mean recent values are unusually high or low relative to the entire data record.

Description of El Niño-Southern Oscillation (ENSO):

El Niño and La Niña are opposite phases of the El Niño-Southern Oscillation (ENSO), a cyclical condition occurring across the Equatorial Pacific Ocean with worldwide effects on weather and climate. During an El Niño, surface waters in the central and eastern equatorial Pacific become warmer than average and the trade winds - blowing from east to west - greatly weaken. During a La Niña, surface waters in the central and eastern equatorial Pacific become much cooler, and the trade winds become much stronger. El Niños and La Niñas generally last about 6 months but can extend up to  2 years. The time between events is irregular, but generally varies between 2-7 years. To monitor ENSO conditions, NOAA operates a network of buoys, which measure temperature, currents, and winds in the equatorial Pacific. 

This climate pattern impacts people and ecosystems around the world. Interactions between the ocean and atmosphere alter weather globally and can result in severe storms or mild weather, drought or flooding. Beyond “just” influencing the weather and ocean conditions, these changes can produce secondary results that influence food supplies and prices, forest fires and flooding, and create additional economic and political consequences. For example, along the west coast of the U.S., warm El Niño events are known to inhibit the delivery of nutrients from subsurface waters, suppressing local fisheries. El Niño events are typically associated with fewer hurricanes in the Atlantic while La Niña events typically result in greater numbers of Atlantic hurricanes.

Data Background:

ENSO ONI data was accessed from NOAA’s Earth Systems Research Laboratory (https://psl.noaa.gov/data/timeseries/monthly/ONI/). The data are plotted in degrees Celsius and represent Sea Surface Temperature anomalies averaged across the so-called Niño 3.4 region in the east-central tropical Pacific between 120°-170°W.

Sea surface temperature is defined as the average temperature of the top few millimeters of the ocean. Sea surface temperature monitoring tells us how the ocean and atmosphere interact, as well as providing fundamental data on the global climate system

Data Interpretation:

Time series: The time series shows the integrated sea surface temperature for Alaska’s Eastern Bering Sea region. During the last five years there has been no notable trend and values have remained within the 10th and 90th percentiles of all observed data in the time series.

Gauge: The gauge value of 86 indicates that the mean sea surface temperature between 2016 and 2021 for the Eastern Bering Sea region was higher than 86% of the temperatures between 1985 and 2021.

Indicator and source information:

The SST product used for this analysis is the NOAA Coral Reef Watch CoralTemp v3.1 SST composited monthly (https://coralreefwatch.noaa.gov/product/5km/index_5km_sst.php) accessed from CoastWatch (https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v3_1_monthly.g…). 

Great Lakes SST data were accessed from (https://coastwatch.glerl.noaa.gov/glsea/glsea.html). 

The data are plotted in degrees Celsius. 

Data background and limitations:

The NOAA Coral Reef Watch (CRW) daily global 5km Sea Surface Temperature (SST) product, also known as CoralTemp, shows the nighttime ocean temperature measured at the surface. The CoralTemp SST data product was developed from two, related reanalysis (reprocessed) SST products and a near real-time SST product. Monthly composites were used for this analysis.

Sea surface temperature is defined as the average temperature of the top few millimeters of the ocean. Sea surface temperature monitoring tells us how the ocean and atmosphere interact, as well as providing fundamental data on the global climate system

Data Interpretation:

Time series: The time series shows the integrated sea surface temperature for Alaska’s Gulf of Alaska region. During the last five years there has been no notable trend while values have remained within the 10th and 90th percentiles of all observed data in the time series.

Gauge: The gauge value of 76 indicates that the mean sea surface temperature between 2016 and 2021 for the Gulf of Alaska region was higher than 76% of the temperatures between 1985 and 2021.

Indicator and source information:

The SST product used for this analysis is the NOAA Coral Reef Watch CoralTemp v3.1 SST composited monthly (https://coralreefwatch.noaa.gov/product/5km/index_5km_sst.php) accessed from CoastWatch (https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v3_1_monthly.g…). 

Great Lakes SST data were accessed from (https://coastwatch.glerl.noaa.gov/glsea/glsea.html). 

The data are plotted in degrees Celsius. 

Data background and limitations:

The NOAA Coral Reef Watch (CRW) daily global 5km Sea Surface Temperature (SST) product, also known as CoralTemp, shows the nighttime ocean temperature measured at the surface. The CoralTemp SST data product was developed from two, related reanalysis (reprocessed) SST products and a near real-time SST product. Monthly composites were used for this analysis.

Values correspond to millions of square kilometers of sea ice cover

Data Interpretation:

Time series: This time series shows the sea ice extent for the Alaska-Arctic region from 1979 to 2021. During the last five years, there has been no notable trend and values are within the 10th and 90th percentiles, albeit near the lower end of the time series.

Gauge: The gauge value of 12 indicates that mean sea ice extent between 2017 and 2021 for the Alaska-Arctic region was only higher than 12% of the sea ice extent measurements between 1979 and 2021.

Indicator and source information:

Sea ice is ice that forms, expands, and melts in the ocean. Sea ice has an important effect on Earth’s heat balance by reflecting solar energy back to space; it also plays a role in determining ocean salinity and currents, and it provides important habitat for many marine organisms. 

The time series shows the Sea Ice extent in September of each year to give a sense of the summertime (i.e., minimum annual) extent through the years of sea ice across the entire Northern Hemisphere, which includes the Arctic Ocean and the Hudson Bay. 

Data background and limitations:

Sea ice figures and data was accessed from NOAA NCEIr for the northern hemisphere, https://www.ncei.noaa.gov/access/monitoring/snow-and-ice-extent/  The data are plotted in units of millions of square km.

Sea level varies due to the force of gravity, the Earth’s rotation and irregular features on the ocean floor. Other forces affecting sea levels include temperature, wind, ocean currents, tides, and other similar processes.

Description of time series:

The time series shows the relative sea level, water height as compared to nearby land level, for the Southern Alaska region. During the last five years there has been no notable trend but values were below the 10th percentile of all observed data in the time series.

Description of gauge:

The gauge value of 10 indicates that the sea level between 2016 and 2021 for the Southern Alaska region was only higher than 10% of the sea level between 1980 and 2021.

Indicator and source information:

NOAA monitors sea levels using tide stations and satellite laser altimeters. Tide stations around the globe tell us what is happening at local levels, while satellite measurements provide us with the average height of the entire ocean. Taken together, data from these sources are fed into models that tell us how our ocean sea levels are changing over time. For this site, data from tide stations around the US were combined to create regionally averaged records of sea-level change since 1980. We present data for all regions.

This indicator includes tide gauges from Ketchikan, Sitka, Juneau, Skagway, Yakutat, Cordova, Valdez, Seward, Seldovia, Nikiski, Anchorage, Kodiak Island, Sand Point, Adak Island, Unalaska, and Port Moller, AK.

Data background and limitations:

Sea level data presented here are measurements of relative sea level, water height as compared to nearby land level, from NOAA tide gauges that have >20 years of hourly data served through NOAA’s Center for Operational Oceanographic Products and Services (CO-OPS) Tides and Currents website. These local measurements are regionally averaged by taking the median value of all the qualifying stations within a region. The measurements are in meters and are relative to the year 2000.

