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In part III of a three-part study on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) models, the authors examine projections of twenty-first-century climate in the representative concentration pathway 8.5 (RCP8.5) emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. The authors also examine changes in the eastern North Pacific and North Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the northern United States, and Atlantic and east Pacific tropical cyclone activity. [ABSTRACT FROM AUTHOR]

We develop a framework for assessing the drivers and impacts of droughts, built upon a Markov random field and machine learning–based drought identification algorithm that operates simultaneously in space and time. The method uses a precipitation threshold for drought, while considering the drought state of neighboring grid points and identifies contiguous and distinct droughts that propagate through space and time. Importantly, this method can identify droughts of any scale, from a single grid point to those encompassing many thousands. We apply it to North American precipitation from observations and a multimodel ensemble of 67 historical simulations to produce a repository of 25 156 identified droughts. The framework uses an observed drought for comparison, and we choose the 2011–14 drought in the western and central United States, which is among the most severe and persistent in recorded history. As the spatiotemporal characteristics of the simulated droughts become more like the observed drought, we quantify if their local-scale impacts (evaporation, leaf area index, soil moisture, and runoff) and large-scale drivers (atmospheric circulation, sea surface temperature, and modes of climate variability) become predictable. Our findings suggest that ecological impacts are not predictable even when simulated droughts closely match the spatiotemporal characteristics of the observed drought. The drought drivers are also not predictable, with similar droughts occurring under a range of atmosphere–ocean conditions. These results suggest that the drivers and impacts of even the most persistent and severe droughts have limited predictability, although additional work is needed to quantify the role of structural uncertainty and better understand the real-world applicability of climate model-based results. Significance Statement: The purpose of this study was to determine if we can predict the atmosphere–ocean conditions that cause simulated (in climate models) drought events that are similar in their characteristics to an observed drought that occurred in the central and western United States between 2011 and 2014. We also analyze the impact of these events on water availability for ecological and human use and whether these impacts are predictable. Our results suggest that both the conditions that cause droughts and their ecological impacts are largely unpredictable. While further work is needed to understand the implications of these results for real-world drought predictability, our results help to elucidate the processes underlying persistent and severe droughts in climate models. [ABSTRACT FROM AUTHOR]

Numerical models have long predicted that the deforestation of the Amazon would lead to large regional changes in precipitation and temperature, but the extratropical effects of deforestation have been a matter of controversy. This paper investigates the simulated impacts of deforestation on the northwest United States December-February climate. Integrations are carried out using the Ocean-Land-Atmosphere Model (OLAM), here run as a variable-resolution atmospheric GCM, configured with three alternative horizontal grid meshes: 1) 25-km characteristic length scale (CLS) over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; 2) 50-km CLS over the United States, 50-km CLS over the Andes and Amazon, and 200-km CLS in the far-field; and 3) 200-km CLS globally. In the high-resolution simulations, deforestation causes a redistribution of precipitation within the Amazon, accompanied by vorticity and thermal anomalies. These anomalies set up Rossby waves that propagate into the extratropics and impact western North America. Ultimately, Amazon deforestation results in 10%-20% precipitation reductions for the coastal northwest United States and the Sierra Nevada. Snowpack in the Sierra Nevada experiences declines of up to 50%. However, in the coarse-resolution simulations, this mechanism is not resolved and precipitation is not reduced in the northwest United States. These results highlight the need for adequate model resolution in modeling the impacts of Amazon deforestation. It is concluded that the deforestation of the Amazon can act as a driver of regional climate change in the extratropics, including areas of the western United States that are agriculturally important. [ABSTRACT FROM AUTHOR]

This paper undertakes a hydrometeorological analysis of flood events in the central United States. Vertically integrated horizontal water vapor transport over 1979-2011 is calculated in the ECMWF Interim Re-Analysis (ERA-Interim) and used in an algorithm to identify episodes of high moisture transport, or atmospheric rivers (ARs), over the central United States. The AR events are almost evenly divided among the seasons (143 during the winter, 144 during the spring, and 124 during the fall), with a minimum (40) during the summer. The annual maxima (AM) floods from 1105 basins over the period 1980-2011 are used as a measure of the hydrologic impact of the AR events. Of these basins, 470 (or 42.5%) had more than 50% of their AM floods linked to ARs. Furthermore, 660 of the 1105 basins (59.7%) had 5 or more of their top 10 AM floods related to ARs, indicating that ARs control the upper tail of the flood peak distribution over large portions of the study area. The seasonal composite average of mean sea level pressure anomalies associated with the ARs shows a trough located over the central United States and a ridge over the U.S. East Coast, leading to southerly winds and the advection of moisture over the study region. Based on the findings of this study, ARs are a major flood agent over the central United States. [ABSTRACT FROM AUTHOR]

