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Jeremy L. Weiss
,
Christopher L. Castro
, and
Jonathan T. Overpeck

Abstract

Higher temperatures increase the moisture-holding capacity of the atmosphere and can lead to greater atmospheric demand for evapotranspiration, especially during warmer seasons of the year. Increases in precipitation or atmospheric humidity ameliorate this enhanced demand, whereas decreases exacerbate it. In the southwestern United States (Southwest), this means the greatest changes in evapotranspirational demand resulting from higher temperatures could occur during the hot–dry foresummer and hot–wet monsoon. Here seasonal differences in surface climate observations are examined to determine how temperature and moisture conditions affected evapotranspirational demand during the pronounced Southwest droughts of the 1950s and 2000s, the latter likely influenced by warmer temperatures now attributed mostly to the buildup of greenhouse gases. In the hot–dry foresummer during the 2000s drought, much of the Southwest experienced significantly warmer temperatures that largely drove greater evapotranspirational demand. Lower atmospheric humidity at this time of year over parts of the region also allowed evapotranspirational demand to increase. Significantly warmer temperatures in the hot–wet monsoon during the more recent drought also primarily drove greater evapotranspirational demand, but only for parts of the region outside of the core North American monsoon area. Had atmospheric humidity during the more recent drought been as low as during the 1950s drought in the core North American monsoon area at this time of year, greater evapotranspirational demand during the 2000s drought could have been more spatially extensive. With projections of future climate indicating continued warming in the region, evapotranspirational demand during the hot–dry and hot–wet seasons possibly will be more severe in future droughts and result in more extreme conditions in the Southwest, a disproportionate amount negatively impacting society.

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Christopher L. Castro
,
Roger A. Pielke Sr.
, and
Jimmy O. Adegoke

Abstract

Fifty-three years of the NCEP–NCAR Reanalysis I are dynamically downscaled using the Regional Atmospheric Modeling System (RAMS) to generate a regional climate model (RCM) climatology of the contiguous United States and Mexico. Data from the RAMS simulations are compared to the recently released North American Regional Reanalysis (NARR), as well as observed precipitation and temperature data. The RAMS simulations show the value added by using a RCM in a process study framework to represent North American summer climate beyond the driving global atmospheric reanalysis. Because of its enhanced representation of the land surface topography, the diurnal cycle of convective rainfall is present. This diurnal cycle largely governs the transitions associated with the evolution of the North American monsoon with regards to rainfall, the surface energy budget, and surface temperature. The lower frequency modes of convective rainfall, though weaker, account for rainfall variability at a remote distance from elevated terrain. As in previous studies with other RCMs, RAMS precipitation is overestimated compared to observations. The Great Plains low-level jet (LLJ) is also well represented in both RAMS and NARR, but the Baja LLJ and associated gulf surges are not.

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Christopher L. Castro
,
Thomas B. McKee
, and
Roger A. Pielke Sr.

Abstract

The North American monsoon is a seasonal shift of upper- and low-level pressure and wind patterns that brings summertime moisture into the southwest United States and ends the late spring wet period in the Great Plains. The interannual variability of the North American monsoon is examined using the NCEP–NCAR reanalysis (1948–98). The diurnal and seasonal evolution of 500-mb geopotential height, integrated moisture flux, and integrated moisture flux convergence are constructed using a 5-day running mean for the months May through September. All of the years are used to calculate an average daily Z score that removes the diurnal, seasonal, and intraseasonal variability. The 30-day average Z score centered about the date is correlated with Pacific sea surface temperature anomaly (SSTA) indices associated with the El Niño–Southern Oscillation (ENSO) and the North Pacific oscillation (NPO). These indices are Niño-3, a North Pacific index, and a Pacific index that combines the previous two. Regional time-evolving precipitation indices for the Southwest and Great Plains, which consider the total number of wet or dry stations in a region, are also correlated with the SSTA indices. The use of nonnormally distributed point source precipitation data is avoided.

