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Bruce T. Anderson

Abstract

Using results taken from a finescale (25 km) regional modeling simulation for the summer of 1999, intraseasonal variations in the climatological summertime hydrologic cycle over the southwestern United States are described for two previously identified spatiotemporal precipitation patterns. Over the western portion of the Rocky Mountain plateau, centered on eastern Utah and western Colorado, columnar moisture divergence associated with precipitation is balanced by a combination of seasonal-mean convective moisture convergence and anomalous upper-air (>4 km) large-scale moisture convergence. The actual precipitating events themselves are predicated upon the anomalous upper-level advection of water vapor into the precipitating region; absent this large-scale advection at upper levels, vertical diffusion of moisture into the atmosphere balances large-scale divergence at midlevels, with little precipitation occurring. The anomalous large-scale advection during precipitating events is due primarily to anomalous large-scale vertical fluxes of moisture, with only a slight contribution from large-scale horizontal moisture fluxes. For precipitation located over the eastern portion of the plateau and the elevated orography of eastern New Mexico and southern Colorado, correlated moisture budget terms indicate that precipitation is again related to mean convective moisture convergence and anomalous midtroposphere large-scale moisture convergence. As with the western-plateau precipitation regime, this anomalous convergence is strongly correlated with an anomalous vertical advection of moisture; however, for the eastern-plateau regime, this vertical term is the sole source of large-scale moisture convergence contributing to rainfall in the region. In both cases, the vertical moisture convergence may be associated with previously identified intraseasonal modifications of the upper-level monsoon ridge centered over the Sierra Madre, which results in significant large-scale vertical velocities over the precipitating regions.

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Bruce T. Anderson
and
Eric Maloney

Abstract

This paper describes aspects of tropical interannual ocean/atmosphere variability in the NCAR Community Climate System Model Version 2.0 (CCSM2). The CCSM2 tropical Pacific Ocean/atmosphere system exhibits much stronger biennial variability than is observed. However, a canonical correlation analysis technique decomposes the simulated boreal winter tropical Pacific sea surface temperature (SST) variability into two modes, both of which are related to atmospheric variability during the preceding boreal winter. The first mode of ocean/atmosphere variability is related to the strong biennial oscillation in which La Niña–related sea level pressure (SLP) conditions precede El Niño–like SST conditions the following winter. The second mode of variability indicates that boreal winter tropical Pacific SST anomalies can also be initiated by SLP anomalies over the subtropical central and eastern North Pacific 12 months earlier.

The evolution of both modes is characterized by recharge/discharge within the equatorial subsurface temperature field. For the first mode of variability, this recharge/discharge produces a lag between the basin-average equatorial Pacific isotherm depth anomalies and the isotherm–slope anomalies, equatorial SSTs, and wind stress fields. Significant anomalies are present up to a year before the boreal winter SLP variations and two years prior to the boreal winter ENSO-like events. For the second canonical factor pattern, the recharge/discharge mechanism is induced concurrent with the boreal winter SLP pattern approximately one year prior to the ENSO-like events, when isotherms initially deepen and change their slope across the basin. A rapid deepening of the isotherms in the eastern equatorial Pacific and a warming of the overlying SST anomalies then occurs during the subsequent 12 months.

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Bruce T. Anderson
and
John O. Roads

Abstract

Using results taken from a finescale (25 km), regional modeling simulation for the summer of 1999, along with contemporaneous daily surface observations, synoptic variations in summertime precipitation over the southwestern United States are described and analyzed. Two separate techniques for characterizing and evaluating large-scale summertime precipitation patterns within the observed and simulated systems are presented; in addition, these evaluation/characterization techniques are used to analyze the hydrologic forcings associated with observed and simulated modes of rainfall variability. Overall, two robust spatiotemporal precipitation patterns are identified involving 1) precipitation over the western portion of the Rocky Mountain plateau centered on eastern Arizona and southern Utah, and 2) precipitation located over the eastern portion of the plateau and the elevated orography of eastern New Mexico and southern Colorado. Time series associated with these two precipitation regimes are correlated with low-level and midlevel circulation patterns in order to investigate the related large-scale environmental conditions. It is found that for both regimes intraseasonal precipitation is related to the intrusion of midtroposphere, midlatitude low-pressure anomalies over the southwestern United States, resulting in synoptic-scale shifts in the position of the climatological midtroposphere monsoon ridge. The interaction between the resultant midtroposphere pressure fields and the quasi-stationary monsoon surface pressures found over the Rocky Mountain plateau during the summertime produce large-scale vertical velocities consistent with the observed and simulated rainfall patterns associated with each regime.

