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Abstract. In recent years, extreme droughts in the United States have increased in frequency and severity, underlining a need to improve our understanding of vegetation resilience and adaptation. Flash droughts are extreme events marked by the rapid dry down of soils due to lack of precipitation, high temperatures, and dry air. These events are also associated with reduced preparation, response, and management time windows before and during drought, exacerbating their detrimental impacts on people and food systems. Improvements in actionable information for flash drought management are informed by atmospheric and land surface processes, including responses and feedbacks from vegetation. Phenologic state, or growth stage, is an important metric for modeling how vegetation modulates land–atmosphere interactions. Reduced stomatal conductance during drought leads to cascading effects on carbon and water fluxes. We investigate how uncertainty in vegetation phenology and stomatal regulation propagates through vegetation responses during drought and non-drought periods by coupling a land surface hydrology model to a predictive phenology model. We assess the role of vegetation in the partitioning of carbon, water, and energy fluxes during flash drought and carry out a comparison against drought and non-drought periods. We selected study sites in Kansas, USA, that were impacted by the flash drought of 2012 and that have AmeriFlux eddy covariance towers which provide ground observations to compare against model estimates. Results show that the compounding effects of reduced precipitation and high vapor pressure deficit (VPD) on vegetation distinguish flash drought from other drought and non-drought periods. High VPD during flash drought shuts down modeled stomatal conductance, resulting in rates of evapotranspiration (ET), gross primary productivity (GPP), and water use efficiency (WUE) that fall below those of average drought conditions. Model estimates of GPP and ET during flash drought decrease to rates similar to what is observed during the winter, indicating that plant function during drought periods is similar to that of dormant months. These results have implications for improving predictions of drought impacts on vegetation. 

We use a land surface hydrology model with a predictive phenology model to analyze changes in how vegetation and the atmosphere interact during extreme drought events known as flash droughts. Included here are model results accompanying a manuscript to be submitted to a journal for peer review. The model outputs include soil moisture, root water uptake, evapotranspiration, gross primary productivity, stomatal conductance, infiltration, leaf area index, and the fraction of photosynthetically active radiation. We find that plants nearly halt water and carbon exchanges and limit their growth during flash drought. 

The influence of the Unified Noah and Noah-MP land surface models (LSMs) on the evolution of cumulus clouds reaching convective initiation (CI) is assessed using infrared brightness temperatures (BT) from GOES-16. Cloud properties from individual cloud objects are examined using output from high-resolution (500 m horizontal grid spacing) model simulations. Cloud objects are tracked over time and related to observed clouds reaching CI to examine differences in cloud extent, longevity, and growth rate. The results demonstrate that differences in assumed surface properties can lead to large discrepancies in the net surface radiative budget, particularly in the sensible and latent heating components where differences exceed 40 W m−2. These differences lead to changes in the local mesoscale circulation patterns that are more pronounced near the edges of forested and grassland boundaries where lower-level convergence is stronger. Higher sensible heating from the Noah-MP LSM produced growth of CI clouds earlier and with increased longevity, which was closer to the timing and growth observed from GOES-16. The increased cloud growth in the Noah-MP experiment results from stronger and deeper updrafts, which lofts more cloud water into the upper levels of the troposphere. The weaker updrafts from the Noah LSM experiment results in shallower convection after CI is detected due to slower growth rates. The differences in cloud properties and growth are directly related to the land surfaces they develop above and point to the importance of accurately representing land properties and radiative characteristics when simulating convection in numerical weather prediction models. 

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere. 

In this study, a polarimetric radar forward model operator was developed for the Weather Research and Forecasting (WRF) model that was based on a scattering algorithm using the T-matrix methodology. Three microphysics schemes—Thompson, Morrison 2-moment, and Milbrandt-Yau 2-moment—were supported in the operator. This radar forward operator used the microphysics, thermodynamic, and wind fields from WRF model forecasts to compute horizontal reflectivity, radial velocity, and polarimetric variables including differential reflectivity (ZDR) and specific differential phase (KDP) for S-band radar. A case study with severe convective storms was used to examine the accuracy of the radar operator. Output from the radar operator was compared to real radar observations from the Weather Surveillance Radar–1988 Doppler (WSR-88D) radar. The results showed that the radar forward operator generated realistic polarimetric signatures. The distribution of polarimetric variables agreed well with the hydrometer properties produced by different microphysics schemes. Similar to the observed polarimetric signatures, radar operator output showed ZDR and KDP columns from low-to-mid troposphere, reflecting the large amount of rain within strong updrafts. The Thompson scheme produced a better simulation for the hail storm with a ZDR hole to indicate the existence of graupel in the low troposphere. 

Flash droughts develop rapidly (∼1 month timescale) and produce significant ecological, agricultural, and socioeconomical impacts. Recent advances in our understanding of flash droughts have resulted in methods to identify and quantify flash drought events. However, few studies have been done to isolate the individual rapid intensification and drought components of flash drought, which could further determine their causes, evolution, and predictability. This study utilized the standardized evaporative stress ratio (SESR) to quantify individual components of flash drought from 1979 – 2019, using evapotranspiration (ET) and potential evapotranspiration (PET) data from the North American Regional Reanalysis (NARR) dataset. The temporal change in SESR was utilized to quantify the rapid intensification component of flash drought. The drought component was also determined using SESR and compared to the United States Drought Monitor. The results showed that SESR was able to represent the spatial coverage of drought well for regions east of the Rocky Mountains. Furthermore, the rapid intensification component agreed well with previous flash drought studies, with the overall climatology of rapid intensification events showing similar hotspots to the flash drought climatology east of the Rocky Mountains. The rapid intensification climatology suggested areas west of the Rocky Mountains experience rapid drying more often than east of the Rocky Mountains. 

Abstract Probabilistic forecasts of changes in soil moisture and an Evaporative Stress Index (ESI) on sub-seasonal time scales over the contiguous U.S. are developed. The forecasts use the current land surface conditions and numerical weather prediction forecasts from the Sub-seasonal to Seasonal (S2S) Prediction Project. Changes in soil moisture are quite predictable 8-14 days in advance with 50% or more of the variance explained over the majority of the contiguous U.S.; however, changes in ESI are significantly less predictable. A simple red noise model of predictability shows that the spatial variations in forecast skill are primarily a result of variations in the autocorrelation, or persistence, of the predicted variable, especially for the ESI. The difference in overall skill between soil moisture and ESI, on the other hand, is due to the greater soil moisture predictability by the numerical model forecasts. As the forecast lead time increases from 8-14 days to 15-28 days, however, the autocorrelation dominates the soil moisture and ESI differences as well. An analysis of modelled transpiration, and bare soil and canopy water evaporation contributions to total evaporation, suggests improvements to the ESI forecasts can be achieved by estimating the relative contributions of these components to the initial ESI state. The importance of probabilistic forecasts for reproducing the correct probability of anomaly intensification is also shown. 

Abstract Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.