skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.
Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.  more » « less
Award ID(s):
2012850
PAR ID:
10333643
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Frontiers in Climate
Volume:
3
ISSN:
2624-9553
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Abstract The spatial and temporal ordering of precipitation occurrence impacts ecosystems, streamflow, and water availability. For example, both large-scale climate patterns and local landscapes drive weather events, and the typical speeds and directions of these events moving across a basin dictate the timing of flows at its outlet. We address the predictability of precipitation occurrence at a given location, based on the knowledge of past precipitation at surrounding locations. We identify “dominant directions of precipitation influence” across the continental United States based on a gridded daily dataset. Specifically, we apply information theory–based measures that characterize dominant directions and strengths of spatial and temporal precipitation dependencies. On a national average, this dominant direction agrees with the prevalent direction of weather movement from west to east across the country, but regional differences reflect topographic divides, precipitation gradients, and different climatic drivers of precipitation. Trends in these information relationships and their correlations with climate indices over the past 70 years also show seasonal and spatial divides. This study expands upon a framework of information-based predictability to answer questions about spatial connectivity in addition to temporal persistence. The methods presented here are generally useful to understand many aspects of weather and climate variability. 
    more » « less
  2. Abstract Water is redistributed from evaporation sources to precipitation sinks through atmospheric moisture transport. In the Brazilian Amazon, the spatial and temporal variability of dry season moisture sources for key agricultural regions has not been investigated. This study investigates moisture sources for dry season rainfall in the state of Rondônia in Brazil, especially during drought years. Using a precipitationshed framework, we quantified the variability of moisture contributions to rainfall in the state of Rondônia (Brazilian Amazon) and the influence of synoptic circulation patterns. Ocean evaporation accounts for 58% of mean dry season precipitation while continental recycling contributed 42%. During drought years, although forests maintain or increase evapotranspiration, the moisture contribution of both ocean and forests to dry season rainfall decreases due to the synoptic circulation changes, reducing the moisture transport into Rondônia. 
    more » « less
  3. Abstract Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2‐m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high‐pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low‐level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings. 
    more » « less
  4. Abstract Temporal changes in the seasonality of extreme precipitation, and possible teleconnections between the seasonality of extreme precipitation and large‐scale climate patterns are not well understood. In this study, we investigated temporal changes in seasonality of annual daily maximum (ADM) and monthly maximum (MM) precipitation indices over the period 1951–2014 for 1,108 stations across the contiguous USA. We also examined seasonality of extreme precipitation during negative and positive phases of three major oscillations: the El Niño–Southern Oscillation, the Northern Atlantic Oscillation, and the Pacific Decadal Oscillation. Our results show that many climate regions within the contiguous USA display distinct seasonality for both ADM and MM. Comparison of seasonality between two historical records of equal length, that is, before and after 1981, shows great spatial variability across the contiguous USA. While a spatial coherence of change in the mean date of occurrence of extreme precipitation across a large area is not visible, a cluster of stations showing decrease in strength of seasonality for the recent period is concentrated in the eastern Gulf Coast and coastal sites of Northeast and Northwest regions. Extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large‐scale climate variabilities have a strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous USA. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality. 
    more » « less
  5. 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. 
    more » « less