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Twenty-First-Century Changes in U.S. Regional Heavy Precipitation Frequency Based on Resolved Atmospheric Patterns
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The Effects of Heavy Precipitation on Polarimetric Radio Occultation (PRO) Bending Angle Observations
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Signatures of Heavy Precipitation on the Thermodynamics of Clouds Seen From Satellite: Changes Observed in Temperature Lapse Rates and Missed by Weather Analyses
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Using Large Ensembles to Identify Regions of Systematic Biases in Moderate-to-Heavy Daily Precipitation
Because of internal variability in both the real-world and global climate models, it is unclear whether disagreement between models and observations reflects true systematic differences, or different phasing of internal variability in the short observational period. Here, we address this issue through an examination of moderate-to-heavy precipitation in large ensembles of global climate models. We find that model inconsistency with a global observational product is lowest for extratropical precipitation in northern hemisphere winter. The inconsistency is systematically greater for the southern hemisphere winter, but the difference between hemispheres could be due to observational quality. Moderate-to-heavy extratropical winter precipitation is less inconsistent than moderate-to-heavy tropical precipitation in most models. Within the tropics, moderate-to-heavy precipitation is particularly inconsistent with the reference in regions including the Caribbean (especially during JJA), the northern and southern flanks of the Pacific and Atlantic ITCZ, and the Indian Ocean.
Subseasonal-to-seasonal Prediction of Atmospheric Rivers in the Western United States
Atmospheric rivers (ARs) account for a large portion of winter total precipitation (especially heavy precipitation) in the coastal states of western US (Dettinger et al., 2011; Rutz et al., 2014), but accurate subseasonal-to-seasonal predictions of ARs remain challenging. The challenge stems from relatively short observational records, the interactions of different climate modes and their nonlinear impacts on ARs, and uncertainties in AR activity due to AR detection methods (Huang et al., 2021). Important sources of AR prediction skill include different climate modes, such as the El Niño/Southern Oscillation (ENSO), Madden–Julian oscillation (MJO), and quasi-biennial oscillation (QBO) (DeFlorio et al., 2018, 2019; Mundhenk et al., 2018). Nevertheless, a lack of consensus on climate mode–AR relationship limits the subseasonal-to-seasonal predictive skills on ARs.
Detection of heavy convective precipitation using rapid interval digital radar and satellite data
A study was carried out to investigate the evolution of thunderstorms with short-interval (5 min) geosynchronous digital satellite data and with digital radar data (12 min interval) in order to determine the potential and limitations of using the satellite data to detect regions of heavy convective precipitation. Initial results indicate that satellite-based estimates of thunderstorm updraft intensity are related to storm precipitation rate as indicated in the digital radar data. The conclusions give support to the effort to use satellite data for detection of heavy convective precipitation, but emphasize the need for high time resolution (5 min) data.
Soil Moisture and Mesoscale Convective Complex Development During the 1993 US Midwest Flood: Results from the MM5-PLACE Atmosphere/Land-Surface Model
The 1993 US Midwest Flood produced record levels of flooding, in the Mississippi River Basin. This flooding resulted from repeated frontal passages and mesoscale convective complexes (MCCs) during the months of June and July. A better understanding of processes that influenced MCC development during the 1993 Flood may lead to improved forecasts of heavy precipitation and flooding. Here, we consider the impact of soil moisture on MCC development during a two-day period (June 23-24) of the 1993 US Midwest Flood. The purpose of this study is to assess the importance of soil moisture distribution on the timing, intensity, and location of heavy precipitation. In this study, the MM5-PLACE Atmosphere/Land-Surface Model is utilized. The atmospheric component consists of the Penn State/NCAR MM5 mesoscale model, and the land-surface component consists of the Goddard Parameterization for Land Atmosphere-Cloud Exchange (PLACE). Initial soil moisture is provided from two sources: 1) NCEP reanalysis, and 2) Antecedent Precipitation Index (API) using NOAA rain gauge measurements as a proxy for soil moisture. NCEP reanalysis provides coarse resolution initial soil moisture (2.5 degree), while API provides high resolution initial soil moisture (10-200 km depending on NOAA rain gauge spacing). Initial results indicate that the distribution of soil moisture has a significant impact on the timing and location of heavy precipitation during this two-day flood event. Precipitation in simulations with high resolution initial soil moisture agrees more closely with observed precipitation. These results suggest that high resolution soil moisture observations are necessary to accurately predict severe storm development, heavy precipitation, and subsequent flooding.