Accurate representation of mesoscale scale convective systems (MCSs) in climate models is of vital importance to understanding global energy, water cycles, and extreme weather. In this study, we evaluate the simulated MCS features over the United States from the newly developed GFDL global high-resolution (∼50 km) AM4 model by comparing them with the observations during spring to early summer (April–June) and late summer (July–August). The results show that the spatial distribution and seasonality of occurrence and genesis frequency of MCSs are reasonably simulated over the central United States in both seasons. The model reliably reproduces the observed features of MCS duration, translation speed, and size over the central United States, as well as the favorable large-scale circulation pattern associated with MCS development over the central United States during spring and early summer. However, the model misrepresents the amplitude and the phase of the diurnal cycle of MCSs during both seasons. In addition, the spatial distribution of occurrence and genesis frequency of MCSs over the eastern United States is substantially overestimated, with larger biases in early spring and summer. Furthermore, while large-scale circulation patterns are reasonably simulated in spring and early summer, they are misrepresented in the model during summer. Finally, we examine MCS-related precipitation, finding that the model overestimates MCS-related precipitation during spring and early summer, but this bias is insufficient to explain the significant dry bias observed in total precipitation over the central United States. Nonetheless, the dry biases in MCS-associated precipitation during late summer likely contribute to the overall precipitation deficit in the model. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are the dominant rainfall producer over the United States during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here, a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central United States and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean-state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust-front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multicolumn parcel model employing those parameterizations captures readily the cold pool–induced upscale growth feature. It simulates smaller mesoscale clusters over the central United States under global warming due to gust-front slowdown and subsidence strength enhancement, which are further attributed to land aridity–induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection lifetime lengthening, and cold pool mechanical-lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes. [ABSTRACT FROM AUTHOR]

Extreme heat events are a threat to human health, productivity, and food supply, so understanding their drivers is critical to adaptation and resilience. Anticyclonic circulation and certain quasi-stationary Rossby wave patterns are well known to coincide with heatwaves, and soil moisture deficits amplify extreme heat in some regions. However, the relative roles of these two factors in causing heatwaves is still unclear. Here we use constructed circulation analogs to estimate the contribution of atmospheric circulation to heatwaves in the United States in the Community Earth System Model version 1 (CESM1) preindustrial control simulations. After accounting for the component of the heatwaves explained by circulation, we explore the relationship between the residual temperature anomalies and soil moisture. We find that circulation explains over 85% of heatwave temperature anomalies in the eastern and western United States but only 75%–85% in the central United States. In this region, there is a significant negative correlation between soil moisture the week before the heatwave and the strength of the heatwave that explains additional variance. Further, for the hottest central U.S. heatwaves, positive temperature anomalies and negative soil moisture anomalies are evident over a month before heatwave onset. These results provide evidence that positive land–atmosphere feedbacks may be amplifying heatwaves in the central United States and demonstrate the geographic heterogeneity in the relative importance of the land and atmosphere for heatwave development. Analysis of future circulation and soil moisture in the CESM1 Large Ensemble indicates that, over parts of the United States, both may be trending toward greater heatwave likelihood. [ABSTRACT FROM AUTHOR]

The Indian Ocean tripole (IOT) is an independent mode of ocean–atmosphere circulation centered on the tropical Indian Ocean. This study explores the physical mechanisms of the IOT affecting the western United States climate variation during the boreal summer. We find that the IOT is significantly correlated with both western United States summer surface temperature and precipitation anomalies. During positive IOT events, the westerly wind anomalies over the northern Indian Ocean are intensified by two cross-equator airflows over the tropical eastern Indian Ocean and the east coast of Africa. The resulting convergence of air over the northern Bay of Bengal–Indochina Peninsula–northern South China Sea (NBB–IP–NSCS) region (15°–25°N, 80°–125°E) exacerbates the surplus precipitation there. Serving as a heat source, these NBB–IP–NSCS precipitation anomalies can excite a circumglobal teleconnection (CGT)-like pattern that propagates eastward from west-central Asia toward North America along the Asia subtropical westerly jet, further influencing local circulation anomalies. Development of strong anticyclonic circulation over the western United States enhances descending motion and divergence there, resulting in negative precipitation anomalies. This circulation anomaly also induces the diabatic heating anomalies through allowing more solar radiation to reach the ground surface, further increasing the surface temperature anomalies. Meanwhile, the increased tropospheric temperature also raises local surface temperatures by modulating the adiabatic air expansion and compression. Ultimately, the CGT-like pattern associated with NBB–IP–NSCS precipitation anomalies sets up an atmospheric bridge by which the IOT can impact summer climate in the western United States. [ABSTRACT FROM AUTHOR]