Teleconnections are computed relative to the climatological evolution of the North American monsoon, rather than to calendar months, thus more accurately accounting for the climatological changes in the large-scale circulation. Tropical and North Pacific SSTs are related to the occurrence of the Pacific Transition and East Pacific teleconnection patterns, respectively, in June and July. A high (low) NPO phase and El Niño (La Niña) conditions favor a weaker (stronger) and southward (northward) displaced monsoon ridge. These teleconnection patterns affect the timing and large-scale distribution of monsoon moisture. In the Great Plains, the spring wet season is lengthened (shortened) and early summer rainfall and integrated moisture flux convergence are above (below) average. In the Southwest, monsoon onset is late (early) and early summer rainfall and integrated moisture flux convergence are below (above) average. Relationships with Pacific SSTA indices decay in the later part of the monsoon coincident with weakening of the jet stream across the Pacific and strengthening of the monsoon ridge over North America. The most coherent summer climate patterns occur over the entire western United States when the Pacific index is substantially high or low, such as during the Midwest flood of 1993 and drought of 1988. The Pacific index in spring is a good predictor of early summer height anomalies over the western United States when the time evolution of the North Pacific SST dipole is considered.

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Matthew B. Switanek
,
Peter A. Troch
, and
Christopher L. Castro

Abstract

In a water-stressed region, such as the southwestern United States, it is essential to improve current seasonal hydroclimatic predictions. Typically, seasonal hydroclimatic predictions have been conditioned by standard climate indices, for example, Niño-3 and Pacific decadal oscillation (PDO). In this work, the statistically unique relationships between sea surface temperatures (SSTs) and particular basins’ hydroclimates are explored. The regions where global SSTs are most correlated with the Little Colorado River and Gunnison River basins’ hydroclimates are located throughout the year and at varying time lags. The SSTs, from these regions of highest correlation, are subsequently used as hydroclimatic predictors for the two basins. This methodology, named basin-specific climate prediction (BSCP), is further used to perform hindcasts. The hydroclimatic hindcasts obtained using BSCP are shown to be closer to the historical record, for both basins, than using the standard climate indices as predictors.

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Christopher L. Castro
,
Adriana B. Beltrán-Przekurat
, and
Roger A. Pielke Sr.

Abstract

Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.

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Erick R. Rivera
,
Francina Dominguez
, and
Christopher L. Castro

Abstract

Inland-penetrating atmospheric rivers (ARs) can affect the southwestern United States and significantly contribute to cool season (November–March) precipitation. In this work, a climatological characterization of AR events that have led to cool season extreme precipitation in the Verde River basin (VRB) in Arizona for the period 1979/80–2010/11 is presented. A “bottom up” approach is used by first evaluating extreme daily precipitation in the basin associated with AR occurrence, then identifying the two dominant AR patterns (referred to as Type 1 and Type 2, respectively) using a combined EOF statistical analysis. The results suggest that AR events in the Southwest do not form and develop in the same regions. Water vapor content in Type 1 ARs is obtained from the tropics near Hawaii (central Pacific) and enhanced in the midlatitudes, with maximum moisture transport over the ocean at low levels of the troposphere. On the other hand, moisture in Type 2 ARs has a more direct tropical origin and meridional orientation with maximum moisture transfer at midlevels. Nonetheless, both types of ARs cross the Baja Peninsula before affecting the VRB. In addition to Type 1 and Type 2 ARs, observations reveal AR events that are a mixture of both patterns. These cases can have water vapor transport patterns with both zonal and meridional signatures, and they can also present double peaks in moisture transport at low- and midlevels. This seems to indicate that the two “types” can be interpreted as end points of a range of possible directions.

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Timothy M. Lahmers
,
Christopher L. Castro
, and
Pieter Hazenberg

Abstract

Evidence for surface and atmosphere coupling is corroborated in both modeling and observation-based field experiments. Recent advances in high-performance computing and development of convection-permitting regional-scale atmospheric models combined with high-resolution hydrologic models have made modeling of surface–atmosphere interactions feasible for the scientific community. These hydrological models can account for the impacts of the overland flow and subsurface flow components of the hydrologic cycle and account for the impact of lateral flow on moisture redistribution at the land surface. One such model is the Weather Research and Forecasting (WRF) regional atmospheric model that can be coupled to the WRF-Hydro hydrologic model. In the present study, both the uncoupled WRF (WRF-ARW) and otherwise identical WRF-Hydro model are executed for the 2017 and 2018 summertime North American monsoon (NAM) seasons in semiarid central Arizona. In this environment, diurnal convection is impacted by precipitation recycling from the land surface. The goal of this work is to evaluate the impacts that surface runoff and shallow subsurface flow, as depicted in WRF-Hydro, have on surface–atmosphere interactions and convection in a coupled atmospheric simulation. The current work assesses the impact of surface hydrologic processes on 1) local surface energy budgets during the NAM throughout Arizona and 2) the spectral behavior of diurnally driven NAM convection. Model results suggest that adding surface and subsurface flow from WRF-Hydro increases soil moisture and latent heat near the surface. This increases the amount of instability and moisture available for deep convection in the model simulations and enhances the organization of convection at the peak of the diurnal cycle.