Evaluating Contemporary and Future-Scenario Substantial-Precipitation Events in the Missouri River Basin Using Object-Oriented Analysis
The Missouri River Basin is the largest single river basin in the United States, and, as such, it plays an important role in natural ecosystems as well as the country’s economy, through agriculture, hydroelectric power generation, and transportation. Episodes of heavy precipitation can have a substantial negative impact on all these aspects of the basin, so understanding how well these episodes are simulated and projected to change in the future climate is important. We analyzed contemporary and projected mid-century behavior of heavy-precipitation episodes using an object-oriented analysis to diagnose short-term (≥5-day) and extended-period (≥30-day) events with substantial precipitation, using PRISM gridded, observed precipitation and RegCM4 regional-climate simulations that used outputs from two different GCMs for boundary conditions. The simulations were produced for the North American portion of the CORDEX program. A 25 km grid was used for the simulations and for aggregated PRISM precipitation. Overall, the simulated contemporary-climate events compared favorably with the PRISM events’ frequency and duration. The simulated event areas tended to be larger than the areas in the PRISM events, suggesting that the effective resolution of the simulations is greater than 25 km. Event areas and durations change little going from contemporary to scenario climate. The short-term events increase in frequency by an amount commensurate with the increase in mean precipitation simulated for the basis. However, the extended-term events showed little change in frequency, despite the average precipitation increase. Roughly half the extended-period events overlapped with at least one short-term event in both the observations and the simulations. Extended-period events that overlap a short-term event generally have larger areas and longer durations compared to their counterparts with no overlapping short-term events. Understanding the climate dynamics yielding the two types of extended-period events could be useful for assessing future changes in the Missouri River Basin’s heavy precipitation events and their impact.
Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High-Resolution Global Nonhydrostatic Model
Current climate model resolution cannot accurately describe the complex topography over the Tibetan Plateau, which limits our understanding of past and future precipitation over this region. This study investigates the daily precipitation characteristics and its future changes over the Tibetan Plateau, especially for the southern slope of Tibetan Plateau (SSTP), by 14-km Nonhydrostatic ICosahedral Atmospheric Model (NICAM) with explicitly calculated convection for historical and future 30-years period. By comparing with the satellite Global Precipitation Measurement (GPM), NICAM well reproduces the historical precipitation spatial pattern, seasonal cycle, and the extreme precipitation belt over SSTP, but overestimates precipitation amount by~35%. It is found that heavy precipitation probability decreases as elevation becomes higher while the light precipitation generally shows the opposite. For the precipitation changes during June to September from 1979–2008 to 2075–2104, NICAM predicts that mean precipitation will decrease over low-level SSTP but increase over high-level SSTP. Nonprecipitation and heavy precipitation probability will increase while light precipitation probability will decrease in the future over SSTP. The extreme precipitation probability and intensity will increase ~50%/°C and ~8%/°C over SSTP, and this increase is more obvious as elevation becomes higher. The robust increase of extreme precipitation along the SSTP topography is unique and has not been identified by the climate model simulations before. The strong meridional gradient of specific humidity over SSTP is found to be further enhanced under global warming, and this gradient enhancement is suggested to be responsible for the increase in the extreme precipitation over SSTP.
Assessment of Storm-Associated Precipitation and Its Extremes Using Observational Data Sets and Climate Model Short-Range Hindcasts
Heavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio-economic loss. Here, in this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short-range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short-range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm-associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon-based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy-precipitating storms.
