Search

Multiscale valid time shifting (VTS) was explored for a real-time convection-allowing ensemble (CAE) data assimilation (DA) system featuring hourly assimilation of conventional in situ and radar reflectivity observations, developed by the Multiscale Data Assimilation and Predictability Laboratory. VTS triples the base ensemble size using two subensembles containing member forecast output before and after the analysis time. Three configurations were tested with 108-member VTS-expanded ensembles: VTS for individual mesoscale conventional DA (ConVTS) or storm-scale radar DA (RadVTS) and VTS integrated to both DA components (BothVTS). Systematic verification demonstrated that BothVTS matched the DA spread and accuracy of the best-performing individual component VTS. The 10-member forecasts showed BothVTS performs similarly to ConVTS, with RadVTS having better skill in 1-h precipitation at forecast hours 1–6, while Both/ConVTS had better skill at later hours 7–15. An objective splitting of cases by 2-m temperature cold bias revealed RadVTS was more skillful than Both/ConVTS out to hour 10 for cold-biased cases, while BothVTS performed best at most hours for less-biased cases. A sensitivity experiment demonstrated improved performance of BothVTS when reducing the underlying model cold bias. Diagnostics revealed enhanced spurious convection of BothVTS for cold-biased cases was tied to larger analysis increments in temperature than moisture, resulting in erroneously high convective instability. This study is the first to examine the benefits of a multiscale VTS implementation, showing that BothVTS can be utilized to improve the overall performance of a multiscale CAE system. Further, these results underscore the need to limit biases within a DA and forecast system to best take advantage of VTS analysis benefits. [ABSTRACT FROM AUTHOR]

Post-wildfire debris flows (PFDF) threaten life and property in western North America. They are triggered by short-duration, high-intensity rainfall. Following a wildfire, rainfall thresholds are developed that, if exceeded, indicate high likelihood of a PFDF. Existing weather forecast products allow forecasters to identify favorable atmospheric conditions for rainfall intensities that may exceed established thresholds at lead times needed for decision-making (e.g., =24 h). However, at these lead times, considerable uncertainty exists regarding rainfall intensity and whether the high-intensity rainfall will intersect the burn area. The approach of messaging on potential hazards given favorable conditions is generally effective in avoiding unanticipated PFDF impacts, but may lead to "messaging fatigue" if favorable triggering conditions are forecast numerous times, yet no PFDF occurs (i.e., false alarm). Forecasters and emergency managers need additional tools that increase their confidence regarding occurrence of short-duration, high-intensity rainfall as well as tools that tie rainfall forecasts to potential PFDF outcomes. We present a concept for probabilistic tools that evaluate PFDF hazards by coupling a high-resolution (1-km), large (100-member) ensemble 24-h precipitation forecast at 5-min resolution with PFDF likelihood and volume models. The observed 15-min maximum rainfall intensities are captured within the ensemble spread, though in highest ~10% of members. We visualize the model output in several ways to demonstrate most likely and most extreme outcomes and to characterize uncertainty. Our experiment highlights the benefits and limitations of this approach, and provides an initial step toward further developing situational awareness and impact-based decision-support tools for forecasting PFDF hazards. [ABSTRACT FROM AUTHOR]

This study employs a long time series (1997-2017) of reforecasts based on a version of the ECMWF Integrated Forecast System to evaluate the dependence of medium-range (i.e., 3-15 days) precipitation forecast skill over California on the state of the large-scale atmospheric flow. As a basis for this evaluation, four recurrent large-scale flow regimes over the North Pacific and western North America associated with precipitation in a domain encompassing northern and central California were objectively identified in ECMWF ERA5 reanalysis data for November-March 1981-2017. Two of the regimes are characterized by zonal upper-level flow across the North Pacific, and the other two are characterized by wavy, blocked flow. Forecast verification statistics conditioned on regime occurrence indicate considerably lower medium-range precipitation skill over California in blocking regimes than in zonal regimes. Moreover, forecasts of blocking regimes tend to exhibit larger errors and uncertainty in the synoptic-scale flow over the eastern North Pacific and western North America compared with forecasts of zonal regimes. Composite analyses for blocking forecasts reveal a tendency for errors to develop in conjunction with the amplification of a ridge over the western and central North Pacific. The errors in the ridge tend to be communicated through the large-scale Rossby wave pattern, resulting in misforecasting of downstream trough amplification and, thereby, moisture flux and precipitation over California. The composites additionally indicate that error growth in the blocking ridge can be linked to misrepresentation of baroclinic development as well as upper-level divergent outflow associated with latent heat release. [ABSTRACT FROM AUTHOR]

