1. Brodzik et al. IGS ‘06 Deriving Long-Term Northern Hemisphere Snow Extent Trends from Satellite Passive Microwave and Visible Data R. L. Armstrong, M. J. Brodzik, M. H. Savoie, K. Knowles, CIRES, University of Colorado, Boulder, USA

2. Brodzik et al. IGS ‘06 “Visible”- derived snow data Microwave-derived snow data NOAA weekly snow charts, (1966-2005) + Robinson QC, regridded to… …Northern Hemisphere EASE-Grid, aggregated into monthly average snow-covered area. EASE-Grid daily passive microwave TBs (1978-2006)… …to derive daily SWE… …combined into weekly maximum SWE maps, and monthly average SCA.

3. Brodzik et al. IGS ‘06 Issues in deriving linear trends In general, any procedure for detecting and estimating trends will be more powerful and efficient if the variance of the data is decreased. Standard, parametric linear regressions assume: Normally distributed data with constant variance over time => remove seasonal signal by calculating standardized anomalies (subtract the monthly mean, divide by the standard deviation) No autoregression (memory) => test for significant autoregression, and include an autoregression term in the model (ref. Weatherhead et al., JGR, 1998) Estimating how long it will take to detect a significant trend of the observed magnitude is a helpful calculation in understanding the relationship between the magnitude of the trend and the variability in the data.

4. Brodzik et al. IGS ‘06 Robinson et al. have noted a significant change in the mean value during 1986-1988. This coincides with the break between SMMR and SSM/I in the microwave record. Northern Hemisphere Visible-Derived Snow-Covered Area Northern Hemisphere Visible-Derived Snow Extent Standardized Anomalies

5. Brodzik et al. IGS ‘06 Microwave Sensor (SMMR-SSM/I) Issues The sensor overlap between SMMR and SSM/I occurs in 1987: Overlap occurs in July and August (Northern Hemisphere summer, no snow to observe simultaneously). The overlap is about 40 days, effectively 20 days of data, since SMMR was cycled on/off on a daily basis. The SMMR swath width was about half that of SSM/I, further reducing the probabilit y of overlapping observations. => we used regressions on T B s at stable Earth targets to adjust SSM/I T B s to SMMR brightnesses, and calculated SWE using the same algorithm for the full time series (ongoing work with Njoku and Chan (JPL)) Daily microwave data don’t provide complete spatial coverage SMMR sampling issues: half the swath width, and data only every other day. => we used temporal piece-wise linear interpolation to fill in coverage gaps

6. Brodzik et al. IGS ‘06 Sensors are similar but exhibit important differences in spatial and temporal coverage that affect the sampling density within the long-term record. Daily passive microwave 37 GHz, horizontally polarized brightness temperatures, July 19, 1987, showing smaller coverage area of SMMR (left) vs. SSM/I (right). SMMRSSM/I

7. Brodzik et al. IGS ‘06 Before and after piece-wise interpolation of daily SSM/I SWE map, February 24, 2004 Before InterpolationAfter Interpolation

8. Brodzik et al. IGS ‘06 Before and after piece-wise interpolation of daily SMMR SWE map, February 24, 1980 Before InterpolationAfter Interpolation

9. Brodzik et al. IGS ‘06 Northern Hemisphere Satellite-Derived Snow Extent 1978 – 2005 Visible (NOAA) Passive Microwave (SMMR & SSM/I)

10. Brodzik et al. IGS ‘06 There is no evidence of a significant trend in either series considered from 1978-2005. There is evidence of a significant decreasing trend in the full series (1966-2005) of visible-derived snow cover. Northern Hemisphere Visible-Derived Snow Extent Standardized Anomalies

11. Brodzik et al. IGS ‘06 For Western 11 U. S. States, there is evidence to suggest significant decreasing trends in both series considered from 1978-2005, but no evidence of a significant trend in the full series (1966-2005) of visible- derived snow cover. Western US Visible-Derived Snow Extent Standardized Anomalies