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Geidy Rodriguez-Vera
,
Rosario Romero-Centeno
,
Christopher L. Castro
, and
Víctor Mendoza Castro

Abstract

This work describes dominant patterns of coupled interannual variability of the 10-m wind and sea surface temperature in the Caribbean Sea and the Gulf of Mexico (CS&GM) during the period 1982–2016. Using a canonical correlation analysis (CCA) between the monthly mean anomalies of these fields, four coupled variability modes are identified: the dipole (March–April), transition (May–June), interocean (July–October), and meridional-wind (November–February) modes. Results show that El Niño–Southern Oscillation (ENSO) influences almost all the CS&GM coupled modes, except the transition mode, and that the North Atlantic Oscillation (NAO) in February has a strong negative correlation with the dipole and transition modes. The antisymmetric relationships found between the dipole mode and the NAO and ENSO indices confirm previous evidence about the competing remote forcings of both teleconnection patterns on the tropical North Atlantic variability. Precipitation in the CS and adjacent oceanic and land areas is sensitive to the wind–SST coupled variability modes from June to October. These modes seem to be strongly related to the interannual variability of the midsummer drought and the meridional migration of the intertropical convergence zone in the eastern Pacific. These findings may eventually lead to improving seasonal predictability in the CS&GM and surrounding land areas.

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Stephen W. Bieda III
,
Christopher L. Castro
,
Steven L. Mullen
,
Andrew C. Comrie
, and
Erik Pytlak

Abstract

Relationships between transient upper-tropospheric troughs and warm season convective activity over the southwest United States and northern Mexico are explored. Analysis of geopotential height and vorticity fields from the North American Regional Reanalysis and cloud-to-ground lightning data indicates that the passage of mobile inverted troughs (IVs) significantly enhances convection when it coincides with the peak diurnal cycle (1800–0900 UTC) over the North American monsoon (NAM) region. The preferred tracks of IVs during early summer are related to the dominant modes of Pacific sea surface temperature (SST) variability. When La Niña–like (El Niño–like) conditions prevail in the tropical Pacific and the eastern North Pacific has a horseshoe-shaped negative (positive) SST anomaly, IVs preferentially track farther north (south) and are slightly (typically one IV) more (less) numerous. These results point to the important role that synoptic-scale disturbances play in modulating the diurnal cycle of precipitation over the NAM region and the significant impact that the statistically supported low-frequency Pacific SST anomalies exert on the occurrence and track of these synoptic transients.

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Carlos Manuel Minjarez-Sosa
,
Christopher L. Castro
,
Kenneth L. Cummins
,
Julio Waissmann
, and
David K. Adams

Abstract

A lightning–precipitation relationship (LPR) is studied at high temporal and spatial resolution (5 min and 5 km). As a proof of concept of these methods, precipitation data are retrieved from the National Severe Storms Laboratory (NSSL) NMQ product for southern Arizona and western Texas while lightning data are provided by the National Lightning Detection Network (NLDN). A spatial- and time-invariant (STI) linear model that considers spatial neighbors and time lags is proposed. A data denial analysis is performed over Midland, Texas (a region with good sensor coverage), with this STI model. The LPR is unchanged and essentially equal, regardless of the domain (denial or complete) used to obtain the STI model coefficients. It is argued that precipitation can be estimated over regions with poor sensor coverage (i.e., southern Arizona) by calibrating the LPR over well-covered domains that are experiencing similar storm conditions. To obtain a lightning-estimated precipitation that dynamically updates the model coefficients in time, a Kalman filter is applied to the STI model. The correlation between the observed and estimated precipitation is statistically significant for both models, but the Kalman filter provides a better precipitation estimation. The best demonstration of this application is a heavy-precipitation, high-frequency lightning event in southern Arizona over a region with poor radar and rain gauge coverage. By calibrating the Kalman filter over a data-covered domain, the lightning-estimated precipitation is considerably greater than that estimated by radar alone. Therefore, for regions where both rain gauge and radar data are compromised, lightning provides a viable alternative for improving QPE.

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