Sea level varies due to the force of gravity, the Earth’s rotation and irregular features on the ocean floor. Other forces affecting sea levels include temperature, wind, ocean currents, tides, and other similar processes.

Description of time series:

The time series shows the relative sea level for this region. During the last five years there has been a positive trend and while values have remained within the 10^th and 90^th percentiles, albeit near the higher range of time series values.

Description of gauge:

The gauge value of 94 indicates that the mean sea level between 2016 and 2021 for Northern Alaska’s Arctic Sea region was higher than 94% of the sea level between 1980 and 2021.

Indicator and source information:

NOAA monitors sea levels using tide stations and satellite laser altimeters. Tide stations around the globe tell us what is happening at local levels, while satellite measurements provide us with the average height of the entire ocean. Taken together, data from these sources are fed into models that tell us how our ocean sea levels are changing over time. For this site, data from tide stations around the US were combined to create regionally averaged records of sea-level change since 1980. We present data for all regions.

This indicator includes tide gauges from Nome and Prudhoe Bay, AK.

Data background and limitations:

Sea level data presented here are measurements of relative sea level, water height as compared to nearby land level, from NOAA tide gauges that have >20 years of hourly data served through NOAA’s Center for Operational Oceanographic Products and Services (CO-OPS) Tides and Currents website. These local measurements are regionally averaged by taking the median value of all the qualifying stations within a region. The measurements are in meters and are relative to the year 2000. 

Values indicate cumulative annual heatwave intensity and duration in a region in degree-days

Description of Time Series: This time series shows the average integrated degree day value for Alaska’s Eastern Bering Sea region. During the last five years there has been no trend, though values have remained above the 90th percentile of all observed data in the time series.

Description of Gauge: The gauge value of 90 indicates that between 2016 and 2021 the average integrated degree-day value was much higher than the median integrated degree-day value between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year integrated degree day value is very low compared to the median value.
• 10 - 25: The five-year integrated degree day value is much lower than the median value.
• 25 - 50: The five-year integrated degree day value is lower than the median value.
• 50: The five-year integrated degree day value average equals the median value.
• 50 - 75: The five-year integrated degree day value is higher than the median value.
• 75 - 90: The five-year integrated degree day value is much higher than the median value.
• 90 - 100: The five-year integrated degree day value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days.

Values indicate cumulative annual heatwave intensity and duration in a region in degree-days

Description of Time Series: This time series shows the average integrated degree day value for Alaska’s Gulf of Alaska region. During the last five years there has been no trend and values have remained between the 10th and 90th percentiles of all observed data in the time series.

Description of Gauge: The gauge value of 85 indicates that between 2016 and 2021 the average integrated degree day value was much higher than the median average integrated degree day value between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year integrated degree day value is very low compared to the median value.
• 10 - 25: The five-year integrated degree day value is much lower than the median value.
• 25 - 50: The five-year integrated degree day value is lower than the median value.
• 50: The five-year integrated degree day value average equals the median value.
• 50 - 75: The five-year integrated degree day value is higher than the median value.
• 75 - 90: The five-year integrated degree day value is much higher than the median value.
• 90 - 100: The five-year integrated degree day value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days.

Values indicate cumulative annual heatwave intensity and duration in a region in degree-days

Description of Time Series: This time series shows the average integrated degree day value for Alaska’s Chukchi Sea region. During the last five years there has been a significant downward trend and values have remained above the 90th percentile of all observed data in the time series.

Description of Gauge: The gauge value of 90 indicates that between 2016 and 2021 the average integrated degree day value was much higher than the median average integrated degree day value between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year integrated degree day value is very low compared to the median value.
• 10 - 25: The five-year integrated degree day value is much lower than the median value.
• 25 - 50: The five-year integrated degree day value is lower than the median value.
• 50: The five-year integrated degree day value average equals the median value.
• 50 - 75: The five-year integrated degree day value is higher than the median value.
• 75 - 90: The five-year integrated degree day value is much higher than the median value.
• 90 - 100: The five-year integrated degree day value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days.

Values indicate monthly percent of an LME area affected by heatwave

Description of Time Series: This time series shows the monthly heatwave spatial coverage for Alaska’s Eastern Bering Sea Region. During the last five years heatwave coverage has trended downward, and the five-year average is within the 10th and 90th percentiles of all observed data in the time series.

Description of Gauge: The gauge value of 90 indicates that between 2016 and 2021 the average area fraction value was very high compared to the median area fraction between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year area fraction value is very low compared to the median value.
• 10 - 25: The five-year area fraction value is much lower than the median value.
• 25 - 50: The five-year area fraction value is lower than the median value.
• 50: The five-year area fraction value average equals the median value.
• 50 - 75: The five-year area fraction value is higher than the median value.
• 75 - 90: The five-year area fraction value is much higher than the median value.
• 90 - 100: The five-year area fraction value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days

Values indicate monthly percent of an LME area affected by heatwave

Description of Time Series: This time series shows the monthly heatwave spatial coverage for Alaska’s Gulf of Alaska Region. During the last five years there has been no significant trend and the five-year average is within the 10th and 90th percentiles of all observed data in the time series.

Description of Gauge: The gauge value of 85 indicates that between 2016 and 2021 the average  area fraction value was much higher than the median area fraction between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year area fraction value is very low compared to the median value.
• 10 - 25: The five-year area fraction value is much lower than the median value.
• 25 - 50: The five-year area fraction value is lower than the median value.
• 50: The five-year area fraction value average equals the median value.
• 50 - 75: The five-year area fraction value is higher than the median value.
• 75 - 90: The five-year area fraction value is much higher than the median value.
• 90 - 100: The five-year area fraction value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days

Values indicate monthly percent of an LME area affected by heatwave

Description of Time Series: This time series shows the monthly heatwave spatial coverage for Alaska’s Chukchi Sea Region. During the last five years there has been a significant downward trend and the five-year average is within the 10th and 90th percentiles of all observed data in the time series.

Description of Gauge: The gauge value of 89 indicates that between 2016 and 2021 the average  area fraction value was much higher than the median area fraction between 1982 and 2021.

Gauge Values

• 0 - 10: The five-year area fraction value is very low compared to the median value.
• 10 - 25: The five-year area fraction value is much lower than the median value.
• 25 - 50: The five-year area fraction value is lower than the median value.
• 50: The five-year area fraction value average equals the median value.
• 50 - 75: The five-year area fraction value is higher than the median value.
• 75 - 90: The five-year area fraction value is much higher than the median value.
• 90 - 100: The five-year area fraction value is very high compared to the median value.

Indicator Source Information:

The marine heatwave data shown here are calculated by NOAA’s National Centers for Environmental Information using Optimum Interpolation Sea Surface Temperature (OISST) data. The NOAA 1/4° OISST is a long term Climate Data Record that incorporates observations from different platforms (satellites, ships, buoys and Argo floats) into a regular global grid. The dataset is interpolated to fill gaps on the grid and create a spatially complete map of sea surface temperature. Satellite and ship observations are referenced to buoys to compensate for platform differences and sensor biases.