Diagnosis of rapidly developing springtime droughts in the central United States has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal "meteorological drought" evolution, here defined as persistent precipitation minus evapotranspiration (P − ET) deficits, revealed in early (1 April–15 May) and late (16 May–30 June) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958–2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1–2 weeks, and generally has a greater impact on the local moisture budget than does reduced poleward moisture transport. The resulting "dynamically driven" evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases around 40 percentiles within about 10 days. Anomalous near-surface warmth develops only after the P − ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P − ET deficits, it still appears to capture much of the springtime development of "flash droughts" as well. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are an important source of precipitation for much of the eastern United States. However, our understanding of the spatiotemporal variability of tropical cyclone precipitation (TCP) and the connections to large-scale atmospheric circulation is limited by irregularly distributed rain gauges and short records of satellite measurements. To address this, we developed a new gridded (0.25° × 0.25°) publicly available dataset of TCP (1948–2015; Tropical Cyclone Precipitation Dataset, or TCPDat) using TC tracks to identify TCP within an existing gridded precipitation dataset. TCPDat was used to characterize total June–November TCP and percentage contribution to total June–November precipitation. TCP totals and contributions had maxima on the Louisiana, North Carolina, and Texas coasts, substantially decreasing farther inland at rates of approximately 6.2–6.7 mm km−1. Few statistically significant trends were discovered in either TCP totals or percentage contribution. TCP is positively related to an index of the position and strength of the western flank of the North Atlantic subtropical high (NASH), with the strongest correlations concentrated in the southeastern United States. Weaker inverse correlations between TCP and El Niño–Southern Oscillation are seen throughout the study site. Ultimately, spatial variations of TCP are more closely linked to variations in the NASH flank position or strength than to the ENSO index. The TCP dataset developed in this study is an important step in understanding hurricane–climate interactions and the impacts of TCs on communities, water resources, and ecosystems in the eastern United States. [ABSTRACT FROM AUTHOR]

A correction to the article "Characterizing large-scale meteorological patterns and associated temperature and precipitation extremes over the northwestern United States using self-organizing maps," which appeared in a previous issue of the periodical, is presented.

Time series of historical annual average temperature, total precipitation, and extreme weather indices were constructed and analyzed for 103 (for temperature indices) and 115 (for precipitation indices) U.S. cities with climate records starting earlier than 1900. Mean rate of change and related 95% confidence bounds were calculated for each city using linear regression for the full periods of record. Box–Cox transformations of some time series of climate records were performed to address issues of non-normal distribution. Thirteen cities among the nine U.S. climate regions were selected and further evaluated with adequacy diagnoses and analyses for each month. The results show that many U.S. cities exhibit long-term historical increases in annual average temperature and precipitation, although there are spatial and temporal variations in the observed trends among the cities. Some cities in the Ohio Valley and Southeast regions exhibit decreasing or statistically nonsignificant increasing trends in temperatures. Many of the cities exhibiting statistically significant increases in precipitation are in the Northeast and Upper Midwest regions. The records for the cities are individually unique in both annual and monthly change, and cities within the same climate region sometimes exhibit substantially different changes. Within the full periods of record, discernible decade-long subtrends were observed for some cities; consequently, analysis of selected shorter periods can lead to inconclusive and biased results. These statistical analyses of constructed time series of city-specific long-term historical climate records provide detailed historical climate change information for cities across the United States. [ABSTRACT FROM AUTHOR]

The net surface water budget, precipitation minus evaporation (P = E), shows a clear seasonal cycle in the U.S. Southwest with a net gain of surface water (positive P = E) in the cold half of the year (October-March) and a net loss of water (negative P = E) in the warm half (April-September), with June and July being the driest months of the year. There is a significant shift of the summer drying toward earlier in the year under a CO2 warming scenario, resulting in substantial spring drying (March-May) of the U.S. Southwest from the near-term future to the end of the current century, with gradually increasing magnitude. While the spring drying has been identified in previous studies, its mechanism has not been fully addressed. Using moisture budget analysis, it was found that the drying is mainly due to decreased mean moisture convergence, partially compensated by the increase in transient eddy moisture flux convergence. The decreased mean moisture convergence is further separated into components as a result of changes in circulation (dynamic changes) and changes in atmospheric moisture content (thermodynamic changes). The drying is found to be dominated by the thermodynamic-driven changes in column-averaged moisture convergence, mainly due to increased dry zonal advection caused by the climatological land-ocean thermal contrast, rather than by the well-known "dry get drier" mechanism. Furthermore, the enhanced dry advection in the warming climate is dominated by the robust zonal mean atmospheric warming, leading to equally robust spring drying in the southwestern United States. [ABSTRACT FROM AUTHOR]