12. Brodzik et al. IGS ‘06

15. Conclusions 1.Trend analysis on environmental records should always include: autoregression, to avoid conclusions with false confidence in significance, and number of years required to detect significant trends We are seeing changes in snow cover over the last several decades, although the message is mixed: no clear hemispheric trends, some regional trends, consistent trends in spring/summer months. Northern Hemisphere: o 1978-2005: no significant trends, > 17 more years required o 1966-2005: Visible trend, –1.7 %/decade Western US: o 1978-2005: Visible trend, -9.4 %/decade; Microwave trend, -8.3 %/decade o 1966-2005: no significant trend, hundreds of years required Both data sets indicate significant decreasing trends in May through August

16. Brodzik et al. IGS ‘06 References Helsel, D. R. and R.M. Hirsch. Chapter 12, Trend Analysis in Statistical Methods in Water Resources. U. S. Department of the Interior. 2002. http://water.usgs.gov/pubs/twri/twri4a3/. http://water.usgs.gov/pubs/twri/twri4a3/ Weatherhead, E. C., G. C. Reinsel, G. C. Tiao, X.-L. Meng, D. Choi, W.-K. Cheang, T. Keller, J. DeLuisi, D. J. Wuebbles, J. B. Kerr, A. J. Miller, S. J. Oltmans, and J. E. Frederick. 1998. Factors affecting the detection of trends: Statistical considerations and application of environmental data. Journal of Geophysical Research, 103(D14), 17,149-17,161. Wilks, D. S. Chapter 8, Time Series in Statistical Methods in the Atmospheric Sciences. San Diego, CA: Academic Press, 1995.

17. Brodzik et al. IGS ‘06 Monthly snow extent climatology for NOAA and passive microwave data (50% or more of the weeks in the particular month over the period of record classified as snow covered).

18. Brodzik et al. IGS ‘06 Blended snow product prototypes, SWE (mm) from AMSR-E with additional snow extent from MODIS in red (October 24-31, 2003, max). Lower image represents AMSR-E snow extent in grey, with percent area of additional pixels that MODIS classifies as snow in blues. (AMSR-E @ 25 km, MODIS CMG @ 5 km) Fall Season Example

19. Brodzik et al. IGS ‘06 Passive Microwave Remote Sensing of Snow Radiation emitted from the soil is scattered by the snow cover Scattering increases in proportion to amount (mass) of snow Brightness temperature decrease, negative spectral gradient

20. Brodzik et al. IGS ‘06 Outline 1.Optical and passive microwave remote sensing of snow 2.Issues in deriving trends 3.Microwave sensor issues 4.Hemispheric-scale results 5.Conclusions

21. Brodzik et al. IGS ‘06 Optical: GOES, AVHRR, MODIS Higher resolution ( ~ 0.5 km) Clouds and darkness obscure surface Limited to surface characteristics Microwave: SMMR, SSM/I, AMSR-E Lower resolution (~ 10-20 km) All weather & day/night Sub-surface characteristics (mass) Satellite Remote Sensing of Snow: Hemispheric Scale

22. Brodzik et al. IGS ‘06 Select fixed earth targets with a variety of physical characteristics that together make up a range of brightness temperatures representing the cold through warm end of the emission range. Potential targets were chosen for temporal and spatial stability. This was done by way of the statistical analysis within a moving 3x3 array of pixels to determine locations with minimal spatial and temporal variability. (Collaboration with E. Njoku and S. Chan (JPL). Salonga (African Tropical Forest) footprint (3x3) mean and standard deviation for annual cycle

23. Brodzik et al. IGS ‘06 Scatter plots of SMMR and SSM/I brightness temperatures (18/19 and 37 GHz) at Earth targets selected for spatial stability: Dome C (Antarctic Ice Sheet), Salonga (African tropical forest), Canada (plains), Summit (Greenland Ice Sheet). Plus signs in plots represent the typical range (+/- 1 standard deviation) of wintertime brightness temperatures in seasonally snow covered regions During the 40 day overlap period in 1987 we examined the closest (temporally) overpasses for SMMR and SSM/I at selected targets and derived regression equations that are then used to adjust the brightness temperatures.