Perspectives on Historical Observing Practices and Homogeneity of the Snowfall Record Kenneth E. Kunkel NOAA Cooperative Institute for Climate and Satellites North Carolina State U. and National Climatic Data Center Kenneth Hubbard University of Nebraska-Lincoln Collaborators: David Robinson, David Easterling, Kelly Redmond, Michael Palecki, and Leslie Ensor

Project Objective Identify long-term trends in snowfall based on data from temporally homogeneous stations which were identified through an extensive QC effort

Project Support Partially funded by the NOAA Climate Progam Office, Climate Change Data and Detection program

Questions Are there long-term trends (1900-present) in snow variables (fall, depth, cover, water equivalent)? Are the climate records (NWS COOP) of sufficient quality to answer this question?

Stations with less than 10% missing snowfall data for 1930-present

Are these interesting features real? Or do they reflect inhomogeneities?

Issues to be investigated? Station histories – changes in observer, location, exposure, etc. Observing practices – how did these change over time and what is the impact on the temporal homogeneity Spatial coherence – can we identify/develop homogeneous time series using spatial coherence to identify problem stations

Spatial Coherence Analysis Identify neighboring stations with long records For each time series, calculate snowfall anomalies (annual snowfall minus long-term mean snowfall) Create time series of annual values (reference station anomaly minus neighboring station anomaly): “Anomalies of anomalies” If reference station’s behavior is similar to neighboring station’s, then values will be small and fluctuate around zero

Data Quality Assessment Identify a station set suitable for trends analyses - No systematic biases - Absence of station change inhomogeneities Expert assessment of quality by authors using a number of statistical and graphical tools Assessment of the quality of >1100 long-term stations with annual snowfall > 12.5 cm (5 inches)

Homogeneous station trends for 1930-present

Annual Snowfall above the 90th percentile threshold

Another Big Issue: Snowfall/Precipitation Ratio The infamous 10:1 ratio was used to estimate precipitation from snow, or snow from precipitation since the 1870s Cleveland Abbe warned against its use in 1888! It was o.k. for coop observers to use this well into the 2 nd half of the 20 th Century A later air temperature related table was used and published in NWS Handbook No. 7 in 1996

Instructions to Observers “ Snowfall is preferably measured as depth of water rather than by the thickness of layer it forms on the ground. When it can not be measured by melting, it is customary to take one-tenth the measured depth of the snowfall on a level open place as the water equivalent of the snowfall” (USDA Weather Bureau; INSTRUCTIONS FOR COOPERATIVE OBSERVERS; Circular B and C, Instrument Division; 1st edition. Revised, 1899)

Annual Mean Ratio of all U.S. snow events > 2 inches (long term stations)

Frequency of 10:1 ratio days

The West did not follow 10:1

Individual station changes vary

Impacts of 10:1 Trends Actual observed snowfall:precipitation ratios are on average greater than 10 Therefore the use of 10:1 introduces biases in the record If snowfall is measured and precipitation is estimated, the precipitation value is an overestimate If precipitation is measured and snowfall is estimated, snowfall is underestimated

Estimation of 10:1 Impacts Chose 48 snowy (>40 inches annual average) stations with records going back to 1900 Developed an empirical relationship between the ratio and temperature using all daily snowfall/precipitation data for which the ratio was not 10:1 For all daily observations for which the ratio was 10:1, we used the empirical relationship to estimate a new liquid equivalent value

Precip trends at 48 sites with > 40 inches annual average snowfall, corrected for 10:1 days

Conclusions Inconsistencies in the U.S. snow record complicate the interpretation of trends – are they real or a consequence of non-climatic influences An in-depth investigation of the characteristics of the observations is required to ascribe climatic influences to the snow record

Rn = LW↓ - LW + SW↓ - αSW↓ ↑Radiant Available Rn – G = LE + HEnergy use S t = S t-1 – E + P – RoConservation of Water Mass Rn = SW↓ -SW↑ + LW↓ - LW↑ + f CO2 Climate Forcing due to CO 2 The connection between conservation of Mass and Energy α changes by ~4 for snow vs. no snow

The following is a contribution from the High Plains Regional Climate Center Martha Shulski: Director Natalie Umphlett: Regional Climatologist Braedi Wickard: Climatologist Intern

Data High Plains Region 78 Coop Stations –(non-mountain) Period Used: Core Winter Months –December –January –February

Methodology Data Criteria –1 inch or more of snow cover –Missing Data Data Analysis –Examine Tmax, Tmin –Partition (all;w.snow;w.o. snow)

Winter Snow Climatology

Conclusions Quantitative measure of influence of snow cover on temperature StateDecemberJanuaryFebruary CO IA KS MN MO MT NE NM ND OK SD WY Average Temp (°F) Colder With Snow Cover