HIST temperature and precipitation
WRF-BCC HIST and PRISM annual daily mean 2-m temperatures were highly correlated (Pearson’s r correlation of 0.99; p value ≈ 0) and exhibited an overall RMSE bias of 0.9 °C (Fig. 2). Overlap with the ensemble dataset occurred over much of the western CONUS and regional pockets in the High Plains and Southeast. Although negative biases of less than 2 °C were noted throughout the Midwest, these biases were larger than any observed within the ensemble dataset. Overall, a muted diurnal 2-m temperature cycle was evident, with nighttime lows too warm, and daytime highs too cold. Interestingly, the mean annual daily low temperature biases in the Midwest were within the observational ensemble range, but the high temperature biases were outside of the ensemble spread—suggesting that WRF-BCC HIST’s handling of daily 2-m high temperatures was likely the main cause of bias in this region. WRF-BCC HIST struggled to recreate the diurnal temperature range over much of the western CONUS despite reliably recreating the daily mean temperature with relatively small cold biases. Such cold biases have been noted over this region in similar studies (Liu et al. 2017) and may be attributable to handling of surface albedo in WRF’s Noah-MP land surface model. Cold biases also dominate much of the CONUS during the Dec–Feb period, whereas warm biases in the High and Northern Plains were evident during Jun–Aug (Fig. 3). Mean daily 2-m temperature biases over much of the Midwest were within the range of ensemble members during the warm season (Mar–Sep); however, Northern and High Plains biases began to fall outside of the ensemble spread during the summer months. Overall, RMSE biases were minimized during fall (0.86 °C) and maximized during winter (1.4 °C); correlation between WRF-BCC HIST and PRISM was significant for all seasons (Pearson r correlation of > 0.97; p value ≈ 0).
While the spatial patterns are replicated well, WRF-BCC HIST performance representing temperature magnitudes was reduced, as many locations in the CONUS exhibited monthly and seasonal biases larger than what could be explained by observational uncertainty (Figs. S1–S3). WRF-BCC HIST also had difficulty recreating the diurnal cycle of 2-m temperature, particularly daily high temperature, and produced overall cooler near-surface temperatures relative to observations. However, the approach to matching modeled and observed temperature is not as straightforward as is the case with precipitation. To mimic the collection of daily values for maximum, minimum, and mean temperature, the dataset was resampled to days starting at 1500 UTC (instead of 0000 UTC). Generally, this time should be late enough in the morning (early enough in the day) not to “double count” lows (highs). Despite this approach, daily high temperatures were too low and daily low temperatures too high—the fingerprint of a failure to capture daily extrema. Although poor model performance cannot be ruled out, it may be that daily thermometer readings are much more sensitive to these extrema due to their virtually infinite temporal resolution. When comparing top-of-hour (presented in this work) and 15-min (not shown) calculations of mean, maximum, and minimum daily temperatures—as well as 1200 UTC resampling—not much is gained in the way of reduced temperature biases. These issues could have a variety of downstream effects on the performance of the model. For example, the cooler daytime highs may prevent locations from reaching convective temperature—effectively suppressing thunderstorm formation. Indeed, this may explain the negative rainfall bias noted in the portions of the southeast CONUS during the Jun–Aug period, when and where surface sensible heating is the main driver of convection initiation. Despite the lack of agreement with the ensemble spread in some regions during the boreal summer, the warm biases noted in Liu et al. (2017) are reduced over most of the eastern CONUS, including the Great Plains, by 1–2 °C. This reduction in warm bias likely disrupted the temperature / precipitation feedback loop noted in previous work (Liu et al. 2017) that resulted in dry biases over the Plains and Midwest during the boreal warm season.