Wintertime extratropical cyclones in the east Pacific region are the source of much of the precipitation over California. There is a lot of uncertainty in future projections of Californian precipitation associated with predicted changes in the jet stream and the midlatitude storm tracks. The question this work seeks to answer is how the changes in the frequency and the intensity of extratropical cyclones in the Pacific storm track influence future changes in Californian precipitation. The authors used an objective cyclone identification method applied to 25 CMIP5 models for the historical and RCP8.5 simulations and investigated the changing relationships between storm frequency, intensity and precipitation. Cyclone data from the historical simulations and differences between the historical and RCP8.5 simulations were used to "predict" the modeled precipitation in the RCP8.5 simulations. In all models, the precipitation predicted using historical relationships gives a lower future precipitation change than the direct model output. In the future, the relationship between track density and precipitation indicates that for the same number of tracks, more precipitation is received. The relationship between track intensity and precipitation (which is quite weak in the historical simulations) does not change in the future. This suggests that other sources, likely enhanced moisture availability, are more important than changes in the intensity of cyclones for the rainfall associated with the storm tracks. [ABSTRACT FROM AUTHOR]

Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950-2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a 'base function' for evaluating a climate model's potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model's skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4's representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation. [ABSTRACT FROM AUTHOR]

This study documents and investigates biases in simulating summer surface air temperature (SAT) variability over the continental United States in the Atmospheric Model Intercomparison Project (AMIP) experiment from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Empirical orthogonal function (EOF) and multivariate regression analyses are used to assess the relative importance of circulation and the land surface feedback at setting summer SAT over a 30-yr period (1979-2008). Regions of high SAT variability are closely associated with midtropospheric highs, subsidence, and radiative heating accompanying clear-sky conditions. The land surface exerts a spatially variable influence on SAT through the sensible heat flux and is a second-order effect in the high-variability centers of action (COAs) in observational estimates. The majority of the AMIP models feature high SAT variability over the central United States, displaced south and/or west of observed COAs. SAT COAs in models tend to be concomitant and strongly coupled with regions of high sensible heat flux variability, suggesting that excessive land-atmosphere interaction in these models modulates U.S. summer SAT. In the central United States, models with climatological warm biases also feature less evapotranspiration than ERA-Interim but reasonably reproduce observed SAT variability in the region. Models that overestimate SAT variability tend to reproduce ERA-Interim SAT and evapotranspiration climatology. In light of potential model biases, this analysis calls for careful evaluation of the land-atmosphere interaction hot spot region identified in the central United States. Additionally, tropical sea surface temperatures play a role in forcing the leading EOF mode for summer SAT in models. This relationship is not apparent in observations. [ABSTRACT FROM AUTHOR]

Time series of U.S. daily heavy precipitation (95th percentile) are analyzed to determine factors responsible for regionality and seasonality in their 1979-2013 trends. For annual conditions, contiguous U.S. trends have been characterized by increases in precipitation associated with heavy daily events across the northern United States and decreases across the southern United States. Diagnosis of climate simulations (CCSM4 and CAM4) reveals that the evolution of observed sea surface temperatures (SSTs) was a more important factor influencing these trends than boundary condition changes linked to external radiative forcing alone. Since 1979, the latter induces widespread, but mostly weak, increases in precipitation associated with heavy daily events. The former induces a meridional pattern of northern U.S. increases and southern U.S. decreases as observed, the magnitude of which closely aligns with observed changes, especially over the south and far west. Analysis of model ensemble spread reveals that appreciable 35-yr trends in heavy daily precipitation can occur in the absence of forcing, thereby limiting detection of the weak anthropogenic influence at regional scales. Analysis of the seasonality in heavy daily precipitation trends supports physical arguments that their changes during 1979-2013 have been intimately linked to internal decadal ocean variability and less so to human-induced climate change. Most of the southern U.S. decrease has occurred during the cold season that has been dynamically driven by an atmospheric circulation reminiscent of teleconnections linked to cold tropical eastern Pacific SSTs. Most of the northeastern U.S. increase has been a warm season phenomenon, the immediate cause for which remains unresolved. [ABSTRACT FROM AUTHOR]