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Nicole Feldl
,
Bruce T. Anderson
, and
Simona Bordoni

Abstract

Projections of amplified climate change in the Arctic are attributed to positive feedbacks associated with the retreat of sea ice and changes in the lapse rate of the polar atmosphere. Here, a set of idealized aquaplanet experiments are performed to understand the coupling between high-latitude feedbacks, polar amplification, and the large-scale atmospheric circulation. Results are compared to CMIP5. Simulated climate responses are characterized by a wide range of polar amplification (from none to nearly 15-K warming, relative to the low latitudes) under CO2 quadrupling. Notably, the high-latitude lapse rate feedback varies in sign among the experiments. The aquaplanet simulation with the greatest polar amplification, representing a transition from perennial to ice-free conditions, exhibits a marked decrease in dry static energy flux by transient eddies. Partly compensating for the reduced poleward energy flux is a contraction of the Ferrel cell and an increase in latent energy flux. Enhanced eddy energy flux is consistent with the upper-tropospheric warming that occurs in all experiments and provides a remote influence on the polar lapse rate feedback. The main conclusions are that (i) given a large, localized change in meridional surface temperature gradient, the midlatitude circulation exhibits strong compensation between changes in dry and latent energy fluxes, and (ii) atmospheric eddies mediate the nonlinear interaction between surface albedo and lapse rate feedbacks, rendering the high-latitude lapse rate feedback less positive than it would be otherwise. Consequently, the variability of the circulation response, and particularly the partitioning of energy fluxes, offers insights into understanding the magnitude of polar amplification.

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Bruce T. Anderson
,
Catherine Reifen
, and
Ralf Toumi

Abstract

Projections of human-induced climate change impacts arising from the emission of atmospheric chemical constituents such as carbon dioxide typically utilize multiple integrations (or ensembles) of numerous numerical climate change models to arrive at multimodel ensembles from which mean and median values and probabilities can be inferred about the response of various components of the observed climate system. Some responses are considered reliable in as much as the simulated responses show consistency within ensembles and across models. Other responses—particularly at regional levels and for certain parameters such as precipitation—show little intermodel consistency even in the sign of the projected climate changes. The authors’ results show that in these regions the consistency in the sign of projected precipitation variations is greater for intramodel runs (e.g., runs from the same model) than intermodel runs (e.g., runs from different models), indicating that knowledge of the internal “dynamics” of the climate system can provide additional skill in making projections of climate change. Given the consistency provided by the governing dynamics of the model, the authors test whether persistence from an individual model trajectory serves as a good predictor for its own behavior by the end of the twenty-first century. Results indicate that, in certain regions where intermodel consistency is low, the short-term trends of individual model trajectories do provide additional skill in making projections of long-term climate change. The climate forcing for which this forecast skill becomes relatively large (e.g., correct in 75% of the individual model runs) is equivalent to the anthropogenic climate forcing imposed over the past century, suggesting that observed changes in precipitation in these regions can provide guidance about the direction of future precipitation changes over the course of the next century.

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Bruce T. Anderson
,
Catherine Reifen
, and
Ralf Toumi

Abstract

While most projections of climate change and its regional impacts focus on overall changes in the state of the climate system, useful information can also be found in the evolution of the climate system from one state to another. Here the authors introduce one method for identifying regions in which significant and systematic long-term nonlinear evolutions may be present, even given quasi-linear anthropogenic forcing. Using climate change projections taken from simulations of NCAR’s Community Climate System Model, version 3 (CCSM3), the authors then employ the technique to isolate systematic nonlinear behavior of soil moisture variations over the United States. While the projections presented here only represent the results from one model system, it is argued that such nonlinear behavior is an important characteristic of future climate change that should be considered when discussing both short-term and long-term impacts of anthropogenic climate forcing.