Precipitation Probability and Its Future Changes From a Global Cloud-Resolving Model and CMIP6 Simulations
This study investigates precipitation probability and its future changes using simulations from the Non-hydrostatic Icosahedral Atmospheric Model (NICAM), a global cloud-resolving model, and Coupled Model Intercomparison Project Phase 6 (CMIP6) models. For the precipitation probability comparison between models and Tropical Rainfall Measuring Mission (TRMM) during 1998–2008, the difference is greatly reduced by regridding to a common coarser resolution, but the difference signs largely remain. Both NICAM and CMIP6 model ensemble mean show less global-mean nonprecipitation fractions than observations, but NICAM agrees better with observations. For global-mean light precipitation probability, NICAM (CMIP6 model ensemble mean) is smaller (larger) than TRMM. NICAM shows higher heavy precipitation probability than TRMM in native resolutions, but it is close to TRMM and falls in the CMIP6 model range after regridding. For the precipitation probability changes from 1979–2008 to 2075–2104, NICAM predicts an increase in nonprecipitation and extreme heavy (>100 mm/day) precipitation probability but a slight decrease in the probability for precipitation <100 mm/day. After regridding to 2.5°, the precipitation intensity where the probability starts to increase becomes ~20 mm/day and the relative increases in nonprecipitation and heavy precipitation probability become much larger. Future heavy precipitation probability relative increases from CMIP6 models are much larger than NICAM at their native resolutions, but NICAM falls in the CMIP6 model range after regridding, indicating robust future increase (decrease) in heavy (light) precipitation probability regardless of underlying convection treatments. Discrepancy, however, exists in the projected nonprecipitation fraction changes: NICAM shows that nonprecipitation probability will increase over most of low and middle latitudes where CMIP6 models show less increases and even decreases.
Chapter 7: Precipitation Change in the United States
Annual precipitation has decreased in much of the West, Southwest, and Southeast and increased in most of the Northern and Southern Plains, Midwest, and Northeast. A national average increase of 4% in annual precipitation since 1901 is mostly a result of large increases in the fall season. Heavy precipitation events in most parts of the United States have increased in both intensity and frequency since 1901. There are important regional differences in trends, with the largest increases occurring in the northeastern United States. In particular, mesoscale convective systems (organized clusters of thunderstorms)-the main mechanism for warm season precipitation in the central part of the United States-have increased in occurrence and precipitation amounts since 1979. The frequency and intensity of heavy precipitation events are projected to continue to increase over the 21st century (high confidence). Mesoscale convective systems in the central United States are expected to continue to increase in number and intensity in the future. There are, however, important regional and seasonal differences in projected changes in total precipitation: the northern United States, including Alaska, is projected to receive more precipitation in the winter and spring, and parts of the southwestern United States are projected to receive less precipitation in the winter and spring. Northern Hemisphere spring snow cover extent, North America maximum snow depth, snow water equivalent in the western United States, and extreme snowfall years in the southern and western United States have all declined, while extreme snowfall years in parts of the northern United States have increased. Projections indicate large declines in snowpack in the western United States and shifts to more precipitation falling as rain than snow in the cold season in many parts of the central and eastern United States.
Notable impact of wildfires in the western United States on weather hazards in the central United States
Increased wildfire events constitute a significant threat to life and property in the United States. Wildfire impact on severe storms and weather hazards is another pathway that threatens society, and our understanding of which is very limited. Here, we use unique modeling developments to explore the effects of wildfires in the western US (mainly California and Oregon) on precipitation and hail in the central US. We find that the western US wildfires notably increase the occurrences of heavy precipitation rates by 38% and significant severe hail (≥2 in.) by 34% in the central United States. Both heat and aerosols from wildfires play an important role. By enhancing surface high pressure and increasing westerly and southwesterly winds, wildfires in the western United States produce ( 1 ) stronger moisture and aerosol transport to the central United States and ( 2 ) larger wind shear and storm-relative helicity in the central United States. Both the meteorological environment more conducive to severe convective storms and increased aerosols contribute to the enhancements of heavy precipitation rates and large hail. Moreover, the local wildfires in the central US also enhance the severity of storms, but their impact is notably smaller than the impact of remote wildfires in California and Oregon because of the lessened severity of the local wildfires. As wildfires are projected to be more frequent and severe in a warmer climate, the influence of wildfires on severe weather in downwind regions may become increasingly important.