The proper understanding of precipitation variability, seasonality, and predictability are important for effective environmental management. Precipitation and its associated extremes vary in magnitude and duration both spatially and temporally, making it one of the most challenging climate parameters to predict on the basis of global and regional climate models. Using information theory, an improved understanding of precipitation predictability in the conterminous United States over the period of 1949-2010 is sought based on a gridded monthly precipitation dataset. Predictability is defined as the recurrent likelihood of patterns described by the metrics of magnitude variability and seasonality. It is shown that monthly mean precipitation and duration-based dry and wet extremes are generally highly variable in the east compared to those in the west, while the reversed spatial pattern is observed for intensity-based wetness indices except along the Pacific Northwest coast. It is thus inferred that, over much of the U.S. landscape, variations of monthly mean precipitation are driven by the variations in precipitation occurrences rather than the intensity of infrequent heavy rainfall. It is further demonstrated that precipitation seasonality for means and extremes is homogeneously invariant within the United States, with the exceptions of the West Coast, Florida, and parts of the Midwest, where stronger seasonality is identified. A proportionally higher role of variability in regulating precipitation predictability is demonstrated. Seasonality surpasses variability only in parts of the West Coast. The quantified patterns of predictability for precipitation means and extremes have direct applications to those phenomena influenced by climate periodicity, such as biodiversity and ecosystem management. [ABSTRACT FROM AUTHOR]

A global-scale decadal climate shift, beginning in 1998/99 and enduring through 2013, has been documented in recent studies, with associated precipitation shifts in key regions throughout the world. These precipitation shifts are most easily detected during March-May when ENSO effects are weak. Analyses have linked this climate shift to a shift in the Pacific decadal variability (PDV) pattern to its negative phase. Here the authors evaluate the predictive skill of the North American Multimodel Ensemble (NMME), and the CFSv2 model alone, in maintaining the observed precipitation shifts in seasonal forecasts, emphasizing the southwestern United States where deficient precipitation has tended to prevail since the late 1990s. The NMME hindcasts out to 6 months lead are found to maintain the observed decadal precipitation shifts in key locations qualitatively correctly, but with increasingly underestimated amplitude with increasing lead time. This finding holds in the separate CFSv2 model hindcasts. The decadal precipitation shift is relatively well reproduced in the southwestern United States. The general underestimation of the precipitation shift is suggested to be related to a muted reproduction of the observed shift in Pacific sea surface temperature (SST). This conclusion is supported by runs from a different (but overlapping) set of atmospheric models, which when forced with observed SST reproduce the decadal shifts quite well. Overall, the capability of the NMME model hindcasts to reflect the observed decadal rainfall pattern shift, but with weakened amplitude (especially at longer leads), underscores the broader challenge of retaining decadal signals in predictions of droughts and pluvials at seasonal-to-interannual time scales. [ABSTRACT FROM AUTHOR]

Idealized atmospheric general circulation model (AGCM) experiments by the U.S. Climate Variability and Predictability Program (CLIVAR) Drought Working Group were used in order to study the influence of natural modes of sea surface temperature (SST) variability in the Pacific on drought in the contiguous United States. The current study expands on previous results by examining the atmospheric response of three AGCMs to three different patterns of the idealized Pacific SST anomalies that operate on different time scales: low-frequency (decadal), high-frequency (interannual), and a pan-Pacific pattern that retains characteristics of interannual and decadal variability. While forcing patterns are generally similar in appearance, results indicate that differences in the relative amplitude of the equatorial and extratropical components of the SST forcing are sufficient to give rise to differing teleconnections, leading to regional differences in the amplitude and significance of the precipitation response. Results indicate that the differences in simulated drought response between AGCMs to different cool-phase (La Niña-like) SST patterns are determined by model sensitivity to changes in the relative amplitude of the equatorial and extratropical components of the SSTA forcing, the strength of the land-atmosphere coupling, and by the amplitude of internal atmospheric variability. Results indicate that the northwestern United States and Great Plains regions are particularly sensitive to the extratropical component of the SST forcing. Evidence is also found that when the cool-phase patterns of SST combine, as they have in recent years, constructive interference leads to an enhanced drought response over the Great Plains. [ABSTRACT FROM AUTHOR]

