This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change.

Mean daily maximum temperature of each month, for the Australian continent between 1970-2016. Daily temperature regulates rates of plant growth and determines critical conditions such as frost on flowering and fruiting. Modelled by expressing each monthly value as a difference anomaly with respect to the gridded 1976-2005 mean daily maximum temperature for each month as provided by ANUClimate_v1-0_temperature-max_monthly-mean_0-01deg_1976-2005. The monthly anomalies were interpolated by trivariate thin plate smoothing spline functions of longitude, latitude and vertically exaggerated elevation using ANUSPLIN Version 4.5. Monthly data values were calculated from Bureau of Meteorology daily data at stations where there were at least 25 days of record, giving an average of 634 data points per month between 1970 and 2016. Automated quality assessment rejected on average 2 data values per month with extreme studentised residuals. The root mean square of all individual cross validation residuals provided by the spline analysis is 0.73 degrees Centigrade. A comprehensive assessment of the analysis and the factors contributing to the quality of the final interpolated grids is in preparation.

These daily gridded observations at 1/8 degree spatial resolution (about 12 km) are a baseline dataset to be compered to downscaled climate predictions. The grid used is the same as has been used by other 1/8th degree spatial resolution downscaling projects. The updated data were processed exactly as in the reference above with the single exception of the precipitation time-of-observation adjustment. For this updated dataset, if a meteorological station has a time of observation before noon, the precipitation is assigned to the prior day, otherwise no adjustment is made. Before using this dataset, please review the materials here: https://my.usgs.gov/confluence/display/GeoDataPortal/2014/04/16/Notice%3A+Evaluation+of+Maurer+gridded+observational+datasets+and+their+impacts+on+downscaled+products

These daily gridded observations at 1/8 degree spatial resolution (about 12 km) are a baseline dataset to be compered to downscaled climate predictions. The grid used is the same as has been used by other 1/8th degree spatial resolution downscaling projects. The updated data were processed exactly as in the reference above with the single exception of the precipitation time-of-observation adjustment. For this updated dataset, if a meteorological station has a time of observation before noon, the precipitation is assigned to the prior day, otherwise no adjustment is made. Before using this dataset, please review the materials here: https://my.usgs.gov/confluence/display/GeoDataPortal/2014/04/16/Notice%3A+Evaluation+of+Maurer+gridded+observational+datasets+and+their+impacts+on+downscaled+products

A grid surface (80m spatial resolution) delineating a Soil Dryness Index (SDI) across the state of Tasmania is produced daily. Soil Dryness is estimated based on the calculation prescribed in Mount (1972) with input data provided from high resolution daily maximum temperature and accumulated rainfall grids. Refer to the following link for details of the latest map updates: https://sdi.tas-hires-weather.cloud.edu.au/shiny/ For operational real-time application, the mapping was fully automated in the R programming language and hosted on a cloud-based computing platform - via the high performance computing cluster provided by the Tasmanian Partnership of Advanced Computing (TPAC) of the University of Tasmania.

Monthly mean daily maximum temperature for the Australian continent between 1976-2005. Daily maximum temperature regulates rates of plant growth. Modelled by fitting trivariate thin plate smoothing spline functions of longitude, latitude and vertically exaggerated elevation to observed and estimated monthly 1976-2005 daily maximum temperature means at 1541 Bureau of Meteorology stations. Missing monthly temperature values were estimated by regression with the long term station that provided estimates of the 1976-2005 monthly means with with the least standard error. This was applied to each station with at least 5 years of record between 1921 and 2012. Quality controls were applied to the regression and the surface fitting processes to remove poor quality data. Thus, of 1597 stations with at least 5 years of record, 38 stations had poor regressions with long term stations and a further 18 stations had extreme studentised residuals from initial spline analyses. These were commonly stations with an old or short period of record, or with an imprecise location. The spline analysis also incorporated the impact of distance from the coast as provided by eMAST_ANUClimate_fx_dist_v1m0. The root mean square error of individual cross validation residuals provided by the spline analysis at 225 stations with near complete records, of at least 28 years, is 0.47 degrees Celsius. The incorporation of distance from the coast reduced overall cross validation errors by around 10%. A comprehensive assessment of the analysis and the factors contributing to the quality of the final interpolated monthly mean daily maximum temperature grids is in preparation.