A Canonical Repsonse of Precipitation Characteristics to Global Warming from CMIP5 Models
In this study, we find from analyses of projections of 14 CMIP5 models a robust, canonical global response in rainfall characteristics to a warming climate. Under a scenario of 1% increase per year of CO2 emission, the model ensemble projects globally more heavy precipitation (+7+/-2.4%/K1), less moderate precipitation (-2.5+/-0.6%/K), more light precipitation (+1.8+/-1.3%/K1), and increased length of dry (no-rain) periods (+4.7+/-2.1%/K). Regionally, a majority of the models project a consistent response with more heavy precipitation over climatologically wet regions of the deep tropics, especially the equatorial Pacific Ocean and the Asian monsoon regions, and more dry periods over the land areas of the subtropics and the tropical marginal convective zones. Our results suggest that increased CO2 emissions induce a global adjustment in circulation and moisture availability manifested in basic changes in global precipitation characteristics, including increasing risks of severe floods and droughts in preferred geographic locations worldwide.
Contrasting Responses of Hailstorms to Anthropogenic Climate Change in Different Synoptic Weather Systems
Hailstones and extreme precipitation generate substantial economic losses across the United States (US) and the globe. Their strong association with short-lived, intense convective storms poses a great challenge in predicting their future changes. Here, we conducted model simulations at 1.2 km grid spacing for severe convective storms with large hail and heavy precipitation that occurred in two typical types of synoptic-scale environments in spring seasons over the central US under both current and future climate conditions. We find that the responses of large hail (diameters >2.5 cm) to anthropogenic climate change (ACC) are markedly different between the hailstorms developed in the two types of synoptic-scale environments, with over 110% increase in large hail occurrences for the frontal systems, whereas less than 30% increase for the Great Plains low-level jet (GPLLJ) systems. This is explained by the larger increase in convective intensity and updraft width and a smaller increase in warm cloud depth in the frontal storms compared with the GPLLJ storms. Interestingly, the occurrences and intensity of heavy precipitation (rain rate >20 mm hr-1) in both types of systems are similarly sensitive to ACC (e.g., 40% and 33% increases in the occurrences for the frontal and GPLLJ systems, respectively). These results advance our knowledge of hail projection and have important implications for managing risks for future hail.
The evolution of the mesoscale environment of severe local storms Preliminary modeling results
A mesoscale model is employed for predicting two severe weather events observed during thy 1979 SESAME field programs. Particular attention is given to the development and decay of a low-level jet, to the formation of a mesoscale convective complex (MCC) and its modification of lower- and upper-level circulation, to the formation of mesoscale regions of heavy precipitation, the intensification of surface warm and cold fronts, the formation of drylines, dynamic coupling of upper- and low-level jets, the formation of a mountain wave, and the formation and maintenance of capping inversions. A summary of the physics and parameters of a tornado outbreak and a heavy precipitation event are given for a simple bulk-PBL formulation with no heating. A ten-layer model was demonstrated to be sufficient for generating and maintaining distinct vertical gradients of temperature, moisture, and wind across low-level inversions.
Preliminary Results of a U.S. Deep South Modeling Experiment Using NASA SPoRT Initialization Datasets for Operational National Weather Service Local Model Runs
A joint collaborative modeling effort among the NWS offices in Mobile, AL, and Houston, TX, and NASA Short-term Prediction Research and Transition (SPoRT) Center began during the 2011-2012 cold season, and continued into the 2012 warm season. The focus was on two frequent U.S. Deep South forecast challenges: the initiation of deep convection during the warm season; and heavy precipitation during the cold season. We wanted to examine the impact of certain NASA produced products on the Weather Research and Forecasting Environmental Modeling System in improving the model representation of mesoscale boundaries such as the local sea-, bay- and land-breezes (which often leads to warm season convective initiation); and improving the model representation of slow moving, or quasi-stationary frontal boundaries (which focus cold season storm cell training and heavy precipitation). The NASA products were: the 4-km Land Information System, a 1-km sea surface temperature analysis, and a 4-km greenness vegetation fraction analysis. Similar domains were established over the southeast Texas and Alabama coastlines, each with an outer grid with a 9 km spacing and an inner nest with a 3 km grid spacing. The model was run at each NWS office once per day out to 24 hours from 0600 UTC, using the NCEP Global Forecast System for initial and boundary conditions. Control runs without the NASA products were made at the NASA SPoRT Center. The NCAR Model Evaluation Tools verification package was used to evaluate both the positive and negative impacts of the NASA products on the model forecasts. Select case studies will be presented to highlight the influence of the products.