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Bruce T. Anderson
,
Dan Gianotti
, and
Guido Salvucci

Abstract

The release of seasonal (and longer) predictions of various climatological quantities is now routine. While undoubtedly devastating to lives and livelihoods, it is unclear whether seasonal extremes in precipitation—for example, extreme dry spells leading to droughts or heavy precipitation events leading to flooding—represent a feasible target for these predictions, that is, whether they are potentially predictable or are instead inherently unpredictable more than a few days to weeks in advance. This paper assesses the potential for predicting seasonal extremes in observed precipitation as a function of region and time of year by decomposing the station-based variance into that attributable to short-memory behavior of typical meteorological events—as generated from station-specific, seasonally varying, daily time-scale stationary stochastic weather models (SSWMs)—and that attributable to longer-time-scale, potentially predictable changes in precipitation-producing processes. Findings suggest the potential for making skillful predictions of seasonal precipitation extremes over the United States is enhanced (reduced) during the cool (warm) season, particularly for heavy precipitation event accumulations. Further, this potential is accentuated along the West Coast, around the Great Lakes, and over the central plains and Ohio River valley but is diminished over the Northeast and northern Great Plains. However, findings also suggest the potential for producing seasonal (and longer) predictions of seasonal precipitation extremes is spatially and seasonally dependent. As such, this paper includes supplemental material for the potentially predictable variance of seasonal extreme dry spell lengths, heavy event accumulations, and total accumulations at 774 stations across all 365 days so readers can evaluate the potential predictability for the location, timing, and metric of most relevance to them.

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Jingyun Wang
,
Bruce T. Anderson
, and
Guido D. Salvucci

Abstract

The intraseasonal variability of summertime precipitation over the southwestern United States is examined using stochastic daily occurrence models combined with empirical daily rainfall distributions to document 1) the seasonal evolution of the frequency and intensity of rainfall events across the summertime monsoon season and 2) the climatological evolution of wet spells, dry spells, and storm events. Study results indicate that the evolution of the North American monsoon system (NAMS) is most apparent in the occurrence of daily rainfall events, which exhibit clear time dependence across the summer season over the southwestern United States and can be principally portrayed by stochastic models. In contrast, the seasonal evolution of NAMS is largely absent in the averaged daily rainfall amount time series. There is also a significant seasonal evolution in the length of dry spells. In the central area of the domain (approximately 39 out of 78 stations) dry-spell lengths tend to increase over the course of the summer season, while on the western fringe (8 out of 78 stations) dry-spell lengths tend to decrease. In contrast, wet spells tend to exhibit constant lengths over the course of the season (44 out of 78 stations). The seasonal trend for storms indicates that the number and duration of storms tend to decrease in September; however, the storm depths tend to be more intense, particularly over the western portion of the domain. Overall, 90% of the area-averaged variance for dry-spell lengths can be explained by the random daily evolution of the stochastic model alone. For wet-spell lengths, the area-averaged variance explained by the stochastic models is 98% and for storm amounts it is 92%. These results suggest that the characteristics of most intraseasonal events over this region (i.e., spell lengths and storm amounts) can be captured by the random evolution of daily rainfall models, even with constant year-to-year statistical parameters, indicating that systematic variations in the background climatic conditions from one year to the next may contribute little to the characteristics of these events.

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Daniel J. Short Gianotti
,
Bruce T. Anderson
, and
Guido D. Salvucci

Abstract

Using weather station data, the parameters of a stationary stochastic weather model (SSWM) for daily precipitation over the contiguous United States are estimated. By construct, the model exactly captures the variance component of seasonal precipitation characteristics (intensity, occurrence, and total amount) arising from high-frequency variance. By comparing the variance of the lower-frequency accumulations (on the order of months) between the SSWM and the original measurements, potential predictability (PP) is estimated. Decomposing the variability into contributions from occurrence and intensity allows one to establish two contributing sources of total PP. Aggregated occurrence is found to have higher PP than either intensity or the seasonal total precipitation, and occurrence and intensity are found to interfere destructively when convolved into seasonal totals. It is recommended that efforts aimed at forecasting seasonal precipitation or attributing climate variability to particular processes should analyze occurrence and intensity separately to maximize signal-to-noise ratios. Significant geographical and seasonal variations exist in all PP components.

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Dan Gianotti
,
Bruce T. Anderson
, and
Guido D. Salvucci

Abstract

A generalizable method is presented for establishing the potential predictability for seasonal precipitation occurrence using rain gauge data. This method provides an observationally based upper limit for potential predictability for 774 weather stations in the contiguous United States. It is found that the potentially predictable fraction varies seasonally and spatially, and that on average 30% of year-to-year seasonal variability is potentially explained by predictable climate processes. Potential predictability is generally highest in winter, appears to be enhanced by orography and land surface coupling, and is lowest (stochastic variance is highest) along the Pacific coast. These results depict “hot” spots of climate variability, for use in guiding regional climate forecasting and in uncovering processes driving climate. Identified “cold” spots are equally useful in guiding future studies as predictable climate signals in these areas will likely be undetectable.

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