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Important climate change related shifts in King County's physical environment have been observed in recent years, and are documented in this indicator. King County is tracking these changes in the local environment to help assess the severity of local climate-influenced impacts.

Increasing air and water temperatures, acidifying marine waters, increasing fall flooding, rising sea levels, decreasing snow pack, and decreasing summertime river flows are examples of changes that have been observed in King County; these trends are consistent with expected and projected local climate change impacts, and many other impacts are also occurring.

Air Temperature

About this measure: This indicator is the trend in annual average air temperature at five long term monitoring sites in King County (1948-2015). This indicator (along with lake temperature) tracks the impact of climate change (natural variability and human-induced global warming) on regional air temperature.

Status: Overall trends in annual average air temperature were upward at the five stations with sufficient long term data, and trends were statistically significant (p<0.05) at three of the five stations over the period 1948-2015 (Sea-Tac [p=0.004], Landsburg [p=0.009], Palmer [p=0.023]). The trend at the Kent station was nearly statistically significant (p=0.068), while the p-value for the trend at the Cedar Lake station was 0.393. The Cedar Lake station is the highest elevation site evaluated at 1,560 ft.

As can be seen in the graphs below, there is a significant amount of synchrony in regional air temperatures, so it is reasonable to assume that regional air temperatures have been increasing over the period 1948-2015. The rate of increase in air temperature over the period 1949-2015 ranged from 0.11°F per decade at Cedar Lake to 0.44°F per decade at Sea-Tac International Airport. These trends are generally greater than reported regional (Pacific Northwest) long term trends for the period 1901-2012, but consistent with accelerated warming observed since the 1970s and similar to global trends.Washington had the warmest year on record in 2015 and limited winter snowpack contributed to summer drought and a record-breaking wildfire season.

Graph showing mean annual air temperatures in King County

Note: Three of the five trend lines are not statistically significant (i.e., p > 0.05)

Graph showing mean annual air temperature at Sea-Tac International Airport

Note: The trend line is statistically significant (p = 0.004)

Influencing factors: Air temperature is influenced by regional climate, which in turn is influenced by global climate variability and change. Studies of long-term changes in air temperature have detected the influence of human-caused warming of the atmosphere superimposed on regional scale variability. Climate variability in this region is strongly influenced by variation in Pacific Ocean circulation. Two measures of this variability that differ in the time-scales of their influence are the El Nino Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). ENSO varies from warm to cool phases on the scale of years, while PDO varies on a decadal scale.

Some of the observed long-term warming is likely due to PDO variability, which shifted from a cool to a warm phase in 1976-1977, returning to a cool phase in 1998 until a strong warm phase began in 2014-2015. The recent shift to a warm phase of the PDO, which appears to be the main pacemaker of variability in the rate of increase of global mean surface air temperature, may mean further increases in air temperature are in store in the near future.

Existing DNRP response: King County will continue to track regional air temperatures at these stations as part of its ongoing environmental monitoring programs. These programs are designed to track how creeks, lakes, rivers, estuaries and Puget Sound respond over time to various activities and inputs from the watersheds and seasonal or year-to-year variability in weather. Improved understanding of the influence of climate variability and change on environmental quality will help separate changes caused by watershed activities from the influence of climate.

Priority new actions: King County has launched the 2015 update to the King County Strategic Climate Action Plan (SCAP). The 2015 SCAP will guide County work to achieve ambitious greenhouse gas emissions reduction targets, prepare for the impacts of a changing climate, and ensure that King County continues to lead on climate action.

Data source: The data source for this indicator comes from five stations in King County that are part of the National Weather Service Cooperative Observer Program (COOP). A cooperative station is a site where observations are taken or other services rendered by volunteers or contractors. The first network of cooperative stations was set up as a result of an act of Congress in 1890 that established the Weather Bureau. COOP data form the cores of the U.S. Historical Network and the U.S. Reference Climate Network and are critical to recognizing and evaluating the extent of human impact on climate.

Collection frequency: Analysis was based on temperature data that were collected daily (hourly or daily minimum, maximum and mean) then averaged by month and then year.