Variations in extreme daily temperatures are explored in relation to changes in seasonal mean temperature using 1218 high-quality U.S. temperature stations spanning 1900-2012. Extreme temperatures are amplified (or damped) by as much as ±50% relative to changes in average temperature, depending on region, season, and whether daily minimum or maximum temperature is analyzed. The majority of this regional structure in amplification is shown to follow from regional variations in temperature distributions. More specifically, there exists a close relationship between departures from normality and the degree to which extreme changes are amplified relative to the mean. To distinguish between intraseasonal and interannual contributions to nonnormality and amplification, an additional procedure, referred to as z bootstrapping, is introduced that controls for changes in the mean and variance between years. Application of z bootstrapping indicates that amplification of winter extreme variations is generally consistent with nonnormal intraseasonal variability. Summer variability, in contrast, shows interannual variations in the spread of the temperature distribution related to changes in the mean, especially in the Midwest. Changes in midwestern temperature variability are qualitatively consistent with those expected from decreases in evapotranspiration and are strongly correlated with a measure of drought intensity. The identified patterns of interannual variations in means and extremes may serve as an analog for modes of variability that can be expected at longer time scales. [ABSTRACT FROM AUTHOR]

The southwestern United States has experienced some of the most important increases in nighttime minimum temperatures over the last 60 yr, and climate models are projecting more of the same to the end of the century. As climate, geography, and population density vary considerably over the area, very diverse extreme temperature levels and dynamics are observed. It is shown how nighttime minimum temperatures over the 1950-2009 period exhibit more complex dynamics than daytime maximum temperatures. The author studies nighttime minimum temperature series from 12 locations and presents one model capable of capturing all the features of the data at each location. The time series preprocessing model normalizes seasonal shocks by daily and yearly volatility components before modeling the residual volatility as an exponential generalized autoregressive conditional heteroskedasticity [EGARCH(1, 1)] process with seasonal autoregressive structure to account for the presence of nonlinear and seasonal linear dependence, respectively, in the residual series. An exceedance over high thresholds approach is then used to model the tail of the distribution of scaled residuals from the preprocessing model. The resulting marginal distribution of nighttime minimum temperature at each location is then examined to see how it has changed in mean, scale, and shape, respectively, over the 60-yr period. Changes at the 12 locations vary considerably: many locations have seen considerable change in some or all of the three parameters, while two locations have experienced little or no change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cloud cover data from ground-based weather observers can be an important source of climate information, but the record of such observations in the United States is disrupted by the introduction of automated observing systems and other artificial shifts that interfere with our ability to assess changes in cloudiness at climate time scales. A new dataset using 54 National Weather Service (NWS) and 101 military stations that continued to make human-augmented cloud observations after the 1990s has been adjusted using statistical changepoint detection and visual scrutiny. The adjustments substantially reduce the trends in U.S. mean total cloud cover while increasing the agreement between the cloud cover time series and those of physically related climate variables. For 1949-2009, the adjusted time series give a trend in U.S. mean total cloud of 0.11% ± 0.22% decade−1 for the military data, 0.55% ± 0.24% decade−1 for the NWS data, and 0.31% ± 0.22% decade−1 for the combined dataset. These trends are less than one-half of those in the original data. For 1976-2004, the original data give a significant increase but the adjusted data show an insignificant trend from −0.17% decade−1 (military stations) to 0.66% decade−1 (NWS stations). Trends have notable regional variability, with the northwest United States showing declining total cloud cover for all time periods examined, while trends for most other regions are positive. Differences between trends in the adjusted datasets from military stations and NWS stations may be rooted in the difference in data source and reflect the uncertainties in the homogeneity adjustment process. [ABSTRACT FROM AUTHOR]

In previous work the authors demonstrated an empirical relation, in the form of an index, between U.S. monthly tornado activity and monthly averaged environmental parameters. Here a detailed comparison is made between the index and reported tornado activity. The index is a function of two environmental parameters taken from the North American Regional Reanalysis: convective precipitation (cPrcp) and storm relative helicity (SRH). Additional environmental parameters are considered for inclusion in the index, among them convective available potential energy, but their inclusion does not significantly improve the overall climatological performance of the index. The aggregate climatological dependence of reported monthly U.S. tornado numbers on cPrcp and SRH is well described by the index, although it fails to capture nonsupercell and cool season tornadoes. The contributions of the two environmental parameters to the index annual cycle and spatial distribution are examined with the seasonality of cPrcp (maximum during summer) relative to SRH (maximum in winter) accounting for the index peak value in May. The spatial distribution of SRH establishes the central U.S. 'tornado alley' of the index, while the spatial distribution of cPrcp enhances index values in the South and Southeast and suppresses them west of the Rockies and over elevation. At the scale of the NOAA climate regions, the largest deficiency of the index climatology occurs over the central region where the index peak in spring is too low and where the late summer drop-off in the reported number of tornadoes is poorly captured. This index deficiency is related to its sensitivity to SRH, and increasing the index sensitivity to SRH improves the representation of the annual cycle in this region. The ability of the index to represent the interannual variability of the monthly number of U.S. tornadoes can be ascribed during most times of the year to interannual variations of cPrcp rather than of SRH. However, both factors are important during the peak spring period. The index shows some skill in representing the interannual variability of monthly tornado numbers at the scale of NOAA climate regions. [ABSTRACT FROM AUTHOR]

