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1. Independent validation of downscaled climate estimates from a coastal Alaska watershed using local historical weather journals

   https://doi.org/10.7717/peerj.12055  

   Williamson, Emily R.; Sergeant, Christopher J. (January 2021, PeerJ)

   Downscaling coarse global and regional climate models allows researchers to access weather and climate data at finer temporal and spatial resolution, but there remains a need to compare these models with empirical data sources to assess model accuracy. Here, we validate a widely used software for generating North American downscaled climate data, ClimateNA, with a novel empirical data source, 20th century weather journals kept by Admiralty Island, Alaska homesteader, Allen Hasselborg. Using Hasselborg’s journals, we calculated monthly precipitation and monthly mean of the maximum daily air temperature across the years 1926 to 1954 and compared these to ClimateNA data generated from the Hasselborg homestead location and adjacent areas. To demonstrate the utility and potential implications of this validation for other disciplines such as hydrology, we used an established regression equation to generate time series of 95% low duration flow estimates for the month of August using mean annual precipitation from ClimateNA predictions and Hasselborg data. Across 279 months, we found strong correlation between modeled and observed measurements of monthly precipitation ( ρ  = 0.74) and monthly mean of the maximum daily air temperature ( ρ  = 0.98). Monthly precipitation residuals (calculated as ClimateNA data - Hasselborg data) generally demonstrated heteroscedasticity around zero, but a negative trend in residual values starting during the last decade of observations may have been due to a shift to the cold-phase Pacific Decadal Oscillation. Air temperature residuals demonstrated a consistent but small positive bias, with ClimateNA tending to overestimate air temperature relative to Hasselborg’s journals. The degree of correlation between weather patterns observed at the Hasselborg homestead site and ClimateNA data extracted from spatial grid cells across the region varied by wet and dry climate years. Monthly precipitation from both data sources tended to be more similar across a larger area during wet years (mean ρ across grid cells = 0.73) compared to dry years (mean ρ across grid cells = 0.65). The time series of annual 95% low duration flow estimates for the month of August generated using ClimateNA and Hasselborg data were moderately correlated ( ρ  = 0.55). Our analysis supports previous research in other regions which also found ClimateNA to be a robust source for past climate data estimates. 

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2. Long-term seasonally and annually aggregated climatic variables for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert, derived from single-day NASA Daymet images, 2000 to 2022

   https://doi.org/10.6073/pasta/c33742c3986658a2d8ed06e2cfb896a1  

   Haight, Jeffrey; De_Albuquerque, Fábio; Frazier, Amy (January 2025, Environmental Data Initiative)

   This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 

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3. Anthropogenic fingerprints in daily precipitation revealed by deep learning

   https://doi.org/10.1038/s41586-023-06474-x  

   Ham, Yoo-Geun; Kim, Jeong-Hwan; Min, Seung-Ki; Kim, Daehyun; Li, Tim; Timmermann, Axel; Stuecker, Malte F. (October 2023, Nature)

   According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN) with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged. 

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4. Daily mean air temperature data for the North American Great Lakes based on coastal weather stations; 1897-2023 (NCEI Accession 0291722)

   https://doi.org/10.25921/ws45-s314  

   Fujisaki-Manome, Ayumi; Cannon, David; King, Katelyn (April 2024, NOAA National Centers for Environmental Information)

   This dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario). 

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5. Current and Future Habitat Suitability for Species of Concern at the Jack and Laura Dangermond Preserve

   https://doi.org/10.32942/X2BH2N  

   Yan, Xue; Zhan, Yanni; Yang, Wenxin; Ross, Olivia; Liu, Yifei; Li, Zijun; Lopez, Isabelle Izzy; Perez, Isabella; Tao, Emma; Vogel, Jacqueline; et al (December 2025, EcoEvoArxiv)

   We modeled current habitat suitability for 45 special status species at the Jack and Laura Dangermond Preserve in Southern California using a suite of environmental and climate conditions and projected those models to forecast future habitat suitability at mid-century (2041-2070) under three emissions scenarios. For many species, habitat suitability at the Preserve may increase in the future under a moderate emissions scenario (SSP3-RCP7.0), and while suitability for each individual species is driven by a different combination of environmental conditions, some overall trends did emerge. All groups (plants, birds, mammals, invertebrates, and herps) were highly impacted by precipitation seasonality and slope. For birds, temperature extremes were also important, including the mean daily minimum temperature of the coldest month and the mean daily maximum temperature of the warmest month. While for herps, invertebrates, and mammals, isothermality and mean daily maximum near-surface air temperature of the warmest month were strong predictors of habitat suitability. Model fits were lower for Golden eagle (Aquila chrysaetos), Loggerhead shrike (Lanius ludovicianus), Yellow Warbler (Setophaga petechia), Pacific pond turtle (Actinemys marmorata), Two-striped garter snake (Thamnophis hammondii), Pallid bat (Antrozous pallidus), Townsend’s big-eared bat (Corynorhinus townsendii), Mountain lion (Puma concolor), and American badger (Taxidea taxus). There were insufficient observations to model the Point Conception Jerusalem cricket (Ammopelmatus muwu). For these species, we recommend adding observations, where possible, and contributing those observations to the Global Biodiversity Information Facility (GBIF) through platforms such as iNaturalist and eBird so they can be included in future replications. 

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