WRF-BCC HIST and PRISM showed good agreement (Pearson’s r correlation of 0.91; p value ≈ 0; RMSE = 210 mm) for annual average precipitation, as nearly all gridpoints (except for a few in the Intermountain West) had a bias smaller than at least one ensemble member (Fig. 4). A broad dry bias was noted in the southeast, east, and northeast CONUS, whereas WRF-BCC HIST tended to produce too much precipitation in the Intermountain West (e.g., Sierra Nevada and Cascade mountain ranges) when compared to PRISM. Like temperature, spatial patterns (e.g., gradients, placement of maxima and minima) of WRF-BCC HIST precipitation were admirably simulated, but precipitation magnitudes also displayed regional and seasonal biases (Fig. 5). For example, WRF-BCC HIST simulations were found to have a wet bias in most of the Northern Plains and Intermountain West during DJF (Fig. 5c), and a general dry bias in the Southeast during much of the boreal warm season (Fig. 5g, h, o). This dry bias was most prevalent along the eastern half of the Gulf Coast, Florida, and along the Atlantic coast during Jun–Aug (Fig. 5k) and continued during SON (Fig. 5o). Given the placement and timing of these biases, WRF-BCC HIST could be underestimating the magnitude of (or not properly simulating) warm-season precipitation associated with coastal land/sea-breeze induced convection, sub-gridscale airmass thunderstorms, and/or tropical cyclones. Precipitation biases in the Midwest during JJA (correlating with the peak MCS frequency climatology; Fig. 5i) were significantly improved from the biases noted in a similar previous simulation (Liu et al. 2017). It is important to note, however, that many of these seasonal biases in WRF-BCC HIST are within the range of the observational spread (Fig. 5d, h, l, p).
Examining monthly precipitation data, RMSE values ranged between 20.3 mm in Apr and 39.3 mm in Sep (Figs. S4–S6). In the warm season, the absolute differences in precipitation in the Intermountain West are small, but the percent differences are large (WRF-BCC HIST is too dry) as this is a climatologically arid region and small differences account for large percentages of the seasonal precipitation. The authors hypothesize that WRF-BCC HIST is not properly simulating Intermountain West convection and subsequent precipitation associated with the onset and duration of the North American Monsoon (Adams and Comrie 1997); however, most of these months and locations are within the observed ensemble spread. Summarizing, three specific precipitation biases were noted for WRF-BCC HIST as compared to PRISM/ensemble data: (1) simulations produced too much precipitation in the northern Plains, High Plains, and Intermountain West during Dec–Feb; (2) a general dry bias is present in the simulations across the Southeast, Mid-Atlantic, and Northeast CONUS during the boreal warm season; and (3) simulations did not produce enough precipitation in the Intermountain West during the climatological peak of the North American Monsoon. Outside of these seasonal/regional biases, WRF-BCC HIST nearly mirrored the observed monthly, seasonal, and annual precipitation, which is notable considering that WRF-BCC HIST is forced with GCM output and not reanalysis (i.e., they should not be expected to be exactly the same weather/climate conditions). It is also worth mentioning that caution should be used in interpreting PRISM to be ground “Truth”, especially in areas with limited surface weather cooperative observations.
HIST temperature and precipitation extremes
2-m temperature and precipitation extreme values from WRF-BCC HIST and PRISM were analyzed to examine potential biases in the tails of these variable distributions. Eight climatologically unique cities were chosen for examination, including Nashville, Tennessee; Phoenix, Arizona; Amarillo, Texas; Seattle, Washington; Grand Junction, Colorado; Albany, New York; Minneapolis, Minnesota; and Tallahassee, Florida. WRF-BCC HIST climatological 2-m temperature range by calendar day (i.e., WRF-BCC record high and low 2-m temperature for a day) followed the same general annual cycle as PRISM extreme values for all cities (Fig. 6). Biases in 2-m temperature extremes were similar to biases noted in the seasonal spatial climatologies. For instance, WRF-BCC HIST simulated calendar day records for minimum 2-m temperature values during the cool season were too cold as compared to PRISM in Minneapolis (Fig. 6g). Except for Tallahassee, all cities examined had lower extreme high 2-m temperatures on average as compared to PRISM. Extreme low temperatures tended to follow a similar muted signal, except in the cool-season where WRF-BCC HIST values tended to be too cold. Pearson’s r and root mean square error were calculated to identify correlation and biases between minimum and maximum temperature extremes in HIST relative to PRISM for each of the selected cities (Figs. S7 and S8). The correlations were all significant (p < 0.05), and Pearson’s r values ranged from 0.85 to 0.98. Root mean square error ranged from 1.5 to 6.9 °C. In general, the extreme maximum temperatures had lower biases in the selected cities (average root mean square error of 3.1 °C) compared to extreme minimum temperatures (average root mean square error of 5.0 °C).