The recent sequence of extreme hydrological events across the eastern United States (e.g., Hurricane Irene in August 2011, Tropical Storm Lee in September 2011, and Hurricane Sandy in October 2012), which led to unprecedented flooding including in various parts in the study region, the Catskill Mountains, and Hudson River Valley in southern New York State, have raised the question of whether the frequency of extreme events across the region is changing. In this study variations in the frequency of extreme precipitation and streamflow events available from historical records are analyzed. This study finds that there has been a marked increase in the frequency of warm season (June-October) extreme hydrologic events during the last two decades, with an accelerated rate of increase since the mid-1990s. The most recent decade has the highest frequency of extreme warm season events in the last 100 years across the study region. No such trend is observed between November and May; in fact the frequency of 4-day extreme precipitation events during the cold period has declined during the last two decades. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The spatial-intensity variability of extreme precipitation over the contiguous United States (CONUS) during boreal winter and relationships with the Madden-Julian oscillation (MJO) are investigated. Daily gridded precipitation is used to define two types of contiguous regions of extreme precipitation (CREPs): the intensity and spatial extent exceeding the 75th and 90th percentiles of frequency distributions. Extreme precipitation occurs twice as frequently when the MJO is active than inactive. Joint probabilities of the fractional area of CONUS sectors when the MJO is active are 2.0-2.5 higher than probabilities during inactive days for both 75th and 90th percentile CREPs (similar for the intensity of CREPs). Probabilities of the fractional area of 75th percentile CREPs when the MJO is active in neutral ENSO are higher than during warm or cold ENSO. Joint probabilities of the fractional area during MJO and warm ENSO are higher than MJO and cold ENSO and statistically significant over southern sectors. Results are similar for joint probabilities of intensity exceedance and MJO activity in warm and cold ENSO phases. Proportions of 75th and 90th percentile CREPs for each sector and phase of the MJO are predominantly large when MJO convective signals are over the central Indian Ocean or western Pacific. Probabilities of the fractional area of 90th percentile CREPs conditioned on MJO phases, however, do not show clear predominance. This indicates that the MJO is not the sole player in the occurrences of CREPs. Last, this study concludes that probabilities of the fractional area and intensity of 75th and 90th percentile CREPs in the CONUS do not depend on the amplitude of the MJO. [ABSTRACT FROM AUTHOR]

Predicting surface air temperature ( T) is a major task of North American (NA) winter seasonal prediction. It has been recognized that variations of the NA winter T''s can be associated with El Niñño--Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). This study presents observed evidence that variability in snow cover over the Tibetan Plateau (TP) and its adjacent areas in prior autumn (September--November) is significantly correlated with the first principal component (PC1) of the NA winter T''s, which features a meridional seesaw pattern over the NA continent. The autumn TP snow cover anomaly can persist into the following winter through a positive feedback between snow cover and the atmosphere. A positive TP snow cover anomaly may induce a negative sea level pressure and geopotential height anomaly over the eastern North Pacific, a positive geopotential height anomaly over Canada, and a negative anomaly over the southeastern United States--a structure very similar to the positive phase of the Pacific--North America (PNA) pattern. This pattern usually favors the occurrence of a warm--north, cold--south winter over the NA continent. When a negative snow cover anomaly occurs, the situation tends to be opposite. Since the autumn TP snow cover shows a weak correlation with ENSO, it provides a new predictability source for NA winter T''s. Based on the above results, an empirical model is constructed to predict PC1 using a combination of autumn TP snow cover and other sea surface temperature anomalies related to ENSO and the NAO. Hindcasts and real forecasts are performed for the 1972--2003 and 2004--09 periods, respectively. Both show a promising prediction skill. As far as PC1 is concerned, the empirical model hindcast performs better than the ensemble mean of four dynamical models from the Canadian Meteorological Centre. Particularly, the real forecast of the empirical model exhibits a better performance in predicting the extreme phases of PC1--that is, the extremely warm winter over Canada in 2009/10--should the model include the autumn TP snow cover impacts. Since all these predictors can be readily monitored in real time, this empirical model provides a real-time forecast tool for NA winter climate. [ABSTRACT FROM AUTHOR]