The skill of NOAA's official monthly U.S. precipitation forecasts (issued in the middle of the prior month) has historically been low, having shown modest skill over the southern United States, but little or no skill over large portions of the central United States. The goal of this study is to explain the seasonal and regional variations of the North American subseasonal (weeks 3–6) precipitation skill, specifically the reasons for its successes and its limitations. The performances of multiple recent-generation model reforecasts over 1999–2015 in predicting precipitation are compared to uninitialized simulation skill using the atmospheric component of the forecast systems. This parallel analysis permits attribution of precipitation skill to two distinct sources: one due to slowly evolving ocean surface boundary states and the other to faster time-scale initial atmospheric weather states. A strong regionality and seasonality in precipitation forecast performance is shown to be analogous to skill patterns dictated by boundary forcing constraints alone. The correspondence is found to be especially high for the North American pattern of the maximum monthly skill that is achieved in the reforecast. The boundary forcing of most importance originates from tropical Pacific SST influences, especially those related to El Niño–Southern Oscillation. We discuss physical constraints that may limit monthly precipitation skill and interpret the performance of existing models in the context of plausible upper limits. Significance Statement: Skillful subseasonal precipitation predictions have societal benefits. Over the United States, however, NOAA's official U.S. monthly precipitation forecast skill has been historically low. Here we explore origins for skill of North American week-3 to week-6 precipitation predictions. Skill arising from initial weather states is compared to that arising from ocean surface boundary states alone. The monthly and seasonally varying pattern of U.S. monthly precipitation skill is appreciably derived from boundary constraints, linked especially with El Niño–Southern Oscillation. Forecasts of opportunity are identified, despite the low skill of monthly precipitation forecasts on average. Potential limits of monthly precipitation skill are explored that provide insight on the juxtaposition of "skill deserts" over the central United States with high skill regions over western North America. [ABSTRACT FROM AUTHOR]

Cool season (September–May) extreme temperature and precipitation events are frequently tied to surface cyclones and anticyclones, which are modulated on the synoptic scale by the state and evolution of the upper-tropospheric jet stream. This study adopts a jet-centered approach to classify the prevailing large-scale flow pattern into jet regimes based on the leading modes of variability of the North Pacific jet (NPJ) and the North Atlantic jet (NAJ), respectively. The characteristics of these joint NPJ–NAJ regimes are subsequently examined using composite analysis to identify large-scale flow environments conducive to the occurrence of anomalous temperatures and precipitation across North America. The analysis reveals that composite large-scale flow environments associated with each joint NPJ–NAJ regime can be approximated as a linear combination of the separate large-scale environments that characterize each NPJ regime and NAJ regime independently. Furthermore, knowledge of the joint NPJ–NAJ regime provides more precision regarding the relative likelihood and spatial coverage of anomalous temperatures and precipitation than would be obtained from consideration of the NPJ or NAJ regime in isolation. The frequencies of each joint NPJ–NAJ regime can also be modulated by the occurrence of sudden stratospheric warmings, with increased frequencies of an equatorward-shifted NAJ and a retracted NPJ during the 30-day period following a warming event. The results from the present study demonstrate that knowledge of the joint NPJ–NAJ regime exhibits the potential to inform forecasts of anomalous temperatures and precipitation at medium-range and subseasonal time scales. Significance Statement: The development of cool season temperature and precipitation extremes are modulated by the state and evolution of the upper-tropospheric jet stream. Therefore, this study adopts a jet-centered approach to quantify the extent to which temperature and precipitation extremes over North America are related to the coevolution of the North Pacific and North Atlantic segments of the jet stream. The analysis demonstrates that the relative likelihood and spatial coverage of temperature and precipitation extremes varies significantly across North America based on the combined state of the North Pacific and North Atlantic jets. The jet-centered approach utilized in this study exhibits the potential to inform operational medium-range (6–10 day) and subseasonal forecasts of temperature and precipitation across North America. [ABSTRACT FROM AUTHOR]

