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In a recent article, Li et al. examined changes in the summer-season location of the western ridge of the North Atlantic subtropical high from 1948 to 2007 because there has been an increase in interannual summer rainfall variability in the southeastern United States. The following major conclusions by Li et al. are incorrect: the western ridge has undergone a significant westward trend since the late 1970s; the western ridge had increased meridional movement during 1978-2007 compared to 1948-1977; and global warming appears to be contributing to the westward expansion of the western ridge. Results presented in this paper reveal that the western ridge has been moving eastward over the past three decades, there was no change in latitudinal variance, and a westward movement of the western ridge should not be linked to global warming. [ABSTRACT FROM AUTHOR]

In a changing climate, there is an interest in predicting how extreme rainfall events may change. Using historical records, several recent papers have evaluated whether high-intensity precipitation scales with temperature in accordance with the Clausius--Clapeyron (C--C) relationship. For varying locations in Europe, these papers have identified both super C--C relationships as well as a breakdown of the C--C relationship under dry conditions. In this paper, a similar analysis is carried out for the United States using data from 14 weather stations clustered in four different hydroclimatic regions: the coastal northeast, interior New York, the central plains, and the western plains. In all regions except interior New York state, 99th percentile 1-h precipitation generally followed the C--C relation. In interior New York, there was evidence that intensity scaled with a super C--C relationship. For the 99.9th percentile precipitation, interior New York displayed some moderate evidence of a super C--C relationship, the western plains showed little relation between precipitation and temperature, and the remainder of sites generally scaled with the C--C relationship. Also, if only July, August, and September precipitation is considered, all stations except those in interior New York have little relation between temperature and precipitation, suggesting that precipitation intensity during summer months may not be well constrained by the C--C relationship. Overall, the C--C relationship (or a variation thereof) does not appear to constrain extreme precipitation in all regions and in all seasons, and its ability to aid in constraining future predictions of extreme precipitation may only be relevant to certain locales and time periods. [ABSTRACT FROM AUTHOR]

The northeastern United States (NEUS) is a densely populated region with a number of major cities along the climatological storm track. Despite its economic and social importance, as well as the area's vulnerability to flooding, there is significant uncertainty around future trends in extreme precipitation over the region. Here, we undertake a regional study of the projected changes in extreme precipitation over the NEUS through the end of the twenty-first century using an ensemble of high-resolution, dynamically downscaled simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) project. We find that extreme precipitation increases throughout the region, with the largest changes in coastal regions and smaller changes inland. These increases are seen throughout the year, although the smallest changes in extreme precipitation are seen in the summer, in contrast to earlier studies. The frequency of heavy precipitation also increases such that there are relatively fewer days with moderate precipitation and relatively more days with either no or strong precipitation. Averaged over the region, extreme precipitation increases by +3%–5% °C−1 of local warming, with the largest fractional increases in southern and inland regions and occurring during the winter and spring seasons. This is lower than the +7% °C−1 rate expected from thermodynamic considerations alone and suggests that dynamical changes damp the increases in extreme precipitation. These changes are qualitatively robust across ensemble members, although there is notable intermodel spread associated with models' climate sensitivity and with changes in mean precipitation. Together, the NA-CORDEX simulations suggest that this densely populated region may require significant adaptation strategies to cope with the increase in extreme precipitation expected at the end of the next century. Significance Statement: Observations show that the northeastern United States has already experienced increases in extreme precipitation, and prior modeling studies suggest that this trend is expected to continue through the end of the century. Using high-resolution climate model simulations, we find that coastal regions will experience large increases in extreme precipitation (+6.0–7.5 mm day−1), although there is significant intermodel spread in the trends' spatial distribution and in their seasonality. Regionally averaged, extreme precipitation will increase at a rate of ∼2% decade−1. Our results also suggest that the frequency of extreme precipitation will increase, with the strongest storms doubling in frequency per degree of warming. These results, taken with earlier studies, provide guidance to aid in resiliency preparation and planning by regional stakeholders. [ABSTRACT FROM AUTHOR]

The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters. SIGNIFICANCE STATEMENT: We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns}a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought. [ABSTRACT FROM AUTHOR]

Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological datasets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded datasets over the conterminous United States. Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5–2.5 day increase in mean wet spell length, 30% low bias in 20-yr return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation dataset is developed that omits the time adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new dataset is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate change applications across the CONUS. [ABSTRACT FROM AUTHOR]

Observed United States trends in the annual maximum 1-day precipitation (RX1day) over the last century consist of 15%–25% increases over the eastern United States (East) and 10% decreases over the far western United States (West). This heterogeneous trend pattern departs from comparatively uniform observed increases in precipitable water over the contiguous United States. Here we use an event attribution framework involving parallel sets of global atmospheric model experiments with and without climate change drivers to explain this spatially diverse pattern of extreme daily precipitation trends. We find that RX1day events in our model ensembles respond to observed historical climate change forcing differently across the United States with 5%–10% intensity increases over the East but no appreciable change over the West. This spatially diverse forced signal is broadly similar among three models used, and is positively correlated with the observed trend pattern. Our analysis of model and observations indicates the lack of appreciable RX1day signals over the West is likely due to dynamical effects of climate change forcing—via a wintertime atmospheric circulation anomaly that suppresses vertical motion over the West—largely cancelling thermodynamic effects of increased water vapor availability. The large magnitude of eastern U.S. RX1day increases is unlikely a symptom of a regional heightened sensitivity to climate change forcing. Instead, our ensemble simulations reveal considerable variability in RX1day trend magnitudes arising from internal atmospheric processes alone, and we argue that the remarkable observed increases over the East has most likely resulted from a superposition of strong internal variability with a moderate climate change signal. Implications for future changes in U.S. extreme daily precipitation are discussed. [ABSTRACT FROM AUTHOR]

The Mississippi River basin (MRB) is a flash flood hotspot receiving the most frequent flash floods and highest average rainfall accumulation of any region in the United States. Given the destruction flash floods cause in the current climate in the MRB, it is critical to understand how they will change in a future, warmer climate in order to prepare for these impacts. Recent work utilizing convection-permitting climate simulations to analyze future precipitation changes in flash flood--producing storms in the United States shows that the MRB experiences the greatest future increase in flash flood rainfall. This result motivates the goal of the present study to better understand the changes to precipitation characteristics and vertical velocity in flash flood--producing storms in the MRB. Specifically, the variations in flash flood--producing storm characteristics related to changes in vertical velocity in the MRB are examined by identifying 484 historical flash flood--producing storms from 2002 and 2013 and studying how they change in a future climate using 4-km convection-permitting simulations under a pseudo--global warming framework. In a future climate, precipitation and runoff increase by 17% and 32%, respectively, in flash flood--producing storms in the MRB. While rainfall increases in all flash flood--producing storms due to similar increases in moisture, it increases the most in storms with the strongest vertical velocity, suggesting that storm dynamics might modulate future changes in rainfall. These results are necessary to predict and prepare for the multifaceted impacts of climate change on flash flood--producing storms in order to create more resilient communities. [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [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]

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%-7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%-2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics-including starting location, intensity, spatial extent, duration, and trajectory-that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via 'data-driven simulations' that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) have, in recent years, been recognized as the cause of the large majority of major floods in rivers all along the U.S. West Coast and as the source of 30%-50% of all precipitation in the same region. The present study surveys the frequency with which ARs have played a critical role as a common cause of the end of droughts on the West Coast. This question was based on the observation that, in most cases, droughts end abruptly as a result of the arrival of an especially wet month or, more exactly, a few very large storms. This observation is documented using both Palmer Drought Severity Index and 6-month Standardized Precipitation Index measures of drought occurrence for climate divisions across the conterminous United States from 1895 to 2010. When the individual storm sequences that contributed most to the wet months that broke historical West Coast droughts from 1950 to 2010 were evaluated, 33%-74% of droughts were broken by the arrival of landfalling AR storms. In the Pacific Northwest, 60%-74% of all persistent drought endings have been brought about by the arrival of AR storms. In California, about 33%-40% of all persistent drought endings have been brought about by landfalling AR storms, with more localized low pressure systems responsible for many of the remaining drought breaks. [ABSTRACT FROM AUTHOR]

