Coral reefs are rapidly declining due to local environmental degradation and global climate change. In particular, corals are vulnerable to ocean heating. Anomalously hot sea surface temperatures (SSTs) create conditions for severe bleaching or direct thermal death. We use SST observations and CMIP6 model SST to project thermal conditions at reef locations at a resolution of 1 km, a 16-fold improvement over prior studies, under four climate emissions scenarios. We use a novel statistical downscaling method which is significantly more skillful than the standard method, especially at near-coastal pixels where many reefs are found. For each location we present projections of thermal departure (TD, the date after which a location with steadily increasing heat exceeds a given thermal metric) for severe bleaching recurs every 5 years (TD5Y) and every 10 years (TD10Y), accounting for a range of post-bleaching reef recovery/degradation. As of 2021, we find that over 91% and 79% of 1 km reefs have exceeded TD10Y and TD5Y, respectively, suggesting that widespread long-term coral degradation is no longer avoidable. We project 99% of reefs to exceed TD5Y by 2034, 2036, and 2040 under SSP5-8.5, SSP3-7.0, and SSP2-4.5 respectively. We project that 2%-5% of reef locations remain below TD5Y at 1.5 degrees Celsius of mean global heating, but 0% remain at 2.0 degrees Celsius. These results demonstrate the importance of further improving ecological projection capacity for climate-vulnerable marine and terrestrial species and ecosystems, including identifying refugia and guiding conservation efforts. Ultimately, saving coral reefs will require rapidly reducing and eliminating greenhouse gas emissions.

We present a near surface air temperature (NSAT) fused data product over the contiguous United States using Level 2 data from the Atmospheric Infrared Sounder (AIRS), on the Aqua satellite, and the Cross-track Infrared Microwave Sounding Suite (CrIMSS), on the Suomi National Polar-orbiting Partnership (SNPP) satellite. We create the fused product using Spatial Statistical Data Fusion (SSDF), a procedure for fusing multiple datasets by modeling spatial dependence in the data, along with ground station data from NOAA’s Integrated Surface Database (ISD) which is used to estimate bias and variance in the input satellite datasets. Our fused NSAT product is produced twice daily and on a 0.25-degree latitude-longitude grid. We provide detailed validation using withheld ISD data and comparison with ERA5-Land reanalysis. The fused gridded product has no missing data; has improved accuracy and precision relative to the input satellite datasets, and comparable accuracy and precision to ERA5-Land; and includes improved uncertainty estimates. Over the domain of our study, the fused product decreases daytime bias magnitude by 1.7 K and 0.5 K, nighttime bias magnitude by 1.5 K and 0.2 K, and overall RMSE by 35% and 15% relative to the AIRS and CrIMSS input datasets, respectively. Our method is computationally fast and generalizable, capable of data fusion from multiple datasets estimating the same quantity. Finally, because our product reduces bias, it produces long-term datasets across multi-instrument remote sensing records with improved bias stationarity, even as individual missions and their data records begin and end.