Methods for analysis: Annual average air temperature data for five NOAA Cooperative stations (Sea-Tac, Kent, Landsburg, Cedar Lake, and Palmer 3 ESE) were tested for trend using a non-parametric Mann-Kendall trend test with an adjustment for serial correlation. A statistical significant trend was identified if the p-value or significance level was less than 0.05. However, the p-value was not used as a "bright-line cutoff". All p-values were reported and the consistent overall upward trend was considered as evidence of a regional trend. In addition, a non-parametric trend slope was calculated (Sen slope) to estimate the average rate of change in air temperature at each station.

Data References:

Additional References:

Abatzoglou, J.T., et al. 2014. Seasonal climate variability and change in the Pacific Northwest of the United States. Journal of Climate 27:2125-2142.

Arhonditsis, G.B., M.T. Brett, C.L. DeGasperi, and D.E. Schindler 2004. Effects of climatic variability on the thermal properties of Lake Washington. Limnology & Oceanography 49:256-270.

Johnstone, J.A. and N.J. Mantua. 2014. Atmospheric controls on northeast Pacific temperature variability and change. Proceedings of the National Academy of Sciences 111:14360-14365.

Mantua, N.J. and S.R. Hare. The Pacific Decadal Oscillation. Journal of Oceanography 58:35-44.

O'Reilly, C. M., S. Sharma, D. K. Gray, S. E. Hampton, J. S. Read, R. J. Rowley, P. Schneider, J. D. Lenters, P. B. McIntyre, B. M. Kraemer, et al. 2015. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett., 42, 10,773–10,781, doi:10.1002/2015GL066235.

Schnieder, P. and S.J. Hook. 2010. Space observations of inland water bodies show rapid surface warming since 1985. Geophysical Research Letters 37, L22405, doi:10.1029/2010GL045059.

Trenberth, K.E. 2015. Has there been a hiatus? Science 349:691-692.

Cascade Snowpack

About this indicator: This indicator is the trend in annual spring snowpack (based on April 1st snow water equivalent (SWE) from 1959-2015) measured at four long term snowpack observation locations in the Cascade Mountains along the eastern border of King County.

Status: Annual spring snowpack varies from year to year depending on changes in weather, particularly to changes in the winter precipitation and air temperature.

No statistically significant trend in annual spring snowpack levels was observed at any of the four stations (1959-2015) (see graphs). This is primarily due to the large inter-annual variability in snowpack levels and the length of the records available to detect a statistically significant trend. Regardless of statistical significance, all trends were downward and the observed trends amounted to declines ranging from 4 to 40 percent. These observed declines are consistent with published research on snowpack trends in the Washington Cascades.

Of particular note was the record low snowpack observed across the Cascade Mountains in the spring of 2015 (for example see the historical records presented in the graphs and the records for the Fish Lake station in particular), which resulted in a declaration of statewide drought by the governor. The record low snowpack was due to unusually warm winter temperatures as winter precipitation in 2015 was near normal; on newspaper headline declared that the drought was a "snowpack" drought). The combination of low snowpack, continuation of above normal temperatures through spring, and early summer and lower than normal 2015 spring rainfall resulted in lower than normal flows and higher than normal temperatures in many King County rivers and streams.

Influencing factors: Spring snowpack is influenced by regional climate, which in turn is influenced by global climate variability and change. Studies of long-term changes in Cascade Mountain snowpack indicates that these long term trends are dominated by trends in temperature, with annual and decadal variation in precipitation providing "noise" to the trend signal. Regional warming has played a role in the observed downward trend, but it is not yet possible to quantify how much of the regional warming trend is due to human-caused global climate warming.

Climate variability in this region is strongly influenced by variation in Pacific Ocean circulation. Two measures of this variability that differ in the time-scales of their influence are the El Nino Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). ENSO varies from warm to cool phases on the scale of years, while PDO varies on a decadal scale.

Some of the observed long-term decline in Cascade snowpack is likely due to PDO variability. The PDO was generally in a negative (cool-wet) phase from 1947-1976 and switched to a positive (warm-dry) phase from 1977 through about 1998. A brief negative phase occurred from 1999-2013 followed again by a positive phase in 2014-2015. An El Niño pattern (positive phase of the Multivariate El Niño Southern Oscillation Index or MEI) also developed in 2014 that persisted and strengthened through 2015. El Niño is also associated with warmer and drier winter and spring weather in the region.