This study uses an empirical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation projections over the state of Pennsylvania in the mid-Atlantic region of the United States for the future period 2046-65. To examine the sensitivity of precipitation change to the water vapor increase brought by global warming, the authors test the following two approaches to downscaling: one uses the specific humidity in the downscaling algorithm and the other does not. Application of the downscaling procedure to the general circulation model (GCM) projections reveals changes in the relative occupancy, but not the fundamental nature, of the simulated synoptic circulation states. Both downscaling approaches predict increases in annual and winter precipitation, consistent in sign with the 'raw' output from the GCMs but considerably smaller in magnitude. For summer precipitation, larger discrepancies are seen between raw and downscaled GCM projections, with a substantial dependence on the downscaling version used (downscaled precipitation changes employing specific humidity are smaller than those without it). Application of downscaling generally reduces the inter-GCM uncertainties, suggesting that some of the spread among models in the raw projected precipitation may result from differences in precipitation parameterization schemes rather than fundamentally different climate responses. Projected changes in the North Atlantic Oscillation (NAO) are found to be significantly related to changes in winter precipitation in the downscaled results, but not for the raw GCM results, suggesting that the downscaling more effectively captures the influence of climate dynamics on projected changes in winter precipitation. [ABSTRACT FROM AUTHOR]

In examining intense precipitation over the central United States, the authors consider only days with precipitation when the daily total is above 12.7 mm and focus only on these days and multiday events constructed from such consecutive precipitation days. Analyses show that over the central United States, a statistically significant redistribution in the spectra of intense precipitation days/events during the past decades has occurred. Moderately heavy precipitation events (within a 12.7-25.4 mm day−1 range) became less frequent compared to days and events with precipitation totals above 25.4 mm. During the past 31 yr (compared to the 1948-78 period), significant increases occurred in the frequency of 'very heavy' (the daily rain events above 76.2 mm) and extreme precipitation events (defined as daily and multiday rain events with totals above 154.9 mm or 6 in.), with up to 40%% increases in the frequency of days and multiday extreme rain events. Tropical cyclones associated with extreme precipitation do not significantly contribute to the changes reported in this study. With time, the internal precipitation structure (e.g., mean and maximum hourly precipitation rates within each preselected range of daily or multiday event totals) did not noticeably change. Several possible causes of observed changes in intense precipitation over the central United States are discussed and/or tested. [ABSTRACT FROM AUTHOR]

This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce high-resolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of interest. First, the SOM was trained using seven coarsely resolved atmospheric variables from the National Centers for Environmental Prediction (NCEP) reanalysis dataset to model observed daily precipitation data from 17 stations across Pennsylvania for the period 1979-2005. Employing the same coarsely resolved variables from nine general circulation model (GCM) simulations taken from the historical analysis of the Coupled Model Intercomparison Project, phase 3 (CMIP3), the trained SOM was subsequently applied to simulate daily precipitation at the same 17 sites for the period 1961-2000. The SOM analysis indicates that the nine model simulations exhibit similar synoptic-scale features to the (NCEP) observations over the 1979-2007 training interval. An analysis of the sea level pressure signatures and the precipitation distribution corresponding to the trained SOM shows that it is effective in differentiating characteristic synoptic circulation patterns and associated precipitation. The downscaling approach provides a faithful reproduction of the observed probability distributions and temporal characteristics of precipitation on both daily and monthly time scales. The downscaled precipitation field shows significant improvement over the raw GCM precipitation fields with regard to observed average monthly precipitation amounts, average monthly number of rainy days, and standard deviations of monthly precipitation amounts, although certain caveats are noted. [ABSTRACT FROM AUTHOR]