Data Background and Caveats:

Heatwave metrics are calculated using OISST, a product that uses some forms of interpolation to fill data gaps. Heatwaves are defined by Hobday et al., 2016 as distinct events where SST anomaly reaches the 90th percentile in a pixel for at least 5 days, separated out by 3 or more days

Chlorophyll a, a pigment produced by phytoplankton, can be measured to determine the amount of phytoplankton present in water bodies. From a human perspective, high values of chlorophyll a can be good (abundance of nutritious diatoms as food for fish) or bad (Harmful Algal Blooms that may cause respiratory distress for people), based on the associated phytoplankton species.

Data Interpretation:

Time series: This time series shows the average concentration levels of chlorophyll a for Alaska’s East Bering Sea region. During the last five years there has been no significant trend while values have remained within the 10th and 90th percentiles of all observed data in the time series.

Gauge: The gauge value of 58 indicates that between 2016 and 2021 the average concentration levels of chlorophyll a in Alaska’s East Bering Sea region were slightly higher than the long term median of all chlorophyll a concentration levels between 1998 and 2021.

Gauge values

0–10: Chlorophyll a was significantly lower than the long term median state.

10–25: Chlorophyll a was considerably lower than the long term median state.

25–50: Chlorophyll a was slightly lower than the long term median state.

50: Chlorophyll a was at the long term median state.

50–75: Chlorophyll a was slightly higher than the long term median state.

75–90: Chlorophyll a was considerably higher than the long term median state.

90–100: Chlorophyll a was significantly higher than the long term median state.

Indicator and source information:

Chlorophyll a concentration values for this indicator were obtained using the ESA OC-CCI product, a merged product from the European Spatial Agency (ESA) that is a validated, error-characterized, Essential Climate Variable (ECV) and climate data record (CDR) from satellite observations specifically developed for climate studies. The dataset (v5.0) is created by band shifting and bias-correcting SeaWiFS, MODIS, VIIRS and OLCI data to match MERIS data, merging the datasets and computing per-pixel uncertainty estimates. Source: https://climate.esa.int/en/projects/ocean-colour/news-and-events/news/ocean-colour-version-50-data-release/

https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg

Annual means for each LME for each year were calculated from the average of the LME 12 monthly means in that year on a pixel by pixel basis. Then for each year, the median average was taken spatially to yield one value per year per LME.

Data background and limitations:

Satellite chlorophyll a data was extracted for each LME from the ESA OC-CCI v5.0 product. These 4 km mapped, monthly composited data were - averaged over each year to produce pixel by pixel annual composites, then the spatial median was calculated  for each LME, resulting in one value per year per LME.   This technique was used for each LME from North America and Hawaii. Phytoplankton concentrations are highly variable (spatially and temporally), largely driven by changing oceanographic conditions and seasonal variability.   

Chlorophyll a, a pigment produced by phytoplankton, can be measured to determine the amount of phytoplankton present in water bodies. From a human perspective, high values of chlorophyll a can be good (abundance of nutritious diatoms as food for fish) or bad (Harmful Algal Blooms that may cause respiratory distress for people), based on the associated phytoplankton species.

Data Interpretation:

Time series: This time series shows the average concentration levels of chlorophyll a for Alaska’s Gulf of Alaska region. During the last five years there has been no significant trend while values have remained within the 10th and 90th percentiles of all observed data in the time series.

Gauge: The gauge value of 50 indicates that between 2016 and 2021 the average concentration levels of chlorophyll a in the Gulf of Alaska region were at the long term median state of all chlorophyll a concentration levels between 1998 and 2021.

Gauge values

0–10: Chlorophyll a was significantly lower than the long term median state.

10–25: Chlorophyll a was considerably lower than the long term median state.

25–50: Chlorophyll a was slightly lower than the long term median state.

50: Chlorophyll a was at the long term median state.

50–75: Chlorophyll a was slightly higher than the long term median state.

75–90: Chlorophyll a was considerably higher than the long term median state.

90–100: Chlorophyll a was significantly higher than the long term median state.

Indicator and source information:

Chlorophyll a concentration values for this indicator were obtained using the ESA OC-CCI product, a merged product from the European Spatial Agency (ESA) that is a validated, error-characterized, Essential Climate Variable (ECV) and climate data record (CDR) from satellite observations specifically developed for climate studies. The dataset (v5.0) is created by band shifting and bias-correcting SeaWiFS, MODIS, VIIRS and OLCI data to match MERIS data, merging the datasets and computing per-pixel uncertainty estimates. Source: https://climate.esa.int/en/projects/ocean-colour/news-and-events/news/ocean-colour-version-50-data-release/

https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg

Annual means for each LME for each year were calculated from the average of the LME 12 monthly means in that year on a pixel by pixel basis. Then for each year, the median average was taken spatially to yield one value per year per LME.  The overall “National Annual Mean” mean was calculated as the average of all LME annual means. See the Data Background section for more details.  

Data background and limitations:

Satellite chlorophyll a data was extracted for each LME from the ESA OC-CCI v5.0 product. These 4 km mapped, monthly composited data were - averaged over each year to produce pixel by pixel annual composites, then the spatial median was calculated  for each LME, resulting in one value per year per LME.   This technique was used for each LME from North America and Hawaii.  The overall “National Annual Mean” was calculated as the average of all the LME annual means. Phytoplankton concentrations are highly variable (spatially and temporally), largely driven by changing oceanographic conditions and seasonal variability.   

Description of time series:

Between 2015 and 2019 the average concentration of zooplankton biomass showed no significant trend.

Description of gauge:

The gauge value of 75 indicates that between 2015 and 2019 the average concentration of zooplankton biomass in Alaskan waters was much higher than the median value of all zooplankton biomass concentration levels between 1995 and 2019.

Gauge values

High values of zooplankton can be good (lots of lipid rich colder water species) or bad (lots of lipid poor warmer water species), depending on the region.

0 - 10: The five-year zooplankton biomass average is very low compared to the median value.

10 - 25: The five-year zooplankton biomass average is much lower than the median value.

25 - 50: The five-year zooplankton biomass average is lower than the median value.

50: The five-year zooplankton biomass average equals the median value.

50 - 75: The five-year zooplankton biomass average is higher than the median value.

75 - 90: The five-year zooplankton biomass average is much higher than the median value.

90 - 100: The five-year zooplankton biomass average is very high compared to the median value.

Description of Zooplankton:

Zooplankton are a diverse group of animals found in oceans, bays, and estuaries. By eating phytoplankton, and each other, zooplankton play a significant role in the transfer of materials and energy up the oceanic food web (e.g., fish, birds, marine mammals, humans.) Like phytoplankton, environmental and oceanographic factors continuously influence the abundance, composition and spatial distribution of zooplankton. These include the abundance and type of phytoplankton present in the water, as well as the water’s temperature, salinity, oxygen, and pH. Zooplankton can rapidly react to changes in their environment. For this reason monitoring the status of zooplankton is essential for detecting changes in, and evaluating the status of ocean ecosystems. We present the annual average total biovolume of zooplankton in the Alaska, California Current, Gulf of Mexico, Hawai'i-Pacific Islands and Northeast regions.