An extreme value analysis (EVA; Cooley 2009) was conducted using Fisher–Tippett–Gnedenko theorem on the time series of daily total precipitation for both WRF-BCC HIST and PRISM to compare the extreme value distributions. Extremes were detected using a block maxima technique over the period of one year, with the solution converging toward a right-skewed Gumbel distribution. Return intervals (R) (Makkonen 2006) were calculated from the extreme events using the formula:
$$R=\frac{1}{P}/\lambda $$
where p is the probability of exceedance, and λ is the rate of extreme events per block. A 1000-iteration bootstrap sample was applied to estimate the 95% confidence intervals for return period distributions of the extremes. Of the eight cities examined, only one (Seattle, WA) had a statistically significant (95% confidence) different distribution of extreme daily precipitation values using a Mann–Whitney U test (Fig. 7). Significant overlap between WRF-BCC HIST and PRISM in the 95% confidence interval for the other locations gives good confidence that WRF-BCC HIST can capture the extreme values of daily precipitation recorded in PRISM. Additional comparisons of extreme values across these data could be the subject of future work.
Projected temperature and precipitation based on RCP 8.5 scenario
WRF-BCC HIST (1990–2005) data were then compared to a projected future climate (FUTR; 2085–2100) based on the CMIP5 RCP 8.5 scenario. Robust and significant changes in mean temperatures across the CONUS through the twenty-first century were noted in this extreme scenario (Fig. 8). These projections suggest that, if this scenario is realized, annual and seasonal mean temperatures will exceed even the warmest outlier years and seasons observed in our current climate state. For many areas in the central CONUS, mean annual temperatures are projected to increase by 5–6 °C, and projections for boreal summer and fall suggest even larger changes (6–7 °C). More muted, but significant, seasonal changes take place across the southeast and western CONUS, with projected changes of 2–3 °C for boreal winter and spring, respectively. These values exceed the observed ensemble spread and the biases noted between PRISM and HIST and are in line with GCM projections for RCP 8.5 (Pachauri et al. 2014). In general, the patterns of larger and smaller changes in temperature follow those produced by CMIP5 GCMs (RCP 8.5)—namely, of larger deltas for interior and northern portions of the continent and smaller deltas for more southerly coastal locations. These results are mimic those produced by CMIP6 members using the SSP5-85 scenario (Almazroui et al. 2021).
The greatest potential future precipitation increases of 200–400 mm year−1 were noted in the vicinity of the Cascade Mountain range and across portions of Mid-South, whereas robust 200–500 mm yrar−1 decreases in future precipitation were noted across broad regions of the Southwest, Great Basin, and Southern Plains (Fig. 9). Seasonally, the largest statistically significant increases in future projected precipitation arise from boreal winter and spring (Fig. 10c, d, g, h) and the largest decreases arise from a reduction in future precipitation during Dec–Aug. The peak of the warm-season (Jun–Aug) is perhaps most notable for its widespread projection of drier conditions across the western and central CONUS (Fig. 10k, l). No widespread statistically significant changes in precipitation were noted across the CONUS during Sep–Nov (Fig. 10m–p). These projected annual/seasonal changes in precipitation highlight the regional nature of the potential changes in the hydroclimate due to anthropogenic forcing and underscore the need for regional-to-local scale assessment of climate variables. Overall, the spatiotemporal changes in precipitation are consistent with previous works discussing current/future expansions of the southern Great Plains arid climate regime and a coincident eastward progression of precipitation and deep-convection maxima (Gensini and Brooks 2018; Seager et al. 2018).
As an example of potential local changes, HIST and FUTR cumulative precipitation plots were created for the eight climatologically unique CONUS cities used in Sect. 4.2 (Fig. 11). Representative of the CONUS Mid-South, Nashville’s future precipitation projection shows a statistically significant (using 1000 random bootstrapped annual values with replacement at 95% confidence level) mean increase of 205 mm (18%) and a broadening of the annual variability (mean standard deviation increase from 134 to 246 mm), suggesting that a future climate may be wetter and have more variability, favoring larger precipitation amounts (Fig. 11a). Phoenix (Fig. 11b) and Amarillo (Fig. 11c), characteristic of a more arid CONUS climate, show a statistically significant decrease in future precipitation accumulation of 44 and 48%, respectively. In both locations, most of the decrease in precipitation was noted in the warm-season months of Mar–Aug. Statistically significant increases in average annual future precipitation were also noted for Seattle (Fig. 11d) and Albany (Fig. 11f), whereas Grand Junction, Minneapolis, and Tallahassee exhibited no statistically significant changes (Fig. 11e, g, h). These results highlight that, even under an aggressive future emissions scenario, some local geographies may not have significantly altered mean annual precipitation accumulations.