The probability of climate extremes is strongly affected by atmospheric circulation. This study quantifies the worldwide influence of three major modes of circulation on station-based indices of intense precipitation: the El Niño--Southern Oscillation, the Pacific interdecadal variability as characterized by the North Pacific index (NPI), and the North Atlantic Oscillation--Northern Annular Mode. The study examines which stations show a statistically significant (5%%) difference between the positive and negative phases of a circulation regime. Results show distinct regional patterns of response to all these modes of climate variability; however, precipitation extremes are most substantially affected by the El Niño--Southern Oscillation. The effects of the El Niño--Southern Oscillation are seen throughout the world, including in India, Africa, South America, the Pacific Rim, North America, and, weakly, Europe. The North Atlantic Oscillation has a strong, continent-wide effect on Eurasia and affects a small, but not negligible, percentage of stations across the Northern Hemispheric midlatitudes. This percentage increases slightly if the Northern Annular Mode index is used rather than the NAO index. In that case, a region of increase in intense precipitation can also be found in Southeast Asia. The NPI influence on precipitation extremes is similar to the response to El Niño, and strongest in landmasses adjacent to the Pacific. Consistently, indices of more rare precipitation events show a weaker response to circulation than indices of moderate extremes; the results are quite similar, but of opposite sign, for negative anomalies of the circulation indices. [ABSTRACT FROM AUTHOR]

This study investigates a decadal climate shift over East Asia in winter, focusing on the changes in hydrological cycle as well as large-scale circulation using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). The RSM is forced by perfect boundary conditions for winter (December-February) from 1979 to 2007. Analyses for two separate periods (1979-87 and 1999-2007) are performed to investigate the regional climate model's ability to simulate climate change in precipitation as well as large-scale circulation. The RSM reproduces differences in large-scale features associated with winter climate change over East Asia when the winter monsoon is modulated on decadal time scales with its weakening pattern observed since the late 1980s. The model adequately reproduces a weakening of the Siberian high and shallowness of the Aleutian low in the lower troposphere and a weakened East Asian coastal trough and East Asian jet in the upper troposphere during 1999-2007, as compared to the first nine winters of 1979-87. Conversely, the decadal shift in precipitation is not well reproduced by the model. The model is capable of reproducing the power spectrum of daily precipitation with maxima at 8.5 days and 45 days in 1979-87, whereas widely spread peaks in 1999-2007 are not captured. The increase of precipitation due to parameterized convection is prominent. This study shows that the dynamical numerical model has a limited capability to reproduce the wintertime hydrological climate over East Asia associated with global warming in recent years. [ABSTRACT FROM AUTHOR]

We performed a meta-data analysis to investigate the importance of event based fluxes to DOC export from forested watersheds. A total of 30 small eastern United States forested watersheds with no wetland component, with a total of 5,176 DOC and accompanying discharge measurements were used in this analysis. There is a clear increase in DOC concentration during hydrologic events (storms and snow melt) that follows a power relationship. We estimate that 86% of DOC is exported during events. The majority (70%) of this event based DOC flux occurs during the rising hydrograph and during large events. Events with a discharge greater than 1.38 cm day make up only 4.8% of the annual hydrograph, yet are responsible for 57% of annual DOC flux. The relationship between event discharge and both DOC concentration and flux is also regulated by temperature and antecedent conditions, with a larger response in both fluxes and concentrations to events during warmer periods and periods where the preceding discharge was low. The temperature relationship also shows seasonality indicating a potential link to the size or reactivity of watershed OM pools. The 86% of DOC lost during events represents a conservative estimate of the amount of allochthonous forested DOC transported laterally to streams. Future research on watershed cycling of DOC should take into account the importance of events in regulating the transport of DOC to downstream ecosystems, determine the relative importance of abiotic versus biotic processes for the temperature regulation of event-associated DOC fluxes, and elucidate the interactions between processes that respond to climate on event versus longer time scales. [ABSTRACT FROM AUTHOR]

