206 Search Results

Several mechanisms have been proposed to explain why the isotope ratios of precipitation vary in space and time and why they correlate with other climate variables like temperature and precipitation. Here we argue that this behavior is best understood through the lens of radiative transfer, which treats the depletion of atmospheric vapor transport by precipitation as analogous to the attenuation of light by absorption or scattering. Building on earlier work by Siler et al. (2021), we introduce a simple model that uses the equations of radiative transfer to approximate the two-dimensional pattern of the oxygen isotope composition of precipitation (δ_p)more » from monthly-mean hydrologic variables. The model accurately simulates the spatial and seasonal variability in δ_p within a state-of-the-art climate model, and permits a simple decomposition of δ_p variability into contributions from gradients in evaporation and the length scale of vapor transport. Outside the tropics, δ_p is mostly controlled by gradients in evaporation, whose dependence on temperature explains the positive correlation between δ_p and temperature (i.e., the temperature effect). At low latitudes, δ_p is mostly controlled by gradients in the transport length scale, whose inverse relationship with precipitation explains the negative correlation between δ_p and precipitation (i.e., the amount effect). This suggests that the temperature and amount effects are both mostly explained by variability in upstream rainout, but they reflect distinct mechanisms governing rainout at different latitudes.« less

Past studies implicate dynamic anomalies operating on subsynoptic scales as a possible initiation source of summertime (July–August) mesoscale convective systems (MCSs) in the central United States during northwesterly flow regimes. To improve our understanding of warm season MCSs occurring over a variety of flow regimes, we track midlevel (600 hPa) vorticity perturbations (“MPs”) as 2D objects comprising wavelengths of 500–2,500 km over the central US from May–August of 2004–2021. We perform statistical analysis of relationships between metrics of MP objects (e.g., duration, size, intensity, and origin) and high-resolution MCS precipitation characteristics (e.g., duration, total rainfall, rain coverage area, and motion)more » that occur while collocated with or in the absence of MPs to discern predictive capability of background dynamic features on storm precipitation potential. Although the majority of MPs collocated with MCS initiation occur during July–August, a significant number (40%) occur between May and June. Northwesterly flow MPs comprise a relative minority of our events, suggesting that MPs can affect MCSs across a variety of warm season flow regimes. MPs affecting MCSs initiated primarily over the high plains near the central Rockies. Only approximately 20% of tracked MCS initiation events were collocated with MPs, but these storms produced ~25% greater lifetime rainfall and coverage area, and ~29% more stratiform rain than non-MP-induced MCSs. In general, larger and more vigorous MPs resulted in more hydrologically impactful MCSs. The most directly attributable benefit to MCS initiation was from MP-enhanced background vertical motion and thermodynamic instability (e.g., increased CAPE).« less

Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use daily in situ precipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four keymore » hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events.« less

Here, the effect of Al on α' precipitation in FeCrAl alloys was studied through thermal aging of several model binary FeCr and FeCrAl alloys with Cr content of 13 wt.%, 17 wt.%, and 25 wt.% with and without 5.5 wt.% Al. Aging was performed up to 1,000h at varying temperatures from 400-500°C. At 400°C no age hardening was observed due to slow kinetics at this temperature. For both the 17Cr and 25Cr alloys, the addition of Al shows a lowering of the miscibility gap, consistent with other reports in literature. Interestingly, however, for the 25Cr alloys the addition of Almore » in the FeCrAl ternary alloy accelerated α' precipitation below the miscibility gap. Such enhanced precipitation has also been predicted by our atomistic kinetic Monte Carlo (AKMC) simulations. While previous literature has often focused on Al suppressing α', here we show that while Al can lower the miscibility gap in the Fe-Cr ferritic system thermodynamically, it may also enhance the kinetics of precipitation.« less

Atmospheric rivers (ARs) transport vast amounts of moisture from low to high latitude regions. One region particularly impacted by ARs is Interior Alaska (AK). We analyze the impact of ARs on the annual river ice breakup date for 26 locations in AK. We investigate the AR-driven rise in local air temperatures and explore the relationship between ARs and precipitation, including extremes and interannual variability. We found that AR events lead to an increase in local air temperatures for over 1 week (by ≈ 1 °C). ARs account for 40% of total precipitation, explain 47% of precipitation variability, and make up 59%more » of extreme precipitation events, each year. By estimating the heat transfer between winter precipitation and the river ice surface, we conclude that increased precipitation during the coldest period of the year delays river ice breakup dates, while precipitation occurring close to the breakup date has little impact on breakup timing.« less

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite-derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High-dimensional model representation is used to disentangle the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM-cloud-precipitation feedback varies with cloud height and time lag and displays a large spatialmore » variability. Positive anomalies in antecedent 7-hr SM and land-surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM-cloud-precipitation feedback and aids in the diagnosis of land-atmosphere interactions simulated by weather and climate models.« less

In numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate (Au), which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local Au is commonly parameterized as a function of DSD moments: cloud droplet number (n_c) and mass (q_c) concentrations. When applied in a large-scale model, the grid-mean Au must also include a correction, or enhancement factor, to account for the horizontal variability of the cloudmore » properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large-eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local Au, as well as its spatial variability. The analysis of an ensemble of warm low-level cloud cases shows that the E3SMv2 formulation represents the Au reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM-tuned local Au rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of n_c and its co-variability with q_c in parameterizing the grid-mean Au.« less

The relationship between precipitation and evapotranspiration (ET) is critical to understanding water cycle related dynamics in ecosystems, including crops. Existing studies of bioenergy crops have primarily focused on annual or seasonal ET rates, with less attention given to the immediate ET response following precipitation events. This study examines the variation in ET rates in the days subsequent to precipitation events across various bioenergy crops—corn, switchgrass, and prairies—utilizing 13 years (2010–2022) of growing season data. Meteorological and eddy covariance flux data were collected from seven eddy covariance flux towers as part of the GLBRC scale-up experiment at the Kellogg Biological Stationmore » Long Term Ecological Research sites. The analysis revealed that average ET peaked the day after precipitation and declined linearly over the following days, with a statistically significant relationship (p-value = 0.00027, R2 = 0.96). Neither the type of biofuel vegetation nor the historical land use significantly influenced ET post-precipitation events (p-values = 0.53 and 0.153, respectively). Key predictors of ET following precipitation events include shortwave radiation, season, day of the year, ambient temperature, vapor pressure deficit (VPD), long-wave radiation, precipitation amount, soil moisture, and annual variability. These findings enhance our comprehension of ET responses in bioenergy crop systems, with implications for water management in sustainable agriculture.« less

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection-permitting resolution. This study spans a 2-month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewermore » MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr^–1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.« less

Dryland precipitation depends on upwind and local moisture sources via moisture recycling. How upwind moisture variations affect interannual variations of downwind precipitation and vegetation in China's drylands remains unclear. We used high-resolution moisture tracking data sets and found terrestrial moisture (93%) was the dominant moisture source for China's drylands, especially from drylands themselves (46%). In most dryland grids, we observed strong correlations between precipitation and upwind moisture sources from 2003 to 2022 (median r = 0.55), with a more significant effect in drier areas. These demonstrated the upwind moisture control on interannual variations of dryland precipitation, in which internal moisture from drylandsmore » exceeds the influence of external terrestrial sources. The upwind moisture variations, especially the recycled moisture of drylands, propagate to influence downwind vegetation greenness in precipitation-sensitive dryland areas. Our findings revealed that upwind moisture variations induced by climate or land-cover changes have important implications for water and food security in China's drylands.« less