Indicator information

Zooplankton data for each region were obtained from the NOAA Fisheries Coastal & Oceanic Plankton Ecology, Production, & Observations Database, an integrated data set of quality-controlled, globally distributed plankton biomass and abundance data with common biomass units and served in a common electronic format with supporting documentation and access software. Alaska specific data comes from the Ecosystems and Fisheries-Oceanography Coordinated Investigations (EcoFOCI): https://www.ecofoci.noaa.gov/

Data Background and Caveats:

Zooplankton data for each region were obtained from the NOAA Fisheries Coastal & Oceanic Plankton Ecology, Production, & Observations Database, an integrated data set of quality-controlled, globally distributed plankton biomass and abundance data with common biomass units and served in a common electronic format with supporting documentation and access software. Source: https://www.st.nmfs.noaa.gov/copepod/about/about-copepod.html

Description of time series:

Between 2015 and 2019 the average concentration of zooplankton biomass showed no significant trend.

Description of gauge:

The gauge value of 52 indicates that between 2015 and 2019 the average concentration of zooplankton biomass in Alaskan waters was higher than the median value of all zooplankton biomass concentration levels between 1981 and 2019

Gauge values

High values of zooplankton can be good (lots of lipid rich colder water species) or bad (lots of lipid poor warmer water species), depending on the region.

0 - 10: The five-year zooplankton biomass average is very low compared to the median value.

10 - 25: The five-year zooplankton biomass average is much lower than the median value.

25 - 50: The five-year zooplankton biomass average is lower than the median value.

 50: The five-year zooplankton biomass average equals the median value.

50 - 75: The five-year zooplankton biomass average is higher than the median value.

75 - 90: The five-year zooplankton biomass average is much higher than the median value.

90 - 100: The five-year zooplankton biomass average is very high compared to the median value.

Description of Zooplankton:

Zooplankton are a diverse group of animals found in oceans, bays, and estuaries. By eating phytoplankton, and each other, zooplankton play a significant role in the transfer of materials and energy up the oceanic food web (e.g., fish, birds, marine mammals, humans.) Like phytoplankton, environmental and oceanographic factors continuously influence the abundance, composition and spatial distribution of zooplankton. These include the abundance and type of phytoplankton present in the water, as well as the water’s temperature, salinity, oxygen, and pH. Zooplankton can rapidly react to changes in their environment. For this reason monitoring the status of zooplankton is essential for detecting changes in, and evaluating the status of ocean ecosystems. We present the annual average total biovolume of zooplankton in the Alaska, California Current, Gulf of Mexico, Hawai'i-Pacific Islands and Northeast regions.

Indicator information

Zooplankton data for each region were obtained from the NOAA Fisheries Coastal & Oceanic Plankton Ecology, Production, & Observations Database, an integrated data set of quality-controlled, globally distributed plankton biomass and abundance data with common biomass units and served in a common electronic format with supporting documentation and access software. Alaska specific data comes from the Ecosystems and Fisheries-Oceanography Coordinated Investigations (EcoFOCI): https://www.ecofoci.noaa.gov/

Data Background and Caveats:

Zooplankton data for each region were obtained from the NOAA Fisheries Coastal & Oceanic Plankton Ecology, Production, & Observations Database, an integrated data set of quality-controlled, globally distributed plankton biomass and abundance data with common biomass units and served in a common electronic format with supporting documentation and access software. Source: https://www.st.nmfs.noaa.gov/copepod/about/about-copepod.html

Values correspond to estimated total forage biomass in millions of tons

Description of time series:

The time series describes the aggregate forage fish biomass in the Eastern Bering Sea from 1987 to 2021. While there was no trend between 2016 and 2021 in the biomass of forage fish, values were below the 10th percentile historically. Due to COVID-19 no data was collected in 2020.

Gauge: Gauge value of 6 indicates that between 2016 and 2021 the biomass of forage fish in the Eastern Bering Sea was only greater than 6% of all forage fish biomass values between 1992 and 2021.

Gauge values

0 - 10: The five-year forage fish small pelagics average is very low compared to the median value.

10 - 25: The five-year forage fish small pelagics average is much lower than the median value.

25 - 50: The five-year forage fish small pelagics average is lower than the median value.

50: The five-year forage fish small pelagics average equals the median value.

50 - 75: The five-year forage fish small pelagics average is higher than the median value.

75 - 90: The five-year forage fish small pelagics average is much higher than the median value.

Indicator source information:

This indicator from the Gulf of Alaska Integrated Ecosystem Assessment Program’s East Bering Sea (EBS) team includes adult and juvenile pollock, herring, capelin, eulachon, sandlance, pelagic rockfish, salmon, squid, and other forage fish. 

Data background and caveats:

Units, time series, and species vary by region for this indicator, so no national score is provided. Best practices and caveats vary by region:

• Information quality for this indicator ranges from a sophisticated highly quantitative stock assessment for pollock (the biomass dominant in the guild) through relatively high variance Eastern Bering Sea shelf survey data for forage fish, to no time series data for salmon and squid.
• This index aggregates survey biomass estimates for key forage fish species in the eastern Bering Sea. The constituents are members of the “forage fish” group included as Ecosystem Components in the BSAI Fishery Management Plan: eulachon, Pacific capelin, sand lance species, rainbow smelt, Pacific sandfish, and a group of minor smelt species. This aggregate does not include important forage species such as age-0 Walleye pollock or Pacific herring. The biomass estimates are from the eastern Bering Sea shelf bottom trawl survey including the northwestern survey strata 82 and 90. Because this survey is not optimized for small pelagic fishes, the data should be viewed with caution.

Values indicate estimated seabird abundance using a relative breeding index

Description of the Time Series

Due to COVID-19 no data was collected in 2020 or 2021 so there is no proper status or trend update.

Description of the Gauge:

Due to COVID-19 no data was collected in 2020 and 2021 so no gauge value is available.

Overall Scores means the following:

• 0 - 10: The five-year seabirds average is very low compared to the median value.
• 10 - 25: The five-year seabirds average is much lower than the median value.
• 25 - 50: The five-year seabirds average is lower than the median value.
• 50: The five-year seabirds average equals the median value.
• 50 - 75: The five-year seabirds average is higher than the median value.
• 75 - 90: The five-year seabirds average is much higher than the median value.
• 90 - 100: The five-year seabirds average is very high compared to the median value.

Description of seabirds:

Seabirds are a vital part of marine ecosystems and valuable indicators of an ecosystem’s status.  Seabirds are attracted to fishing vessels and frequently get hooked or entangled in fishing gear, especially longline fisheries. This is a common threat to seabirds. Depending on the geographic region, fishermen in the United States often interact with albatross, cormorants, gannet, loons, pelicans, puffins, gulls, storm-petrels, shearwaters, terns, and many other species. We track seabirds because of their importance to marine food webs, but also as an indication of efficient fishing practices.  

Indicator and Source Information:

This data was compiled by the US Fish and Wildlife Service and the multivariate index is calculated by a NOAA author. The Alaska Maritime National Wildlife Refuge has monitored seabirds at colonies around Alaska in most years since the early- to mid-1970’s. Time series of annual breeding success and phenology (among other parameters) are available from over a dozen species at eight Refuge sites in the Gulf of Alaska, Aleutian Islands, and Bering and Chukchi Seas. Monitored colonies in the eastern Bering Sea include St. Paul and St. George Islands. Here, we focus on cliff-nesting, primarily fish-eating species: black-legged kittiwake (Rissa tridactyla), red-legged kittwake (R. brevirostris), common murre (Uria aalge), thick-billed murre (U. lomvia), and redfaced cormorants (Phalacrocorax urile). Reproductive success is defined as the proportion of nest sites with eggs (or just eggs for murres that do not build nests) that fledged a chick.