Data from observations and the Intergovernmental Panel on Climate Change (IPCC) twentieth-century climate change model [phase 3 of the Coupled Model Intercomparison Project (CMIP3)] simulations were analyzed to examine the decadal changes of the impact of ENSO on air temperature Tair and precipitation P over the United States. The comparison of composites for the early period (1915–60) and the recent period (1962–2006) indicates that cooling (warming) over the south and warming (cooling) over the north during ENSO warm (cold) winters have been weakening. The ENSO influence on winter P over the Southwest is strengthening, while the impact on P over the Ohio Valley is weakening for the recent decades. These differences are not due to the long-term trends in Tair or P; they are attributed to the occurrence of the central Pacific (CPAC) ENSO events in the recent years. The CPAC ENSO differs from the canonical eastern Pacific (EPAC) ENSO. The EPAC ENSO has a sea surface temperature anomaly (SSTA) maximum in the eastern Pacific. Enhanced convection extends from the date line to the eastern Pacific, with negative anomalies in the western Pacific. The atmospheric responses resemble a tropical Northern Hemisphere pattern. The wave train is consistent with the north–south Tair contrast over North America during the EPAC ENSO winters. The CPAC ENSO has enhanced convection in the central Pacific. The atmospheric responses show a Pacific–North American pattern. It is consistent with west–east contrast in Tair and more rainfall over the Southwest during the CPAC ENSO winters. [ABSTRACT FROM AUTHOR]

Multicentury preindustrial control simulations from six of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) models are used to examine the relationship between low-frequency precipitation variations in the Great Plains (GP) region of the United States and global sea surface temperatures (SSTs). This study builds on previous work performed with atmospheric models forced by observed SSTs during the twentieth century and extends it to a coupled model context and longer time series. The climate models used in this study reproduce the precipitation climatology over the United States reasonably well, with maximum precipitation occurring in early summer, as observed. The modeled precipitation time series exhibit negative “decadal” anomalies, identified using a 5-yr running mean, of amplitude comparable to that of the twentieth-century droughts. It is found that low-frequency anomalies over the GP are part of a large-scale pattern of precipitation variations, characterized by anomalies of the same sign as in the GP region over Europe and southern South America and anomalies of opposite sign over northern South America, India, and Australia. The large-scale pattern of the precipitation anomalies is associated with global-scale atmospheric circulation changes; during wet periods in the GP, geopotential heights are raised in the tropics and high latitudes and lowered in the midlatitudes in most models, with the midlatitude jets displaced toward the equator in both hemispheres. Statistically significant correlations are found between the decadal precipitation anomalies in the GP region and tropical Pacific SSTs in all the models. The influence of other oceans (Indian and tropical and North Atlantic), which previous studies have identified as potentially important, appears to be model dependent. [ABSTRACT FROM AUTHOR]

The present study assesses the potential of the U.S. Climate Variability and Predictability (CLIVAR) Drought Working Group (DWG) models in simulating interannual precipitation variability over North America, especially the Great Plains. It also provides targets for the idealized DWG model experiments investigating drought origin. The century-long Atmospheric Model Intercomparison Project (AMIP) simulations produced by version 3.5 of NCAR’s Community Atmosphere Model (CAM3.5), the Lamont-Doherty Earth Observatory’s Community Climate Model (CCM3), and NASA’s Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric models are analyzed; CCM3 and NSIPP-1 models have 16- and 14-ensemble simulations, respectively, while CAM3.5 only has 1. The standard deviation of summer precipitation is different in AMIP simulations. The maximum over the central United States seen in observations is placed farther to the west in simulations. Over the central plains the models exhibit modest skill in simulating low-frequency precipitation variability, a Palmer drought severity index proxy. The presence of a linear trend increases correlations in the period 1950–99 when compared with those for the whole century. The SST links of the Great Plains drought index have features in common with observations over both the Pacific and Atlantic Oceans. Interestingly, summer-to-fall precipitation regressions of the warm Trend, cold Pacific, and warm Atlantic modes of annual mean SST variability (used in forcing the DWG idealized model experiments) tend to dry the southwestern, midwestern, and southeastern regions of the United States in the observations and, to a lesser extent, in the simulations. The similarity of the idealized SST-forced droughts in DWG modeling experiments with AMIP precipitation regressions of the corresponding SST principal components, evident especially in the case of the cold Pacific pattern, suggests that the routinely conducted AMIP simulations could have served as an effective proxy for the more elaborated suite of DWG modeling experiments. [ABSTRACT FROM AUTHOR]

Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble. [ABSTRACT FROM AUTHOR]