Graph showing mean annual air temperatures in King County
Graph showing mean annual air temperature at Sea-Tac International Airport
Graph showing mean annual air temperatures in King County
Graph showing mean annual air temperature at Sea-Tac International Airport

Observed April 1st snow water equivalent (SWE) at four snow courses in the Cascade Mountains along the eastern King County border. Trend lines (1959-2015) and their statistical significance (significant if p < 0.05).

Existing DNRP response: On November 2, 2015, the King County Council unanimously approved the 2015 update of the King County Strategic Climate Action Plan (SCAP). The 2015 SCAP is a five-year blueprint for County action to confront climate change, integrating climate change into all areas of County operations and its work in the community.

The 2015 SCAP will guide County work to achieve ambitious greenhouse gas emissions reduction targets, prepare for the impacts of a changing climate, and ensure that King County continues to lead on climate action.

Priority new actions: Priority actions in the updated SCAP include integrating climate change considerations into the Equity and Social Justice Strategic Plan, as well as adaptation planning and responses for transportation infrastructure, wastewater treatment and conveyance systems, stormwater conveyance and treatment systems, flood risk reduction and floodplain management, etc.

Map showing Spring Snowpack

Spring Snowpack
2015 Findings
Download the PDF version - 682 KB PDF.

Data source: The data source for this indicator comes from the Natural Resources and Conservation Service, National Water and Climate Center, U.S. Department of Agriculture.

Collection frequency: Field observations of snow depth are made at each site each year on or near April 1 and converted to an estimate of snow water equivalent based on measurements of the water content of snow collected from the site.

Methods for analysis: The snow water equivalent reported on or near April 1st of each year was compiled for each site. A non-parametric Mann-Kendall trend test was performed on the data available from each station over the period 1959-2015.

Data Reference: Washington snow course data

Mote, P., A. Hamlet, and E. Salathé. 2008. Has spring snowpack declined in the Washington Cascades? Hydrology and Earth System Sciences 12:193-206.

Pederson, G.T., S.T. Gray, C.A. Woodhouse, J.L. Betancourt, D.B. Fagre, J.S. Littell, E. Watson, B.H. Luckman, and L.J. Graumlich. 2011. The unusual nature of recent snowpack declines in the North American cordillera. Science 333:332-335.

Stoelinga, M.T., M.D. Albright, and C.F. Mass. 2010. A new look at snowpack trends in the Cascade Mountains. Journal of Climate 23:2473-2491.

Onset of Thermal Stratification in Large Lakes

About this measure: This indicator is the trend in annual date of the onset of thermal stratification of Lake Washington and Lake Sammamish (1993-2015). This indicator (along with lake temperature) tracks the impact of climate change (natural variability and human-induced global warming) on the two largest lakes in King County.

Status: The date of onset of thermal stratification in the spring of each year in Lake Washington and Lake Sammamish varies from year to year depending on changes in weather, particularly to changes in the regional air temperature.

No statistically significant trend in the date of onset of spring stratification was observed in either lake over the period 1993-2015. This is primarily due to the large inter-annual variability the date of stratification onset and the length of the records available to detect a statistically significant trend. Statistical analysis of temperature data for Lake Washington from 1963 to 2015 provided by the University of Washington collected as part of a long-term lake ecology study indicates an almost statistically significant (p = 0.097) long-term decrease in the date of onset of stratification of approximately -1.9 days per decade or about 10 days earlier over the period of record. There is a significant amount of synchrony in regional lake temperatures, so it is reasonable to assume that Lake Sammamish had a similar trend over the period 1963-2015. Over the same period (1963-2015), Lake Washington also experienced a statistically significant increase in the duration of stratification (4.1 days per decade or about 21 days over the period of record), an increase in the date of fall destratification (1.9 days per decade or 10 days later over the period of record) and an increase in maximum lake stability (105 J m-2 per decade). These changes have been linked to changes in the life cycles of phytoplankton and higher trophic level (phenological) changes in Lake Washington and in other warming lakes around the world.

