We demonstrate the potential of Climate Engine and Google Earth Engine to both the research community and decision makers by highlighting several recent case studies related to climate, drought, fire, ecology, and agriculture. The following figures and examples are from our manuscript in review for publication in the Bulletin of the American Meteorological Society (BAMS). Manuscript data and figures can be re-created in real time through Climate Engine by clicking on figure buttons. 

 

Figure 1

User interface of Climate Engine illustrating the (top) mapping, and (bottom) time series menus. The spatial distribution of average latent heat flux (LE) from Climate Forecast System Reanalysis (CFSR) for July 23-September 20, 2016 is displayed using user defined colormap options (top). A time series of spatially averaged daily LE is displayed in (b) for July 23-September 20, 2016 for a user-drawn polygon over the western US (shown in blue in top figure). LE is declining due to decreases in soil moisture and solar radiation, which is typical for this region and period.



Figure 2

Land surface temperature (LST) anomalies from MODIS for (a) January - February, 2016, (b) January - March, 2015, and (c) and April 2016 relative to 2000-2015 averages extracted from Climate Engine’s mapping tools. Figures illustrate patterns of anomalously warm and cool temperatures over the Arctic and mid-latitude continents, respectively, potentially caused by a combination sea-ice loss and internal atmospheric variability. Panel (d) shows the Normalized Difference Snow Index (NDSI) anomaly from MODIS for April 2016 for southern Greenland, where unusually warm temperatures resulted in an unusually early melting in south-western Greenland.

 

 
 

Figure 3

Effects of the 2012 Great Plains drought on near surface boundary layer feedbacks between ET and ET0 are shown through maps of (a) June - August EDDI, (c)  MODIS NDVI, and (d) LST anomalies relative to 2000-2015 averages extracted from Climate Engine’s mapping tool. Panel (b) shows a time series of accumulated JJA Penman-Monteith reference ET0 averaged over the state of Missouri from 1979-2015 using Climate Engine’s time series tool.


 
 

Figure 4

The 2015 snow drought over the Northwestern U.S. is shown by Climate Engine generated maps of October 2014 - March 2015 (a) SPI, (b) EDDI, and (c)  MODIS Normalized NDSI anomalies relative to 2000-2015 averages. SPI shows little to no drought over the Cascades and Northern Rockies. However, EDDI shows extreme drought conditions primarily caused by anomalously high temperature and solar radiation. This led to extremely high freezing levels, resulting in anomalously low snow cover (i.e. snow drought) at mid to high elevations as illustrated by the NDSI anomaly.

 

Figure 5

Fire danger, using the Energy Release Component (ERC), over the mountains of southwestern Idaho is shown by (a) a maps of mean ERC values expressed as a percent departure from average for the May 1-September 5, 2016 relative to 1981-2010 normals, (b) a time series of daily ERC values averaged over Boise County, Idaho (outlined in black in panel (a)) for May 1-September 5, 2016  shown by the red trace, with 1979-2015 daily median values and daily 5-95th percentile shown by the black trace and grey shading, respectively. The Pioneer Fire started on 18 July, 2016 and coincides with an extended period of well above normal ERC values. (c) Landsat NDVI anomaly for July 18 - September 22, 2016 relative to the Landsat 5/7/8 climatology (1984-2015) during this period. (c) Illustrates the extent of the burn area - over 76 thousand hectares making it one of the largest fires of the 2016 western U.S. fire season.


 
 

Figure 6

Effects of groundwater irrigation on spring area vegetation vigor in eastern Nevada and western Utah are illustrated using the time series tool of Climate Engine to track August - September average NDVI from 1984-2016 spatially averaged over two user-drawn domains highlighted in (a). Irrigation commenced in 2002 resulting in (b) increased NDVI and a coincident (c) decline in NDVI within the spring area due to lowering of the groundwater table and drying of the spring. Lowering of the water table changed the vegetation response to precipitation within the spring area as evidenced by (d) pre- and post-irrigation NDVI and water year precipitation (PPT) relationships. The complementary relationship between supply (PPT), demand (ET0), and water use (NDVI a common proxy for ET) is also evident in (d), a product surface boundary layer feedbacks.

 


Figure 7

Extensive fallowed cropland in 2015 within the Tulare Lake Basin, Central Valley of California, due to multiyear drought is illustrated with the spatial distribution of Landsat growing season (April - October) maximum NDVI for (a) 2011 and (b) 2015. (c) The time series tool in Climate Engine was used to extract NDVI from 2011-2015 for the red polygon illustrated in panels (a-b). Field level crop phenology stages (identified as a cotton crop for all years according to USDA cropland data layers) are clearly evident, along with fallowing that occurred in 2015.

 

Figure 8

(a) The September 2015-February 2016 CHIRPS precipitation anomaly over Africa relative to 1981-2010 conditions depict large areas of Ethiopia received less than half of normal precipitation. Consequently, widespread impacts to agricultural productivity, especially within pastoral regions, were present across Ethiopia evidenced by reduced greenness in remote sensing images. (b) MODIS NDVI anomalies for September 2015-February 2016 relative to 2000-2015 normals are shown for the inset box of panel (a). (c) Landsat NDVI anomalies for September 2015-February 2016 relative to 2001-2016 normals are shown for the inset box of panel (b). Computing and visualizing NDVI anomalies at regional and field scales, especially for pastoral regions (i.e. c-d), are ideal examples of the utility of Climate Engine to support crop failure and famine early warning efforts.