Data Background and Caveats:

Data only include the species listed above in the Eastern Bering Sea Sub Region of Alaska. Reproductive activity of central-place foraging seabirds can reflect ecosystem conditions at multiple spatial and temporal scales. For piscivorous species that feed at higher trophic levels, continued reduced reproductive success may indicate that the ecosystem has not yet shifted back from warm conditions and/or there is a lagged response of the prey. Despite environmental changes returning back to more neutral conditions, seabird foraging conditions do not appear to have recovered in the eastern Bering Sea. In contrast, the improvement in attendance and minimal reproductive activity among murres in the Gulf of Alaska during 2017 indicates some improvement in foraging conditions for those species. Data can be directly accessed here: https://apps-afsc.fisheries.noaa.gov/refm/reem/ecoweb/Index.php?ID=9

The x-axis represents years. The y-axis represents the number of fish stocks or fish populations that are deemed by NOAA as overfished. Overfished means the population of fish is too low. Therefore the population cannot support a large amount of fishing.

Description of time series:

The series shows the number of fish populations that have been listed as overfished since 2000. Between 2017 and 2022 the number of overfished stocks showed an upward trend.

Description of Overfished stocks:

An overfished stock is a population of fish that is too low. Therefore the population can not support a large amount of fishing. A fish population can be “overfished” as the result of many factors, including overfishing, as well as habitat degradation, pollution, climate change, and disease. Stocks are determined to be overfished by NOAA as mandated in the Magnuson-Stevens Act, based on the results of stock assessments.

Overall Scores mean the following:

High values for overfished stocks are bad, low numbers are good.

• 0 - 10: The five-year overfished stock status average is very low compared to the median value.
• 10 - 25: The five-year overfished stock status average is much lower than the median value.
• 25 - 50: The five-year overfished stock status average is lower than the median value.
• 50: The five-year overfished stock status average equals the median value.
• 50 - 75: The five-year overfished stock status average is higher than the median value.
• 75 - 90: The five-year overfished stock status average is much higher than the median value.
• 90 - 100: The five-year overfished stock status average is very high compared to the median value.

Data Source:

Data were obtained from the NOAA Fisheries Fishery Stock Status website. Stocks that met the criteria for overfished status were summed by year for each region.

Values Correspond to the Number of ESA Threatened or Endangered Species in a given region

Data Interpretation

Gauge and Trend Analyses were not appropriate for marine mammal data.

Data Background and Caveats

NOAA Fisheries goes through required regulatory steps to list, reclassify, or delist a species under the ESA. For more information, see a step-by-step description of the ESA listing process. The listing process requires time and resources; as a result, the timing and number of listed marine species is not necessarily indicative of the actual number of currently endangered or threatened species and the exact timing of when these species became eligible to be listed under the ESA. Many marine species were initially listed when the ESA was passed in 1973; others have taken more time to be listed, and some have been reclassified or delisted since then.

Values correspond to the number of MMPA Strategic or Depleted Marine Mammal Species listed each year in each region

Data Interpretation

Gauge and Trend Analyses were not appropriate for marine mammal data.

Data Background and Caveats

NOAA Fisheries prepares marine mammal stock assessment reports to track the status of marine mammal stocks. Some marine mammal stocks are thriving, while others are declining, and we often don’t know all the reasons behind a species or stock’s population trend. Because of this variability, it is difficult to indicate the state of an ecosystem or specific region using stock assessment data for marine mammal species that often range across multiple ecosystems and regions.

Values indicate annual cumulative change in centroid across all species in a region in degrees N

Description of Time Series: Between 2014 and 2019 the average species latitudinal shift showed an increasing trend, indicating a northward shift in distributions.

Description of Gauge: The gauge value of 87 indicates that between 2014 and 2019 the average species latitudinal shift was much higher compared to the median average latitudinal shift between 1981 and 2019.

Gauge Values

• 0 - 10: The five-year latitudinal shift is very low compared to the median value.
• 10 - 25: The five-year latitudinal shift is much lower than the median value.
• 25 - 50: The five-year latitudinal shift is lower than the median value.
• 50: The five-year latitudinal shift average equals the median value.
• 50 - 75: The five-year latitudinal shift is higher than the median value.
• 75 - 90: The five-year latitudinal shift is much higher than the median value.
• 90 - 100: The five-year latitudinal shift is very high compared to the median value.

Indicator Source Information:

This data provides important information for fisheries management including which species are caught where and at what depth. The scientists at DisMAP use this data to calculate each species’ centroid as the mean latitude and depth of catch in the survey, weighted by biomass. The centroid for each species is calculated for each year after standardizing the data to ensure that the measure is consistent over time despite changes in survey techniques and total area surveyed. 
 

Data Background and Caveats:

The regional and national marine species distributions shown here represent the average shift in the centroid of species caught in surveys conducted in each region. These species represent a wide range of habitats and species types. As species distributions respond to many environmental and biological factors, combining data from multiple diverse species allows for a more complete picture of the general trends in marine species distribution. In order to more easily track and display changes in these distributions, the first year is standardized to zero. Thus, the indicator represents relative change in distribution from the first survey year.

Values Indicate annual cumulative change in average species centroid depth in meters - for example, a value of -5 indicates the species centroid moving deeper by 5m.

Description of Time Series: Between 2014 and 2019 the average species water column depth shift showed no significant trend.

Description of Gauge: The gauge value of 92 indicates that between 2014 and 2019 the average species water column depth shift was very high compared to the median average water column depth shift between 1981 and 2019 with species moving towards the surface.

Gauge Values

• 0 - 10: The five-year water column depth shift is very high compared to the median value with species moving deeper.
• 10 - 25: The five-year water column depth shift is much higher than the median value with species moving deeper.
• 25 - 50: The five-year water column depth shift is higher than the median value with species moving deeper.
• 50: The five-year water column depth shift average equals the median value.
• 50 - 75: The five-year water column depth shift is higher than the median value with species moving towards the surface.
• 75 - 90: The five-year water column depth shift is much higher than the median value with species moving towards the surface.
• 90 - 100: The five-year water column depth shift is very high compared to the median value with species moving towards the surface.

Indicator Source Information:

This data provides important information for fisheries management including which species are caught where and at what depth. The scientists at Ocean Adapt use this data to calculate each species’ centroid as the mean latitude and depth of catch in the survey, weighted by biomass. The centroid for each species is calculated for each year after standardizing the data to ensure that the measure is consistent over time despite changes in survey techniques and total area surveyed. 

Data Background and Caveats:

The regional and national marine species distributions shown here represent the average centroid of all species caught in every year of the surveys. These species represent a wide range of habitats and species types. As species distributions respond to many environmental and biological factors, combining data from multiple diverse species allows for a more complete picture of the general trends in marine species distribution. In order to more easily track and display changes in these distributions, the first year is standardized to zero. Thus, the indicator represents relative change in distribution from the first survey year.