Text for screen readers

Note: Trend line is not statistically significant (p = 0.097)

Influencing factors: The water temperature, and hence the thermal regime, of these two large lakes is influenced by regional climate, which in turn is influenced by global climate variability and change. Studies of long-term changes in the temperatures of large lakes throughout the world have detected the influence of human-caused warming of the atmosphere superimposed on regional scale variability. Climate variability in this region is strongly influenced by variation in Pacific Ocean circulation. Two measures of this variability that differ in the time-scales of their influence are the El Nino Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). ENSO varies from warm to cool phases on the scale of years, while PDO varies on a decadal scale.

Some of the observed long-term warming, and changes in the strength and timing of stratification and destratification, of Lake Washington and Lake Sammamish is likely due to PDO variability, which shifted from a cool to a warm phase in 1976-1977, returning to a cool phase in 1998 until a strong warm phase began in 2014-2015,The recent shift to a warm phase of the PDO, which appears to be the main pacemaker of variability in the rate of increase of global mean surface air temperature, may mean further changes in lake stratification and stability are in store in the near future. Without long term temperature monitoring of the kind performed by the University of Washington and King County, it will not be possible to separate the influence of natural variability from the effects of human-induced global warming on these lakes. Research has also shown that the effect of climate variability and change is not limited to lake stratification, but includes ecological changes that result from shifts in the timing of the onset and duration of lake thermal stratification — the processes that lead to warmer lake water generally also lead to earlier thermal stratification, longer period of stratification and greater thermal stability of these lakes.

Existing DNRP response: King County will continue to monitor these lakes as part of its ongoing Major Lakes Ambient Monitoring Program and Lake Buoy Monitoring Program. These programs are designed to track how lakes respond over time to various activities and inputs from the watersheds through influent streams, lake nutrient cycles, ecological interactions, and seasonal or year-to-year variability in weather. Improved understanding of the influence of climate variability and change on lake quality will help separate changes caused by watershed activities from the influence of climate.

Priority new actions: King County is collaborating with the Global Lake Ecological Observatory Network (GLEON) to support the development of a scalable, persistent network of lake ecological observations.

Data source: The data source for this indicator comes from the King County DNRP/WLR Division's Major Lakes Monitoring Program (1993-2015) and King County Lake Monitoring Buoys (2008-2015). Long-term Lake Washington temperature data (1963-2015) were also provided by Dr. Daniel Schindler, University of Washington, Seattle, WA.

Collection frequency: Analysis was based on temperature profiles that were collected monthly (December through February) and twice monthly (March through November) from a central station in Lake Sammamish and in Lake Washington as part of the Major Lakes Monitoring Program and daily temperature profiles collected as part of the automated buoy profiling systems on Lake Washington and Lake Sammamish.

Methods for analysis: Temperature profile data were volume-weighted to calculate thermal stability (Schmidt Stability Index) each sampling date. A threshold of 50 J m-2 (Winder and Schindler 2004a) was used to identify the date of onset of stratification and the date of destratification each year. A non-parametric Mann-Kendall trend test with an adjustment for serial correlation was performed to test for the statistical significance of the trend (p<0.05). In addition, a non-parametric trend slope was calculated (Sen slope).

Data References:

Additional References:

Arhonditsis, G.B., M.T. Brett, C.L. DeGasperi, and D.E. Schindler 2004. Effects of climatic variability on the thermal properties of Lake Washington. Limnology & Oceanography 49:256-270.

Hampton, S.E. 2005. Increased niche differentiation between two Conochilus species over 33 years of climate change and food web alteration. Limnology and Oceanography 50:421-426.

Hampton, S.E., P. Romare, and D.E. Seiler. 2006. Environmentally controlled Daphnia spring increase with implications for sockeye salmon fry in Lake Washington, USA. Journal of Plankton Research 28:399-406.

Hampton, S.E., M.D. Scheuerell, D.E. Schindler. 2006. Coalescence in the Lake Washington story: Interaction strengths in a planktonic food web. Limnology and Oceanography 51:2042-2051.

Jankowski, T., D.M. Livingstone, H. BŸhrer, R. Forster, and P. Niederhauser. 2006. Consequences of the 2003 European heat wave for lake temperature profiles, thermal stability, and hypolimnetic oxygen depletion: Implications for a warmer world. Limnology and Oceanography 51:815-819.