The present work assesses spring and summer precipitation over North America as well as summer precipitation variability over the central United States and its SST links in simulations of the twentieth-century climate and projections of the twenty-first- and twenty-second-century climates for the A1B scenario. The observed spatial structure of spring and summer precipitation poses a challenge for models, particularly over the western and central United States. Tendencies in spring precipitation in the twenty-first century agree with the observed ones at the end of the twentieth century over a wetter north-central and a drier southwestern United States, and a drier southeastern Mexico. Projected wetter springs over the Great Plains in the twenty-first and twenty-second centuries are associated with an increase in the number of extreme springs. In contrast, projected summer tendencies have demonstrated little consistency. The associated observed changes in SSTs bear the global warming footprint, which is not well captured in the twentieth-century climate simulations. Precipitation variability over the Great Plains presents a coherent picture in spring but not in summer. Models project an increase in springtime precipitation variability owing to an increased number of extreme springs. The number of extreme droughty (pluvial) events during the spring--fall part of the year is under(over)estimated in the twentieth century without consistent projections. Summer precipitation variability over the Great Plains is linked to SSTs over the Pacific and Atlantic Oceans, with no apparent ENSO link in spite of the exaggerated variability in the equatorial Pacific in climate simulations; this has been identified already in observations and atmospheric models forced with historical SSTs. This link is concealed due to the increased warming in the twenty-first century. Deficiencies in land surface--atmosphere interactions and global teleconnections in the climate models prevent them from a better portrayal of summer precipitation variability in the central United States. [ABSTRACT FROM AUTHOR]

Coupled global circulation models (CGCMs) have been widely used to explore potential future climate change. Before these climate projections can be trusted, the ability of the models to simulate present-day climate must be assessed. This study evaluates the ability of three CGCMs that participated in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change to simulate long-term drought over the Great Plains region with the same frequency and intensity as was observed during the twentieth century. The three models evaluated in this study are the Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.0 (CM2.0); the National Centers for Atmospheric Research Community Climate System Model, version 3 (CCSM3); and third climate configuration of the Met Office Unified Model (HadCM3). The models are shown to capture the broad features of the climatology of the Great Plains, with maximum precipitation occurring in early summer, as observed. However, all of the models overestimate annual precipitation rates. Also, in CCSM3, precipitation and evapotranspiration experience unrealistic decreases between the months of June and August. Long-term droughts are found in each simulation of the twentieth century that are comparable in duration, severity, and spatial extent as has been observed. However, the processes found to be associated with simulated long-term droughts vary among the models. In both CM2.0 and HadCM3, low-frequency variations in Great Plains precipitation are found to correspond with low-frequency variations in tropical Pacific SSTs. In CCSM3, on the other hand, there appears to be no significant correlation between tropical Pacific SST variability and Great Plains precipitation. Strong land–atmosphere coupling in CCSM3 may explain the persistence of long-term droughts in this model. [ABSTRACT FROM AUTHOR]

The lake level elevation of the Great Salt Lake (GSL), a large closed basin lake in the arid western United States, is characterized by a pronounced quasi-decadal oscillation (QDO). The variation of the GSL elevation is very coherent with the QDO of sea surface temperature anomalies in the tropical central Pacific (also known as the Pacific QDO). However, such coherence denies any direct association between the precipitation in the GSL watershed and the Pacific QDO because, in a given frequency, the precipitation variation always leads the GSL elevation variation. Therefore, the precipitation variation is phase shifted from the Pacific QDO. This study investigates the physical mechanism forming the coherence between the GSL elevation and the Pacific QDO. Pronounced and coherent quasi-decadal signals in precipitation, streamflow, water vapor flux, and drought conditions are found throughout the Great Basin. Recurrent atmospheric circulation patterns develop over the Gulf of Alaska during the warm-to-cool and cool-to-warm transition phases of the Pacific QDO. These circulation patterns modulate the water vapor flux associated with synoptic transient activities over the western United States and, in turn, lead to the QDO in the hydrological cycle of the Great Basin. As the GSL integrates the hydrological responses in the Great Basin, the hydrological QDO is then transferred to the GSL elevation. Because the GSL elevation consistently lags the precipitation by a quarter-phase (about 3 yr in the quasi-decadal time scale), these processes take an average of 6 yr for the GSL elevation to eventually respond to the Pacific QDO. This creates a half-phase delay of the GSL elevation from the Pacific QDO, thereby forming the inverse, yet coherent, relationship between them. Tree-ring reconstructed precipitation records confirm that the quasi-decadal signal in precipitation is a prominent feature in this region. [ABSTRACT FROM AUTHOR]