The Global Ensemble Forecast System (GEFS) is upgraded to version 12, in which the legacy Global Spectral Model (GSM) is replaced by a model with a new dynamical core—the Finite Volume Cubed-Sphere Dynamical Core (FV3). Extensive tests were performed to determine the optimal model and ensemble configuration. The new GEFS has cubed-sphere grids with a horizontal resolution of about 25 km and an increased ensemble size from 20 to 30. It extends the forecast length from 16 to 35 days to support subseasonal forecasts. The stochastic total tendency perturbation (STTP) scheme is replaced by two model uncertainty schemes: the stochastically perturbed physics tendencies (SPPT) scheme and stochastic kinetic energy backscatter (SKEB) scheme. Forecast verification is performed on a period of more than two years of retrospective runs. The results show that the upgraded GEFS outperforms the operational-at-the-time version by all measures included in the GEFS verification package. The new system has a better ensemble error–spread relationship, significantly improved skills in large-scale environment forecasts, precipitation probability forecasts over CONUS, tropical cyclone track and intensity forecasts, and significantly reduced 2-m temperature biases over North America. GEFSv12 was implemented on 23 September 2020. [ABSTRACT FROM AUTHOR]

Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state‐of‐the‐art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow‐characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates. Key Points: The correction of modeled precipitation‐temperature correlations can improve the accuracy of hydrological simulationsThe advantages of using multivariate bias correction methods in hydrological simulations are weakened when it comes to the validation periodThe benefits of correcting precipitation‐temperature correlations in hydrological simulations are climate regime dependent [ABSTRACT FROM AUTHOR]

The extent to which submonthly forecast skill can be increased by spatial pattern correction is examined in probabilistic rainfall forecasts of weekly and week-3–4 averages, constructed with extended logistic regression (ELR) applied to three ensemble prediction systems from the Subseasonal-to-Seasonal (S2S) project database. The new spatial correction method projects the ensemble-mean rainfall neighboring each grid point onto Laplacian eigenfunctions and then uses those amplitudes as predictors in the ELR. Over North America, individual and multimodel ensemble (MME) forecasts that are based on spatially averaged rainfall (e.g., first Laplacian eigenfunction) are characterized by good reliability, better sharpness, and higher skill than those using the gridpoint ensemble mean. The skill gain is greater for week-3–4 averages than week-3 leads and is largest for MME week-3–4 outlooks that are almost 2 times as skillful as MME week-3 forecasts over land. Skill decreases when using more Laplacian eigenfunctions as predictors, likely because of the difficulty in fitting additional parameters from the relatively short common reforecast period. Higher skill when increasing reforecast length indicates potential for further improvements. However, the current design of most subseasonal forecast experiments may prove to be a limit on the complexity of correction methods. Relatively high skill for week-3–4 outlooks with winter starts during El Niño and MJO phases 2–3 and 6–7 reflects particular opportunities for skillful predictions. [ABSTRACT FROM AUTHOR]

A variational bias correction (VarBC) scheme is developed and tested using regional Weather Research and Forecasting Model Data Assimilation (WRFDA) to correct systematic errors in aircraft-based measurements of temperature produced by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system. Various bias models were investigated, using one or all of aircraft height tendency, Mach number, temperature tendency, and the observed temperature as predictors. These variables were expected to account for the representation of some well-known error sources contributing to uncertainties in TAMDAR temperature measurements. The parameters corresponding to these predictors were evolved in the model for a two-week period to generate initial estimates according to each unique aircraft tail number. Sensitivity experiments were then conducted for another one-month period. Finally, a case study using VarBC of a cold front precipitation event is examined. The implementation of VarBC reduces biases in TAMDAR temperature innovations. Even when using a bias model containing a single predictor, such as height tendency or Mach number, the VarBC produces positive impacts on analyses and short-range forecasts of temperature with smaller standard deviations and biases than the control run. Additionally, by employing a multiple-predictor bias model, which describes the statistical relations between innovations and predictors, and uses coefficients to control the evolution of components in the bias model with respect to their reference values, VarBC further reduces the average error of analyses and short-range forecasts with respect to observations. The potential impacts of VarBC on precipitation forecasts were evaluated, and the VarBC is able to indirectly improve the prediction of precipitation location by reducing the forecast error for wind-related synoptic circulation leading to precipitation. [ABSTRACT FROM AUTHOR]

This paper presents the results of a direct comparison of sub-seasonal (60-day) forecast skill using two different land surface initializations in the Canadian Climate Centre for Modelling and Analysis (CCCma) CanCM3 coupled global climate model. The first land surface initialization uses randomized values of soil moisture whereas in the second case the model is initialized with a “best-estimate” of soil moisture derived from offline land surface model simulations. In this experiment the realistic soil moisture initialization improved temperature forecast skill during the boreal summer. Improvement was particularly evident for the wettest and driest quartiles of soil moisture initial conditions. Certain geographic regions, such as North America, showed the greatest improvement in temperature forecast skill. In contrast to temperature forecasts, there was much less skill improvement in precipitation forecasts between the two different soil moisture initializations, although there are geographic regions, such as North America, that do show increased skill. [Traduit par la rédaction] Cet article présente les résultats d'une comparaison directe de l'habileté des prévisions sous-saisonnières (60 jours) faites au moyen de deux initialisations de surface du terrain différentes dans le modèle couplé climatique global CanCM3 du Centre canadien de la modélisation et de l'analyse climatique (CCCma). La première initialisation de la surface du terrain se sert de valeurs d'humidité du sol choisies au hasard alors que dans le second cas, le modèle est initialisé à l'aide des « meilleures valeurs estimatives » d'humidité du sol dérivées de simulations modélisées hors ligne de la surface du terrain. Dans cette expérience, l'initialisation réaliste de l'humidité du sol a amélioré l'habileté des prévisions de température durant l’été boréal. L'amélioration était particulièrement évidente pour les quartiles le plus mouillé et le plus sec des conditions initiales d'humidité du sol. Certaines régions géographiques, comme l'Amérique du Nord, ont montré la plus forte amélioration dans l'habileté des prévisions de températures. Comparativement aux prévisions de température, il y avait beaucoup moins d'amélioration d'habileté dans les prévisions de précipitations entre les deux différentes initialisations d'humidité du sol, bien que certaines régions géographiques, comme l'Amérique du Nord, montrent une habileté accrue. [ABSTRACT FROM AUTHOR]

Results from a suite of ensembles of twenty-first-century climate projections made using the Community Climate System Model, version 4 (CCSM4) are analyzed to document model bias and to explore possible future changes in air temperature, precipitation, and snow cover over North America. Large biases still exist in all analyzed fields in this version of the model, and the necessary assumption in future climate projections is therefore that the bias persists into the future, such that the differences in a field between two time periods are meaningful indications of potential changes. Projected temperature increases show strong regional patterns with spatial similarities for all the emissions scenarios considered, although there are considerable differences in the magnitude of the projected change. Projections indicate an increase in total precipitation over much of North America for all emissions scenarios, with the exception of the Southwest United States. All of North America except parts of northern Canada shows a projected decrease in snow cover over the twenty-first century. [ABSTRACT FROM AUTHOR]

Thirty years of the North American Regional Reanalysis (NARR) are dynamically downscaled to an 8-km grid spacing using the Regional Atmospheric Modeling System (RAMS) to generate a climatology of glacier winter accumulation over the southern Coast Mountains in British Columbia (BC), Canada. RAMS precipitation fields are bias corrected using observations from Environment Canada (EC) synoptic and climate stations and BC provincial snow pillow stations. Raw and bias-corrected model output is compared with observations from EC Reference Climate Network stations, BC provincial Ministry of Transportation and Highways stations, BC Hydro stations, snow course data, and glacier mass balance studies. A water balance is also applied to 12 drainage basins located within the modeling domain to test the consistency of both the raw and bias-corrected precipitation fields with observed streamflow. Model output is compared with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) and bias-corrected NARR. Isotropic spectral power densities are examined to compare the effective spatial resolution of the various precipitation fields. The spatial distribution of the bias-correction field suggests that RAMS underpredicts precipitation on the western edge of Vancouver Island, Canada, and overpredicts along the southern Coast Mountains. The bias correction helps close the water balance budgets in all basins except the Somass on Vancouver Island. The bias correction generally improves the agreement between RAMS and observed snow water equivalent amounts at the glacier and snow course sites, and observed precipitation amounts at the synoptic, climate, and snow pillow stations. The RAMS and NARR isotropic spectral power densities show a loss of variability at approximately 45 and 63 km, while PRISM shows little falloff down to 16 km. [ABSTRACT FROM AUTHOR]

The structure of the diurnal cycle of warm-season precipitation and its associated fields during the North American monsoon are examined for the core monsoon region and for the southwestern United States, using a diverse set of observations, analyses, and forecasts from the North American Monsoon Experiment field campaign of 2004. Included are rain gauge and satellite estimates of precipitation, Eta Model forecasts, and the North American Regional Reanalysis (NARR). Daily rain rates are of about the same magnitude in all datasets with the exception of the Climate Prediction Center (CPC) Morphing (CMORPH) technique, which exhibits markedly higher precipitation values. The diurnal cycle of precipitation within the core region occurs earlier in the day at higher topographic elevations, evolving with a westward shift of the maximum. This shift appears in the observations, reanalysis, and, while less pronounced, in the model forecasts. Examination of some of the fields associated with this cycle, including convective available potential energy (CAPE), convective inhibition (CIN), and moisture flux convergence (MFC), reveals that the westward shift appears in all of them, but more prominently in the latter. In general, warm-season precipitation in southern Arizona and parts of New Mexico shows a strong effect due to northward moisture surges from the Gulf of California. A reported positive bias in the NARR northward winds over the Gulf of California limits their use with confidence for studies of the moist surges along the Gulf; thus, the analysis is complemented with operational analysis and the Eta Model short-term simulations. The nonsurge diurnal cycle of precipitation lags the CAPE maximum by 6 h and is simultaneous with a minimum of CIN, while the moisture flux remains divergent throughout the day. During surges, CAPE and CIN have modifications only to the amplitude of their cycles, but the moisture flux becomes strongly convergent about 6 h before the precipitation maximum, suggesting a stronger role in the development of precipitation. [ABSTRACT FROM AUTHOR]

No-analog communities (communities that are compositionally unlike any found today) occurred frequently in the past and will develop in the greenhouse world of the future. The well documented no-analog plant communities of late-glacial North America are closely linked to ‘novel’ climates also lacking modern analogs, characterized by high seasonality of temperature. In climate simulations for the Intergovernmental Panel on Climate Change A2 and B1 emission scenarios, novel climates arise by 2100 AD, primarily in tropical and subtropical regions. These future novel climates are warmer than any present climates globally, with spatially variable shifts in precipitation, and increase the risk of species reshuffling into future no-analog communities and other ecological surprises. Most ecological models are at least partially parameterized from modern observations and so may fail to accurately predict ecological responses to these novel climates. There is an urgent need to test the robustness of ecological models to climate conditions outside modern experience. [ABSTRACT FROM AUTHOR]

In support of the 2004 North American Monsoon Experiment (NAME) field campaign, NOAA established and maintained a field site about 100 km north of Mazatlán, Mexico, consisting of wind profilers, precipitation profilers, surface upward–downward-looking radiometers, and a 10-m meteorological tower to observe the environment within the North American monsoon. Three objectives of this NOAA project are discussed in this paper: 1) to observe the vertical structure of precipitating cloud systems as they passed over the NOAA profiler site, 2) to estimate the vertical air motion and the raindrop size distribution from near the surface to just below the melting layer, and 3) to better understand the microphysical processes associated with stratiform rain containing well-defined radar bright bands. To provide a climatological context for the profiler observations at the field site, the profiler reflectivity distributions were compared with Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) reflectivity distributions from the 2004 season over the NAME domain as well as from the 1998–2005 seasons. This analysis places the NAME 2004 observations into the context of other monsoon seasons. It also provides a basis for evaluating the representativeness of the structure of the precipitation systems sampled at this location. The number of rain events observed by the TRMM PR is dependent on geography; the land region, which includes portions of the Sierra Madre Occidental, has more events than the coast and gulf regions. Conversely, from this study it is found that the frequencies of occurrence of stratiform rain and reflectivity profiles with radar bright bands are mostly independent of region. The analysis also revealed that the reflectivity distribution at each height has more year-to-year variability than region-to-region variability. These findings suggest that in cases with a well-defined bright band, the vertical profile of the reflectivity relative to the height of the bright band is similar over the gulf, coast, and land regions. [ABSTRACT FROM AUTHOR]

An international team of scientists from the United States, Mexico, and Central America carried out a major field campaign during the summer of 2004 to develop an improved understanding of the North American monsoon system leading to improved precipitation forecasts. Results from this campaign, which is the centerpiece of the North American Monsoon Experiment (NAME) Process Study, are reported in this issue of the Journal of Climate. In addition to a synthesis of key findings, this brief overview article also raises some important unresolved issues that require further attention. More detailed background information on NAME, including motivating science questions, where NAME 2004 was conducted, when, and the experimental design, was published previously by Higgins et al. [ABSTRACT FROM AUTHOR]

The interannual and intraseasonal variability of the North American monsoon is of great interest because a large proportion of the annual precipitation for Arizona and New Mexico arrives during the summer monsoon. Forty-one years of daily monsoon season precipitation data for Arizona and New Mexico were studied using wavelet analysis. This time-localized spectral analysis method reveals that periodicities of less than 8 days are positively correlated with mean daily precipitation during the 1 July–15 September monsoon period. Roughly 17% of the years indicate no significant periodicity during the monsoon period for either region and are associated with low monsoon precipitation. High- and low-frequency modes explain an equivalent percentage of the variance in monsoon precipitation in both Arizona and New Mexico, and in many years concurrent multiple periodicities occur. Wavelet analysis was effective in identifying the contribution of high-frequency modes that had not been discerned in previous studies. These results suggest that precipitation processes during the monsoon season are modulated by phenomena operating at synoptic (2–8 days) and longer (>8 days) time scales and point to the need for further studies to better understand the associated atmospheric processes. [ABSTRACT FROM AUTHOR]

It is not unreasonable to expect that boreal forests that exist along 60°N in the Eurasian and North American continents were created and are maintained by warm seasonal rainfall. As revealed from satellite observations and various precipitation sources, zonally elongated rainbelts appear along these forests. Previous studies show that a relationship may exist between the frontal zone along the Arctic seaboard and regional patterns of high-latitude precipitation. It was observed by this study that baroclinic zones associated with strong Arctic westerlies coincide with minor storm tracks and boreal forest rainbelts only in eastern Canada. In contrast, this coincidence does not occur in northern Europe, eastern Siberia, and the Alaska–Pacific coast, because boreal forest rainbelts in these regions are located farther south of strong Arctic westerlies and ahead of high-latitude troughs over central Eurasia, the Bering Sea, the Labrador Sea, and the Norwegian Sea. Therefore, instead of baroclinicity along strong Arctic westerlies, favorable environments for the formation of minor storm tracks are developed by positive vorticity advections ahead of these high-latitude troughs. The water vapor budget analyses performed with NCEP and Goddard Earth Observing System (GEOS-1) reanalyses show that the boreal forest rainbelts are essentially maintained by the convergence of water vapor flux associated with transient disturbances at high latitudes. [ABSTRACT FROM AUTHOR]

The North American Monsoon Experiment (NAME) is an internationally coordinated process study aimed at determining the sources and limits of predictability of warm-season precipitation over North America. The scientific objectives of NAME are to promote a better understanding and more realistic simulation of warm-season convective processes in complex terrain, intraseasonal variability of the monsoon, and the response of the warm-season atmospheric circulation and precipitation patterns to slowly varying, potentially predictable surface boundary conditions. During the summer of 2004, the NAME community implemented an international (United States, Mexico, Central America), multiagency (NOAA, NASA, NSF, USDA) field experiment called NAME 2004. This article presents early results from the NAME 2004 campaign and describes how the NAME modeling community will leverage the NAME 2004 data to accelerate improvements in warm-season precipitation forecasts for North America. [ABSTRACT FROM AUTHOR]

Understanding how bird populations respond to changes in waterbody availability in the climatically variable Prairie Pothole Region (PPR) of North America hinges on being able to couple hydrological and climate modeling to represent potential future landscapes. Model experiments run with the Pothole Complex Hydrologic Model using downscaled climate data (variables relating to precipitation, temperature, and potential evapotranspiration at 1/8° spatial resolution under four general circulation climate models and two gas emissions scenarios) were used to forecast the abundances of six focal wetland‐dependent bird species in the Missouri Coteau portion of the PPR, providing ensemble scenarios at a spatial scale relevant to resource management. Although the projected number of May ponds (waterbodies present during bird breeding season) fluctuated through time with some decadal periodicity (and with the number present in a given year reflecting abundance over the previous three years), the ensemble model average indicated an increase in the average number of waterbodies present by the turn of the next century. Overall, the model experiments conservatively projected an 11.75% increase in the number of waterbodies present by 2090–2099 compared to a baseline period from 1967 to 2005 in the PPR. Wetland‐dependent bird occurrence and abundance were significantly associated with temporal patterns and decadal periodicity in waterbody dynamics. Because of the strong associations between wetland‐dependent bird occurrence and abundance and the number of prairie potholes, projected waterbody increases are forecasted to result in an 11.97% overall increase in occurrence and 8.63% increase in abundance of the six focal species by the end of the 21st century; these results contrast with forecasted drought‐associated declines in waterbodies and birds in the PPR. This integrated hydrological–climatological approach offers a means of assessing how wetland‐dependent bird populations may respond to changes in wetland habitat availability due to a changing climate. Our results provide information that can help managers decide how to mitigate the effects of climate shifts on the distribution of wetland habitat and biota. [ABSTRACT FROM AUTHOR]