Values indicate annual cumulative change in centroid across all species in a region in degrees N. Dashed lines correspond to years with missing data.

Description of Time Series: Between 2014 and 2019 the average species latitudinal shift shows no significant trend.

Description of Gauge: The gauge value of 60 indicates that between 2014 and 2019 the average species latitudinal shift was higher than the median average latitudinal shift between 1984 and 2019.

Gauge Values

• 0 - 10: The five-year latitudinal shift is very low compared to the median value.
• 10 - 25: The five-year latitudinal shift is much lower than the median value.
• 25 - 50: The five-year latitudinal shift is lower than the median value.
• 50: The five-year latitudinal shift average equals the median value.
• 50 - 75: The five-year latitudinal shift is higher than the median value.
• 75 - 90: The five-year latitudinal shift is much higher than the median value.
• 90 - 100: The five-year latitudinal shift is very high compared to the median value.

Indicator Source Information:

This data provides important information for fisheries management including which species are caught where and at what depth. The scientists at DisMAP use this data to calculate each species’ centroid as the mean latitude and depth of catch in the survey, weighted by biomass. The centroid for each species is calculated for each year after standardizing the data to ensure that the measure is consistent over time despite changes in survey techniques and total area surveyed. 
 

Data Background and Caveats:

The regional and national marine species distributions shown here represent the average shift in the centroid of species caught in surveys conducted in each region. These species represent a wide range of habitats and species types. As species distributions respond to many environmental and biological factors, combining data from multiple diverse species allows for a more complete picture of the general trends in marine species distribution. In order to more easily track and display changes in these distributions, the first year is standardized to zero. Thus, the indicator represents relative change in distribution from the first survey year.

Values Indicate annual cumulative change in average species centroid depth in meters - for example, a value of -5 indicates the species centroid moving deeper by 5m. Dashed lines correspond to years with missing data.

Description of Time Series: Between 2014 and 2019 the average species water column depth shift shows no significant trend.

Description of Gauge: The gauge value of 60 indicates that between 2014 and 2019 the average species water column depth shift was higher than the median average water column depth shift between 1984 and 2019.

Gauge Values

• 0 - 10: The five-year water column depth shift is very high compared to the median value with species moving deeper.
• 10 - 25: The five-year water column depth shift is much higher than the median value with species moving deeper.
• 25 - 50: The five-year water column depth shift is higher than the median value with species moving deeper.
• 50: The five-year water column depth shift average equals the median value.
• 50 - 75: The five-year water column depth shift is higher than the median value with species moving towards the surface.
• 75 - 90: The five-year water column depth shift is much higher than the median value with species moving towards the surface.
• 90 - 100: The five-year water column depth shift is very high compared to the median value with species moving towards the surface.

Indicator Source Information:

This data provides important information for fisheries management including which species are caught where and at what depth. The scientists at Ocean Adapt use this data to calculate each species’ centroid as the mean latitude and depth of catch in the survey, weighted by biomass. The centroid for each species is calculated for each year after standardizing the data to ensure that the measure is consistent over time despite changes in survey techniques and total area surveyed. 

Data Background and Caveats:

The regional and national marine species distributions shown here represent the average centroid of all species caught in every year of the surveys. These species represent a wide range of habitats and species types. As species distributions respond to many environmental and biological factors, combining data from multiple diverse species allows for a more complete picture of the general trends in marine species distribution. In order to more easily track and display changes in these distributions, the first year is standardized to zero. Thus, the indicator represents relative change in distribution from the first survey year.

Values correspond to total employment in all industries in the coastal counties of a given region

Time Series

Alaska’s coastal employment has been relatively steady between 2014 - 2019, with a clear decreasing trend but no substantial difference from historical patterns.   

Gauge

The gauge value of 67 indicates that coastal employment between 2014 and 2019 for Alaska was higher than 67% of all years between 2005 and 2019.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal employment level over the last 5 years of data was below any annual employment level up until that point, while a value of 100 would indicate the average over that same period was above any annual employment level up until that point.

Data Source:

Coastal employment numbers were downloaded from the NOAA ENOW Explorer Tool, filtered to present only coastal county values using the Census Bureau’s list of coastal counties within each state. ENOW Explorer streamlines the task of obtaining and comparing economic data, both county and state, for the six sectors dependent on the ocean and Great Lakes: living resources, marine construction, marine transportation, offshore mineral resources, ship and boat building, and tourism and recreation. Data are derived from Economics: National Ocean Watch (ENOW), available on NOAA’s Digital Coast. Of note is that these data fail to include self-employed individuals. Coastal county employment numbers were then summed within each region for reporting purposes.

Values correspond to landings in millions of metric tons

Commercial Landings Time Series

Between 2016 and 2021, commercial landings from Alaska are substantially above historic levels, although there is no recent trend apparent.   

Commercial Landings Gauge

The gauge value of 89 indicates that the mean annual commercial landings between 2016 and 2021 for Alaska was higher than 89% of all years between 1950 and 2021.

Extreme Gauge values:

A value of zero on the gauge means that the average revenue or landings over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.

Indicator Source Information:

Landings are reported in pounds of round (live) weight for all species or groups except univalve and bivalve mollusks, such as clams, mussels, oysters and scallops, which are reported as pounds of meats (excludes shell weight). Landings data may sometimes differ from state-reported landings due to our reporting of mollusks in meat weights rather than gallons, shell weight, or bushels. Also, NMFS includes some species such as kelp and oysters that are sometimes reported by state agricultural agencies and may not be included with state fishery agency landings data.

Data Background and Caveats:

All landings summaries will return only non confidential landing statistics. Federal statutes prohibit public disclosure of landings (or other information) that would allow identification of the data contributors and possibly put them at a competitive disadvantage. Most summarized landings are non confidential, but whenever confidential landings occur they have been combined with other landings and usually reported as "Withheld for Confidentiality" Total landings by state include confidential data and will be accurate, but landings reported by individual species may, in some instances, be misleading due to data confidentiality.

Landings data do not indicate the physical location of harvest but the location at which the landings either first crossed the dock or were reported from.

Many fishery products are gutted or otherwise processed while at sea and are landed in a product type other than round (whole) weight. Our data partners have standard conversion factors for the majority of the commonly caught species that convert their landing weights from any product type to whole weight. It is the whole weight that is displayed in our web site landing statistics. Caution should be exercised when using these statistics. An example of a potential problem is when landings statistics are used to monitor fishery quotas. In some situations, specific conversion factors may have been designated in fishery management plans or Federal rule making that differ from those historically used by NOAA Fisheries in reporting landings statistics.

The dollar value of the landings are ex-vessel (as paid to the fisherman at time of first sale) and are reported as nominal (current at the time of reporting) values. Users can use the Consumer Price Index (CPI) or the Producer Price Index (PPI) to convert these nominal landing values into real (deflated) values.

Landings do not include aquaculture products except for clams, mussels and oysters.

Pacific landings summarized by state include an artificial “state” designation of “At-Sea Process, Pac.” This designation was assigned to landings consisting of primarily whiting caught in the EEZ off Washington and Oregon that were processed aboard large vessels while at sea. No Pacific state lists these fish on their trip tickets which are used to report state fishery landing, hence the at-sea processor designation was used to insure that they would be listed as a U.S. landing.

Landing summaries are compiled from data bases that overlap in time and geographic coverage, and come from both within and outside of NOAA Fisheries. 

Values correspond to real revenue is 2021 US Dollars

Commercial Revenue Time Series

Commercial revenue from Alaska between 2015 and 2020  were not different from historical patterns, and there is no trend in values.  Given that landings were at historically high levels for that same period, this suggests that the price per pound of fish is substantially lower than historical levels.

Commercial Revenue Gauge

The gauge value of 44 indicates that the mean annual commercial revenue between 2015 and 2020 for Alaska was higher than 44% of all years between 1950 and 2020.

Extreme Gauge values:

A value of zero on the gauge means that the average revenue or landings over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.

Indicator Source Information:

Landings are reported in pounds of round (live) weight for all species or groups except univalve and bivalve mollusks, such as clams, mussels, oysters and scallops, which are reported as pounds of meats (excludes shell weight). Landings data may sometimes differ from state-reported landings due to our reporting of mollusks in meat weights rather than gallons, shell weight, or bushels. Also, NMFS includes some species such as kelp and oysters that are sometimes reported by state agricultural agencies and may not be included with state fishery agency landings data.

Data Background and Caveats:

All landings summaries will return only non confidential landing statistics. Federal statutes prohibit public disclosure of landings (or other information) that would allow identification of the data contributors and possibly put them at a competitive disadvantage. Most summarized landings are non confidential, but whenever confidential landings occur they have been combined with other landings and usually reported as "Withheld for Confidentiality" Total landings by state include confidential data and will be accurate, but landings reported by individual species may, in some instances, be misleading due to data confidentiality.

Landings data do not indicate the physical location of harvest but the location at which the landings either first crossed the dock or were reported from.

Many fishery products are gutted or otherwise processed while at sea and are landed in a product type other than round (whole) weight. Our data partners have standard conversion factors for the majority of the commonly caught species that convert their landing weights from any product type to whole weight. It is the whole weight that is displayed in our web site landing statistics. Caution should be exercised when using these statistics. An example of a potential problem is when landings statistics are used to monitor fishery quotas. In some situations, specific conversion factors may have been designated in fishery management plans or Federal rule making that differ from those historically used by NOAA Fisheries in reporting landings statistics.

The dollar value of the landings are ex-vessel (as paid to the fisherman at time of first sale) and are reported as nominal (current at the time of reporting) values. Users can use the Consumer Price Index (CPI) or the Producer Price Index (PPI) to convert these nominal landing values into real (deflated) values.

Landings do not include aquaculture products except for clams, mussels and oysters.

Pacific landings summarized by state include an artificial “state” designation of “At-Sea Process, Pac.” This designation was assigned to landings consisting of primarily whiting caught in the EEZ off Washington and Oregon that were processed aboard large vessels while at sea. No Pacific state lists these fish on their trip tickets which are used to report state fishery landing, hence the at-sea processor designation was used to insure that they would be listed as a U.S. landing.

Landing summaries are compiled from data bases that overlap in time and geographic coverage, and come from both within and outside of NOAA Fisheries. 

Values correspond to cumulative number of angler trips

Description of time series:

Between 2014 and 2019, recreational fishing effort from Alaska is around historic levels. There is no significant trend apparent.  

Description of gauge:

The gauge value of 50 indicates that the recreational fishing effort between 2015 and 2019 for Alaska was the median value for the recreational fishing effort values between 1996 and 2019.

 Extreme Gauge values:

A value of zero on the gauge means that the average effort or harvest over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.

Indicator Source Information

Recreational harvest and effort data pulled from National Summary Query. Units of data are in Effort in Angler Trips and Harvest in numbers of fish. The data from these queries is used by state, regional and federal fisheries scientists and managers to maintain healthy and sustainable fish stocks.

Data Background and Caveats:

To properly interpret this information, it is important to consider the following key points:

• When comparing harvest estimates across an extended time series, note differences in sampling coverage through the years. Some estimates may not be comparable over long time series.
• Changes may occur between preliminary and final estimates and year to year, meaning that the data may change when updated. Please review the Limitations and other sections on the Using the Data page from the source for more information.

Values correspond to harvest in millions of fish

Description of time series:

Between 2015 and 2020, recreational harvest from Alaska are around historic levels, although 2020 presents the lowest level of effort for the entire series. There is a significant downward trend apparent.  

Description of gauge:

The gauge value of 32 indicates that the recreational fishing harvest between 2015 and 2020 for Alaska was higher than 32% of the recreational fishing harvest values between 1996 and 2020.

 Extreme Gauge values:

A value of zero on the gauge means that the average effort or harvest over the last 5 years of data was below any annual value up until that point, while a value of 100 would indicate the average value over that same period was above any annual value up until that point.

Indicator Source Information

Recreational harvest and effort data pulled from National Summary Query. Units of data are in Effort in Angler Trips and Harvest in numbers of fish.The data from these queries is used by state, regional and federal fisheries scientists and managers to maintain healthy and sustainable fish stocks.

Data Background and Caveats:

To properly interpret this information, it is important to consider the following key points:

• When comparing catch estimates across an extended time series, note differences in sampling coverage through the years. Some estimates may not be comparable over long time series.
• Changes may occur between preliminary and final estimates and year to year, meaning that the data may change when updated. Please review the Limitations and other sections on the Using the Data page from the source for more information.

The x-axis on this time series represents years and the y-axis represents the percent of communities that are moderate to highly engaged in commercial fishing across Alaska. Commercial fishing engagement is measured by the number permits, fish dealers, and vessel landings across Alaska. 

Description of time series:

This time series shows the percent of communities moderately or highly engaged in commercial fishing in Alaska from 2009 to 2019. Between 2014 and 2019 (highlighted in green) the percent of communities moderately or highly engaged in commercial fishing showed an increasing significant trend.

Description of gauge:

The gauge value of 82 indicates that the average annual commercial fishing engagement between 2014 and 2019 for Alaska was higher than 82% of all years in the time series.

Description of Alaska Commercial Fishing Engagement:

Commercial fishing engagement is measured by the presence of fishing activity in coastal communities. The commercial engagement index is measured through permits, fish dealers, and vessel landings. A high rank within these indicates more engagement in fisheries. For details on both data sources and indicator development, please see https://www.fisheries.noaa.gov/national/socioeconomics/social-indicator….

NOAA Monitors commercial fishing engagement to better understand the social and economic impacts of fishing policies and regulations on our nation’s vital fishing communities. This and other social indicators help assess a coastal community’s resilience. NOAA works with state and local partners to monitor these indicators. We present data from the Northeast, Southeast, Gulf of Mexico, California Current, Alaska, and Hawaiian Island regions.

Extreme Gauge values:

A value of zero on the gauge means that the average percentage of communities engaged in commercial or recreational fishing over the last 5 years of data was below any annual engagement level up until that point, while a value of 100 would indicate the average over that same period was above any engagement level up until that point.

Data Source:

Commercial fishing engagement data is from the National Marine Fisheries Service’s social indicator data portal:https://www.st.nmfs.noaa.gov/data-and-tools/social-indicators/ The percentage of all communities in each region classified as medium, medium high, or highly engaged is presented for both recreational and commercial fishing

The x-axis on this time series represents years and the y-axis represents the percent of communities that are moderately to highly engaged in recreational fishing across Alaska.

Time Series

This time series shows the percent of communities moderately to highly engaged in recreational fishing in Alaska from 2009 to 2019. Between 2015 and 2019 (highlighted in green) the percent of communities moderately or highly engaged in recreational fishing showed no trend.

Recreational Engagement Gauge

The gauge value of 36 indicates that the average annual recreational fishing engagement between 2014 and 2019 for Alaska was higher than 36% of all years between 2009 and 2019

Indicator Source Information

The Alaska recreational engagement index is measured using the number of charter and sportfishing guide businesses, and sportfishing and guide licenses.

Data Source:

Commercial fishing engagement data is from the National Marine Fisheries Service’s social indicator data portal:https://www.st.nmfs.noaa.gov/data-and-tools/social-indicators/ The percentage of all communities in each region classified as medium, medium high, or highly engaged is presented for both recreational and commercial fishing

Values correspond to the number of events in a given year

Time Series

Billion dollar disasters in Alaska only began to occur after 2000, but the last 5 years of data indicates the number of storms has begun to deviate from historical patterns of events, but there is no trend in the number of disasters.   

Gauge

The gauge value of 83 indicates that the number of billion dollar disasters between 2017 and 2021 for Alaska was higher than 83% of all years between 1980 and 2022.

Extreme Gauge values

A value of zero on the gauge means that the average number of disasters over the last 5 years of data was below any annual level up until that point, while a value of 100 would indicate the average over that same period was above any annual number of disasters up until that point. 

Indicator Source Information:

Billion dollar disaster event frequency data are taken from NOAA’s National Centers for Environmental Information. The number of disasters within each region were summed for every year of available data. Although the number is the count of unique disaster events within a region, the same disaster can impact multiple regions, meaning a sum across regions will overestimate the unique number of disasters.

Data Background and Caveats:

Events are included if they are estimated to cause more than one billion U.S. dollars in direct losses. The cost estimates of these events are adjusted for inflation using the Consumer Price Index (CPI) and are based on costs documented in several Federal and private-sector databases.

Values correspond to the total coastal population for a given region

Time Series

Alaska’s average coastal population between 2014 – 2019 was substantially above historic levels, although the recent trend is not different from historical trends.   

Gauge

The gauge value of 90 indicates that the coastal population between 2014 and 2019  for Alaska was higher than 90% of the coastal population values between 1970 and 2019.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal population over the last 5 years of data was below any annual population level up until that point, while a value of 100 would indicate the average over that same period was above any annual population level up until that point.

Indicator Source Information:

The American Community Survey (ACS) helps local officials, community leaders, and businesses understand the changes taking place in their communities. It is the premier source for detailed population and housing information about our nation. 

Data Background and Caveats:

The values represented here are coastal county population estimates for states bordering US Large Marine Ecosystems as calculated by the US Census Bureau from the American Community Survey.

Values correspond to percent change in the GDP of the Tourism Sector of Coastal Counties in US States that border a region

Description of Time Series: Between 2014 and 2018 the average change in coastal county tourism GDP showed a decreasing trend.

Description of Gauge: The gauge value of 54 indicates that between 2014 and 2018 the average change in coastal county tourism sector GDP was higher than the median change in coastal county tourism sector GDP between 2006 and 2018.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal tourism GDP over the last 5 years of data was below any annual coastal tourism GDP level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism GDP value up until that point.

Indicator Source Information

Coastal tourism Gross Domestic Product is the total measure (in billions of 2012 dollars) of goods and services provided from various industries involved in tourism services and products along the coast. Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.

Values correspond to percent change in the total Employment of the Tourism Sector of Coastal Counties in US States that border a region

Description of Time Series: Between 2014 and 2018 the average change in coastal county employment showed a decreasing trend.

Description of Gauge: Between 2014 and 2018 the average change in coastal county tourism sector employment was higher than the median change in coastal county tourism sector employment between 2006 and 2018.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal tourism employment over the last 5 years of data was below any annual coastal tourism employment level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism employment level up until that point.

Indicator Source Information:

Coastal tourism employment is the total measure of jobs in tourism industries along the coast.  Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.

Values correspond to percent change in the total real wage compensation of the Tourism Sector of Coastal Counties in US States that border a region

Description of Time Series: Between 2014 and 2018 the average change in coastal county real wage compensation showed a decreasing trend.

Description of Gauge: Between 2014 and 2018 the average change in coastal county tourism sector real wage compensation was much higher than the median change in coastal county tourism sector real wage compensation between 2006 and 2018.

Extreme Gauge values:

A value of zero on the gauge means that the average coastal tourism wage compensation over the last 5 years of data was below any annual coastal tourism wage compensation level up until that point, while a value of 100 would indicate the average over that same period was above any annual coastal tourism wage compensation level up until that point.

Indicator Source Information:

Coastal tourism wage is the measure of wages (nominal) paid to employees in tourism industries along the coast. Data for Coastal Counties come from the US Census Bureau. This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties. The dataset is built to support coastal and ocean planning and other activities pursuant to the Energy Policy Act, Coastal Zone Management Act, Magnuson-Stevens Fishery Conservation and Management Act, National Environmental Policy Act, Rivers and Harbors Act and the Submerged Lands Act.

Beach closures are the number of days when beach water quality is determined to be unsafe.

Data Interpretation:

Time series: This time series shows the average number of beach closure days for the Alaska region from 2000 to 2021. 

Gauge: A gauge was not appropriate for this data due to data limitations.

Gauge values

0–10: The five-year beach closure days average is very low compared to the median value.

10–25: The five-year beach closure days average is much lower than the median value.

25–50: The five-year beach closure days average is lower than the median value.

50: The five-year beach closure days average equals the median value.

50–75: The five-year beach closure days average is higher than the median value.

75–90: The five-year beach closure days average is much higher than the median value.

90–100: The five-year beach closure days average is very high compared to the median. 

* gauge value is the percentile rank of the last five years based on the time series.

Indicator and source information:

Unsafe water quality may have significant impacts on human health, local economies, and the ecosystem. Beach water quality is determined by the concentration of bacteria in the water (either Enterococcus sp. or Escherichia coli). 

The US Environmental Protection Agency (EPA) supports coastal states, counties and tribes in monitoring beach water quality, and notifying the public when beaches must be closed. The information presented is from states, counties, and tribes that submit data to the EPA Beach Program reporting database (BEACON). Data obtained from the EPA BEACON 2.0 website have been provided to EPA by the coastal and Great Lakes states, tribes and territories that receive grants under the BEACH Act. Data were refined to closure, by state or territory, by year.

Data background and limitations:

Data compiled by states or territories are combined in regions defined as US Large Marine Ecosystems (LME). Changes in the number of beach closure days may be driven by changes in the number of beaches monitored under the BEACH Act versus by state and local municipalities and not by changes in water and/or air quality. Not all US beach closures are captured in this database, because not all beaches in a state or territory are monitored through the EPA BEACH Act. Data that were not identified to a water body or identified as inland water were not included. Data for beaches monitored by state and local municipalities are not included. 

Data from 2020 and beyond may be inflated by the Covid-19 pandemic, as there was no consistent way for states to report pandemic-related closures.