Johnstone, J.A. and N.J. Mantua. 2014. Atmospheric controls on northeast Pacific temperature variability and change. Proceedings of the National Academy of Sciences 111:14360-14365.

Mantua, N.J. and S.R. Hare. The Pacific Decadal Oscillation. Journal of Oceanography 58:35-44.

Meis, S., S.J. Thackeray, and I.D. Jones. 2009. Effects of recent climate change on phytoplankton phenology in a temperate lake. Freshwater Biology 1888-1898.

O'Reilly, C.M., S.R. Alin, P-D. Pilsnier, A.S. Cohen, and B.A. McKee. 2003. Climate change decreases aquatic ecosystem productivity of Lake Tanganyika, Africa. Nature 424:766-768.

O'Reilly, C. M., S. Sharma, D. K. Gray, S. E. Hampton, J. S. Read, R. J. Rowley, P. Schneider, J. D. Lenters, P. B. McIntyre, B. M. Kraemer, et al. 2015. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett., 42, 10,773–10,781, doi:10.1002/2015GL066235.

Robertson, D.M. and R.A. Ragotzkie. 1990. Changes in the thermal structure of moderate to large sized lakes in response to changes in air temperature. Aquatic Sciences 52(4):360-380.

Romare, P., D.E. Schindler, M.D. Scheuerell, J.M. Scheuerell, A.H. Litt, and J.H. Shepherd. 2005. Variation in spatial and temporal gradients in zooplankton spring development: the effect of climatic factors. Freshwater Biol. 50:1007-1021.

Schnieder, P. and S.J. Hook. 2010. Space observations of inland water bodies show rapid surface warming since 1985. Geophysical Research Letters 37, L22405, doi:10.1029/2010GL045059.

Trenberth, K.E. 2015. Has there been a hiatus? Science 349:691-692.

Verberg, P., R.E. Heckey, and H. Kling. 2003. Ecological consequences of a century of warming in Lake Tanganyika. Science 301:505-507.

Winder, M and D.E. Shindler. 2004a. Climate change uncouples trophic interactions in an aquatic ecosystem. Ecology 85:2100-2106.

Winder, M. and D.E. Schindler. 2004b. Climatic effects on the phenology of lake processes. Global Change Biology 10:1844-1856.

Sea Level Rise

About this indicator: Sea level measurements are recorded at tidal gauges and from satellites. Sea level rise varies by location with changes in land elevation and wind patterns (WA DOE. 2012).

Examples of indirect impacts of rising sea levels are coastal community flooding, altered hydrology, increases in coastal erosion and landslides, saltwater well intrusion, and alteration or loss of wetland, nearshore habitat and estuary acreage.

Influencing factors: Rising sea levels are primarily caused by additional water from melting glaciers and ice sheets and from thermal expansion of ocean waters due to warmer sea temperatures (WA DOE. 2012).

Status: Oceans rose approximately 8 inches from 1870-2008, an average of 0.06 inches per year. Recent years have shown an increase in the rate of change. Average global sea level rose approximately 0.12 inches per year during 1993-2008 (USEPA. 2010).

Graph showing mean annual air temperatures in King County

Average absolute sea level of world’s oceans (1870-2008). Shaded area shows likely range of values. (USEPA. 2010)

Graph showing mean annual air temperature at Sea-Tac International Airport

Mean sea level trend in Seattle, WA (1898-2006). A rising sevel trend of 2.06 mm/yr (0.68 feet per100 years) was observed at a station in the Seattle, WA area. (NOAA. 2012).

The mean sea level trend at a station in Seattle, WA is 2.06 mm/year with a 95% confidence interval of +/- 0.17 mm/year based on monthly mean sea level data from 1898 to 2006 which is equivalent to a change of 0.68 feet in 100 years (NOAA. 2012).

Projections: Projected medium change for the Puget Sound region by 2050 and 2100 are 6 and 13 inches, respectively (Mote et al. 2008). More recent studies project increases in the range of 2-4 feet (WA DOE. 2012).

Data source:

References: