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Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new "probabilistic" gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). [ABSTRACT FROM AUTHOR]

This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models' ability to predict extended periods of extreme climate conducive to eight 'billion-dollar' historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales. [ABSTRACT FROM AUTHOR]

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of "tail dependence" arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data. [ABSTRACT FROM AUTHOR]

Precipitation over the contiguous United States exhibits large multi-decadal oscillations since the early twentieth century, and they often lead to dry (e.g., 1946-1976 and 1999-present) and wet (e.g., 1977-1998) periods and apparent precipitation trends (e.g., from the 1950s to 1990s) over most of the western and central US. The exact cause of these inter-decadal variations is not fully understood. Using observational and reanalysis data and model simulations, this paper examines the influence of the Inter-decadal Pacific Oscillation (IPO) on US precipitation. The IPO is a leading mode of sea surface temperatures (SSTs) seen mostly in the Pacific Ocean. It is found that decadal precipitation variations over much of the West and Central US, especially the Southwest, closely follow the evolution of the IPO ( r = 0.85 during 1923-2010 for the Southwest US), and the dry and wet periods are associated, respectively, with the cold and warm phases of the IPO. In particular, the apparent upward trend from the 1950s-1990s and the dry decade thereafter in precipitation over much of the West and Central US are largely caused by the IPO cycles, which switched to a warm phase around 1977 and back to a cold phase around 1999. An atmospheric model forced with observed SSTs reproduces much of this association of US precipitation with the IPO ( r = 0.95 between smoothed observed and simulated Southwest US precipitation during 1950-2009 and r = 0.88 between the simulated Southwest US precipitation and the IPO). Atmospheric reanalysis and model data both show a strong high (low) pressure center and anti-cyclonic (cyclonic) anomaly circulation over the North Pacific in the lower troposphere during cold (warm) phases of the IPO, which lead to dry and cold northwesterly and northerly winds and below-normal precipitation over much of the West US during IPO cold periods. The IPO induced changes are most pronounced during the boreal cold season. The results reinforce the notion that tropical Pacific SSTs (and the accompanying SST anomalies in the North Pacific) have large impacts on US precipitation and highlight the need to understand and simulate the IPO for decadal prediction of US precipitation. [ABSTRACT FROM AUTHOR]

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere-ocean-sea ice-land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998-2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately. [ABSTRACT FROM AUTHOR]

Madden-Julian Oscillation (MJO) signals have been detected using highly sampled observations from the U.S. DOE ARM Climate Research Facility located at the Tropical Western Pacific Manus site. Using downwelling shortwave radiative fluxes and derived shortwave fractional sky cover, and the statistical tools of wavelet, cross wavelet, and Fourier spectrum power, we report finding major convective signals and their phase change from surface observations spanning from 1996 to 2006. Our findings are confirmed with the satellite-gauge combined values of precipitation from the NASA Global Precipitation Climatology Project and the NOAA interpolated outgoing longwave radiation for the same location. We find that the Manus MJO signal is weakest during the strongest 1997-1998 El Niño Southern Oscillation (ENSO) year. A significant 3-5-month lead in boreal winter is identified further between Manus MJO and NOAA NINO3.4 sea surface temperature (former leads latter). A striking inverse relationship is found also between the instantaneous synoptic and intraseasonal phenomena over Manus. To further study the interaction between intraseasonal and diurnal scale variability, we composite the diurnal cycle of cloudiness for 21-MJO events that have passed over Manus. Our diurnal composite analysis of shortwave and longwave fractional sky covers indicates that during the MJO peak (strong convection), the diurnal amplitude of cloudiness is reduced substantially, while the diurnal mean cloudiness reaches the highest value and there are no significant phase changes. We argue that the increasing diurnal mean and decreasing diurnal amplitude are caused by the systematic convective cloud formation that is associated with the wet phase of the MJO, while the diurnal phase is still regulated by the well-defined solar forcing. This confirms our previous finding of the anti-phase relationship between the synoptic and intraseasonal phenomena. The detection of the MJO over the Manus site provides further opportunities in using other ground-based remote sensing instruments to investigate the vertical distributions of clouds and radiative heatings of the MJO that currently is impossible from satellite observations. [ABSTRACT FROM AUTHOR]

This study investigates the refined simulation skill that results when the regional Climate extension of the Weather Research and Forecasting (CWRF) model is nested in the ECMWF Hamburg version 4.5 (ECHAM) atmospheric general circulation model over the United States during 1980-2009, where observed sea surface temperatures are used in both models. Over the contiguous US, for each of the four seasons from winter to fall, CWRF reduces the root mean square error of the ECHAM seasonal mean surface air temperature simulation by 0.19, 0.82, 2.02 and 1.85 °C, and increases the equitable threat score of seasonal mean precipitation by 0.18, 0.11, 0.09 and 0.12. CWRF also simulates much more realistically daily precipitation frequency and heavy precipitation events, typically over the Central Great Plains, Cascade Mountains and Gulf Coast States. These CWRF skill enhancements are attributed to the increased spatial resolution and physics refinements in representing orographic, terrestrial hydrology, convection, and cloud-aerosol-radiation effects and their interactions. Empirical orthogonal function analysis of seasonal mean precipitation and surface air temperature interannual variability shows that, in general, CWRF substantially improves the spatial distribution of both quantities, while temporal evolution (i.e. interannual variability) of the first 3 primary patterns is highly correlated with that of the driving ECHAM (except for summer precipitation), and they both have low temporal correlations against observations. During winter, when large-scale forcing dominates, both models also have similar responses to strong ENSO signals where they successfully capture observed precipitation composite anomalies but substantially fail to reproduce surface air temperature anomalies. When driven by the ECMWF Reanalysis Interim, CWRF produces a very realistic interannual evolution of large-scale precipitation and surface air temperature patterns where the temporal correlations with observations are significant. These results indicate that CWRF can greatly improve mesoscale regional climate structures but it cannot change interannual variations of the large-scale patterns, which are determined by the driving lateral boundary conditions. [ABSTRACT FROM AUTHOR]

An ensemble of future climate projections performed with the regional climate model RegCM4 is used to assess changes in inter-annual variability of precipitation over Southern Mexico and Central America (SMECAM). Two different Global Climate Models (GCMs) from the coupled model intercomparison project phase 5 are used to provide boundary conditions for two different RegCM4 configurations. This results in four regional climate projections extending from 1970 to 2100 for the greenhouse gas representative concentration pathway RCP8.5. The precipitation variability over the SMECAM region and its dependence on the gradient between Atlantic and Pacific sea surface temperature (SST) anomalies are verified by reproducing SST anomaly patterns during wettest and driest years similar to those seen in observational datasets. RegCM4 does a comparably better job than the driving GCMs. This strong relationship between precipitation and SST anomalies does not appear to change substantially under future climate conditions. For the rainy season, June to September, we find a future change in inter-annual variability of precipitation towards a much greater occurrence of very dry seasons over the SMECAM region, with this change being more pronounced in the regional than in the global model projections. A greater warming of the Tropical Northeastern Pacific (TNP) compared to the Tropical North Atlantic (TNA), which causes stronger wind fluxes from the TNA to the TNP through the Caribbean Low Level Jet, is identified as the main process responsible for these drier conditions. [ABSTRACT FROM AUTHOR]

In the western United States, persistent and recurrent flow patterns not only modulate precipitation events but also result in prolonged surface inversion episodes. In this region, the frequency of persistent ridge/trough events ranges between 20 and 40 days, well within the intraseasonal timescale. Based on NCEP reanalysis data starting at 1949, with a focus on the interior West, we observed that episodes of prolonged ridge/trough events appear to occur about a week later every year and resets every 5-7 years-a previously undocumented phenomenon examined herein. Diagnostic analyses indicate that the interplay between regional intraseasonal flow patterns and the North Atlantic Oscillation (NAO) alternates the preferred timeframe for the persistent ridge/trough events to occur. This may result from different phases of the NAO shifting the winter mean ridge and such shifts modulate the occurrence and timing of persistent ridge/trough events. When the timing changes evolve around the quasi-6 years cycle of the NAO, the resultant evolution forms what appears to be a steady phase delay in the ridge/trough events year after year. These results are a further step in disclosing the multiple-scale interaction between intraseasonal and interannual modes and its regional climate/weather impact. [ABSTRACT FROM AUTHOR]

This study evaluates the UCLA-ETA regional model's dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement. [ABSTRACT FROM AUTHOR]

Ensemble regional model simulations over the central US with 30-km resolution are analyzed to investigate the physical processes of projected precipitation changes in the mid-twenty-first century under greenhouse gas forcing. An atmospheric moisture balance is constructed, and changes in the diurnal cycle are evaluated. Wetter conditions over the central US in April and May occur most strongly in the afternoon and evening, supported primarily by moisture convergence by transient eddy activity, indicating enhanced daytime convection. In June, increased rainfall over the northern Great Plains is strongest from 0000 to 0600 LT. It is supported by positive changes in stationary meridional moisture convergence related to a strengthening of the GPLLJ accompanied by an intensification of the western extension of the North Atlantic subtropical high. In the Midwest, decreased rainfall is strongest at 1500 LT and 0000 LT. Both a suppression of daytime convection as well as changes in the zonal flow in the GPLLJ exit region are important. Future drying over the northern Great Plains in summer is triggered by weakened daytime convection, and persists throughout August and September when a deficit in soil moisture develops and land-atmosphere feedbacks become increasingly important. [ABSTRACT FROM AUTHOR]

Seasonal extreme daily precipitation is analyzed in the ensemble of NARCAPP regional climate models. Significant variation in these models' abilities to reproduce observed precipitation extremes over the contiguous United States is found. Model performance metrics are introduced to characterize overall biases, seasonality, spatial extent and the shape of the precipitation distribution. Comparison of the models to gridded observations that include an elevation correction is found to be better than to gridded observations without this correction. A complicated model weighting scheme based on model performance in simulating observations is found to cause significant improvements in ensemble mean skill only if some of the models are poorly performing outliers. The effect of lateral boundary conditions are explored by comparing the integrations driven by reanalysis to those driven by global climate models. Projected mid-century future changes in seasonal precipitation means and extremes are presented and discussions of the sources of uncertainty and the mechanisms causing these changes are presented. [ABSTRACT FROM AUTHOR]

An attempt has been made to explore all the facets of Evaporation-Precipitation (E-P) distribution and variability over the Indian Ocean (IO) basin using Objectively Analyzed air-sea Fluxes (OAFlux) data and subsequently a thorough assessment of the latest version of National Centers for Environment Prediction (NCEP) Climate Forecast System (CFS) version-2 is done. This study primarily focuses on two fundamental issues, first, the core issue of pervasive cold SST bias in the CFS simulation in the context of moisture flux exchange between the atmosphere and the ocean and second, the fidelity of the model in simulating mean and variability of E-P and its elemental components associated with the climatic anomalies occurring over the Indian and the Pacific ocean basin. Valuation of evaporation and precipitation, the two integral component of E-P, along with the similar details of wind speed, air-sea humidity difference ( $$\Updelta Q$$) and Sea Surface Temperature (SST) are performed. CFS simulation is vitiated by the presence of basin wide systematic positive bias in evaporation, $$\Updelta Q$$ and similar negative bias in wind speed and SST. Bifurcation of the evaporation bias into its components reveals that bias in air humidity ( $$\hbox{Q}_{a}$$) is basically responsible for the presence of pervasive positive evaporation bias. The regions where CFS does not adhere to the observed wind-evaporation and $$\hbox{Q}_{a}$$-evaporation relation was found to lie over the northern Arabian Sea (AS), the western Bay of Bengal (BoB) and the western Equatorial IO. Evaporation bias is found to control a significant quantum of cold SST bias over most of the basin owing to its intimate association with SST in a coupled feedback system. This area is stretched over the almost entire north IO, north of $$15^{\circ}\hbox{S}$$ excluding a small equatorial strip, where the evaporation bias may essentially explain 20-100 % of cold SST bias. This percentage is maximum over the western IO, central AS and BoB. The CFS simulation comply the distinct feature of the observed mean annual cycle of evaporation and precipitation, but with the additive systematic bias over most of the region. El Niño and negative Indian Ocean Dipole (NIOD) seems to have much better control over the interannual variability of evaporation in the CFS simulation, contrary to the observation where El Niño and positive Indian Ocean Dipole (PIOD) has the larger say. Both El Niño and PIOD (La Niña and NIOD) have the negative (positive) influence on the basin wide evaporation with the exception over a limited region and this relation holds for the twain. The seasonal (JJA and SON) locking of El Niño and PIOD to evaporation and precipitation is displayed by the north-south and east-west asymmetric correlation pattern respectively and this is much perspicuous in the observation as compared to the CFS. The conjoined influence of El Niño and PIOD on evaporation (precipitation) reveals the dominance of PIOD (PIOD + El Niño) response in case of both the observation as well as the CFS. This study will lead a way forward to rectify the ubiquitous cold SST bias in the CFS simulation and help in establishing the credibility of the CFS in terms of seasonal predictability. [ABSTRACT FROM AUTHOR]

The effects of horizontal resolution and the treatment of convection on simulation of the diurnal cycle of precipitation during boreal summer are analyzed in several innovative weather and climate model integrations. The simulations include: season-long integrations of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) with explicit clouds and convection; year-long integrations of the operational Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts at three resolutions (125, 39 and 16 km); seasonal simulations of the same model at 10 km resolution; and seasonal simulations of the National Center for Atmospheric Research (NCAR) low-resolution climate model with and without an embedded two-dimensional cloud-resolving model in each grid box. NICAM with explicit convection simulates best the phase of the diurnal cycle, as well as many regional features such as rainfall triggered by advancing sea breezes or high topography. However, NICAM greatly overestimates mean rainfall and the magnitude of the diurnal cycle. Introduction of an embedded cloud model within the NCAR model significantly improves global statistics of the seasonal mean and diurnal cycle of rainfall, as well as many regional features. However, errors often remain larger than for the other higher-resolution models. Increasing resolution alone has little impact on the timing of daily rainfall in IFS with parameterized convection, yet the amplitude of the diurnal cycle does improve along with the representation of mean rainfall. Variations during the day in atmospheric prognostic fields appear quite similar among models, suggesting that the distinctive treatments of model physics account for the differences in representing the diurnal cycle of precipitation. [ABSTRACT FROM AUTHOR]

This study examines the potential impact of vegetation feedback on changes in summer climate aridity over the contiguous United States (US) due to the doubling of atmospheric CO concentration using a set of 100-year-long climate simulations made by a global climate model interactively coupled with a dynamic vegetation model. The Thornthwaite moisture index ( I), which quantifies climate aridity on the basis of atmospheric water supply (i.e., precipitation) and atmospheric water demand (i.e., potential evapotranspiration, PET), is used to measure climate aridity. Warmer atmosphere and drier surface resulting from increased CO concentration increase climate aridity over most of the contiguous US. This phenomenon is due to larger increments in PET than in precipitation, regardless of the presence or absence of vegetation feedback. Compared to simulations without active dynamic vegetation feedback, the presence of vegetation feedback significantly alleviates the increase in aridity. This vegetation-feedback effect is most noticeable in the subhumid regions such as southern, midwestern and northwestern US, primarily by the increasing vegetation greenness. In these regions, the greening in response to warmer temperatures enhances moisture transfer from soil to atmosphere by evapotranspiration (ET). The increased ET and subsequent moistening over land areas result in weaker surface warming (1-2 K) and PET (3-10 mm month), and greater precipitation (4-10 mm month). Collectively, they result in moderate increases in I. Our results suggest that moistening by enhanced vegetation feedback may prevent aridification under climatic warming especially in areas vulnerable to climate change, with consequent implications for mitigation strategies. [ABSTRACT FROM AUTHOR]

Trends in monthly heavy precipitation, defined by a return period of one year, are assessed for statistical significance in observations and Global Climate Model (GCM) simulations over the contiguous United States using Monte Carlo non-parametric and parametric bootstrapping techniques. The results from the two Monte Carlo approaches are found to be similar to each other, and also to the traditional non-parametric Kendall's τ test, implying the robustness of the approach. Two different observational data-sets are employed to test for trends in monthly heavy precipitation and are found to exhibit consistent results. Both data-sets demonstrate upward trends, one of which is found to be statistically significant at the 95% confidence level. Upward trends similar to observations are observed in some climate model simulations of the twentieth century, but their statistical significance is marginal. For projections of the twenty-first century, a statistically significant upwards trend is observed in most of the climate models analyzed. The change in the simulated precipitation variance appears to be more important in the twenty-first century projections than changes in the mean precipitation. Stochastic fluctuations of the climate-system are found to be dominate monthly heavy precipitation as some GCM simulations show a downwards trend even in the twenty-first century projections when the greenhouse gas forcings are strong. [ABSTRACT FROM AUTHOR]

We present a new event-scale catalog of stable isotopic measurements from 5 years of storm events at 4 sites in southern California, which is used to understand the storm to storm controls on the isotopic composition of precipitation and validate the event-scale performance of an isotope-enabled GCM simulation ( IsoGSM) (Yoshimura et al. ). These analyses are motivated to improve the interpretation of proxy records from this region and provide guidance in testing the skill of GCMs in reproducing the hydrological variability in the western US. We find that approximately 40% of event-scale isotopic variability arises from the percentage of precipitation that is convective and the near surface relative humidity in the days prior to the storms landfall. The additional isotopic variability arises from the fact that storms arriving from different source regions advect moisture of distinct isotopic compositions. We show using both field correlation and Lagrangian trajectory analysis that the advection of subtropical and tropical moisture is important in producing the most isotopically enriched precipitation. The isotopic catalog is then used along with satellite-derived δD retrievals of atmospheric moisture to benchmark the performance of the IsoGSM model for the western US. The model is able to successfully replicate the observed isotopic variability suggesting that it is closely reproducing the moisture transport and storm track dynamics that drive the large storm-to-storm isotopic range. Notably, we find that an increase in moisture flux from the central tropical Pacific leads to a convergence of isotopically enriched water vapor in the subtropics and consequently an increase in δO of precipitation at sites along the entire west coast. Changes in poleward moisture flux from the central Tropical Pacific have important implications for both the global hydrological cycle and regional precipitation amounts and we suggest such changes can be captured through instrumental and proxy-reconstruction of the spatiotemporal isotopic patterns in the precipitation along the west coast of the US. [ABSTRACT FROM AUTHOR]

variable-grid atmospheric general circulation model, LMDZ, with a local zoom over southeast China is used to investigate regional climate changes in terms of both means and extremes. Two time slices of 30 years are chosen to represent, respectively, the end of the 20th century and the middle of the 21st century. The lower-boundary conditions (sea-surface temperature and sea-ice extension) are taken from the outputs of three global coupled climate models: Institut Pierre-Simon Laplace (IPSL), Centre National de Recherches Météorologiques (CNRM) and Geophysical Fluid Dynamics Laboratory (GFDL). Results from a two-way nesting system between LMDZ-global and LMDZ-regional are also presented. The evaluation of simulated temperature and precipitation for the current climate shows that LMDZ reproduces generally well the spatial distribution of mean climate and extreme climate events in southeast China, but the model has systematic cold biases in temperature and tends to overestimate the extreme precipitation. The two-way nesting model can reduce the 'cold bias' to some extent compared to the one-way nesting model. Results with greenhouse gas forcing from the SRES-A2 emission scenario show that there is a significant increase for mean, daily-maximum and minimum temperature in the entire region, associated with a decrease in the number of frost days and an increase in the heat wave duration. The annual frost days are projected to significantly decrease by 12-19 days while the heat wave duration to increase by about 7 days. A warming environment gives rise to changes in extreme precipitation events. Except two simulations (LMDZ/GFDL and LMDZ/IPSL2) that project a decrease in maximum 5-day precipitation (R5d) for winter, other precipitation extremes are projected to increase over most of southeast China in all seasons, and among the three global scenarios. The domain-averaged values for annual simple daily intensity index (SDII), R5d and fraction of total rainfall from extreme events (R95t) are projected to increase by 6-7, 10-13 and 11-14%, respectively, relative to their present-day values. However, it is clear that more research will be needed to assess the uncertainties on the projection in future of climate extremes at local scale. [ABSTRACT FROM AUTHOR]

Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region. [ABSTRACT FROM AUTHOR]

We investigate the ability of a global atmospheric general circulation model (AGCM) to reproduce observed 20 year return values of the annual maximum daily precipitation totals over the continental United States as a function of horizontal resolution. We find that at the high resolutions enabled by contemporary supercomputers, the AGCM can produce values of comparable magnitude to high quality observations. However, at the resolutions typical of the coupled general circulation models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, the precipitation return values are severely underestimated. [ABSTRACT FROM AUTHOR]

Extreme climate events have been increasing over much of the world, and dynamical models predict further increases in response to enhanced greenhouse forcing. We examine the ability of a high-resolution nested climate model, RegCM3, to capture the statistics of daily-scale temperature and precipitation events over the conterminous United States, using observational and reanalysis data for comparison. Our analyses reveal that RegCM3 captures the pattern of mean, interannual variability, and trend in the tails of the daily temperature and precipitation distributions. However, consistent biases do exist, including wet biases in the topographically-complex regions of the western United States and hot biases in the southern and central United States. The biases in heavy precipitation in the western United States are associated with excessively strong surface and low-level winds. The biases in daily-scale temperature and precipitation in the southcentral United States are at least partially driven by biases in circulation and moisture fields. Further, the areas of agreement and disagreement with the observational data are not intuitive from analyzing the simulated mean seasonal temperature and precipitation fields alone. Our evaluation should enable more informed application and improvement of high-resolution climate models for the study of future changes in socially- and economically-relevant temperature and precipitation events. [ABSTRACT FROM AUTHOR]

Snow cover is known to exert a strong influence on climate, but quantifying its impact is difficult. This study investigates the global impact of terrestrial snow cover through a pair of GCM simulations run with prognostic snow cover and with all snow cover on land eliminated (NOSNOWCOVER). In this experiment all snowfall over land was converted into its liquid–water equivalent upon reaching the surface. Compared with the control run, NOSNOWCOVER produces mean-annual surface air temperatures up to 5 K higher over northern North America and Eurasia and 8–10 K greater during winter. The globally averaged warming of 0.8 K is one-third as large as the model’s response to 2 × CO2 forcing. The pronounced surface heating propagates throughout the troposphere, causing changes in surface and upper-air circulation patterns. Despite the large atmospheric warming, the absence of an insulating snow pack causes soil temperatures in NOSNOWCOVER to fall throughout northern Asia and Canada, including extreme wintertime cooling of over 20 K in Siberia and a 70% increase in permafrost area. The absence of snow melt water also affects extratropical surface hydrology, causing significantly drier upper-layer soils and dramatic changes in the annual cycle of runoff. Removing snow cover also drastically affects extreme weather. Extreme cold-air outbreaks (CAOs)—defined relative to the control climatology—essentially disappear in NOSNOWCOVER. The loss of CAOs appears to stem from both the local effects of eliminating snow cover in mid-latitudes and a remote effect over source regions in the Arctic, where −40°C air masses are no longer able to form. [ABSTRACT FROM AUTHOR]

We analyze simulations of the global climate performed at a range of spatial resolutions to assess the effects of horizontal spatial resolution on the ability to simulate precipitation in the continental United States. The model investigated is the CCM3 general circulation model. We also preliminarily assess the effect of replacing cloud and convective parameterizations in a coarse-resolution (T42) model with an embedded cloud-system resolving model (CSRM). We examine both spatial patterns of seasonal-mean precipitation and daily time scale temporal variability of precipitation in the continental United States. For DJF and SON, high-resolution simulations produce spatial patterns of seasonal-mean precipitation that agree more closely with observed precipitation patterns than do results from the same model (CCM3) at coarse resolution. However, in JJA and MAM, there is little improvement in spatial patterns of seasonal-mean precipitation with increasing resolution, particularly in the southeast USA. This is because of the dominance of convective (i.e., parameterized) precipitation in these two seasons. We further find that higher-resolution simulations have more realistic daily precipitation statistics. In particular, the well-known tendency at coarse resolution to have too many days with weak precipitation and not enough intense precipitation is partially eliminated in higher-resolution simulations. However, even at the highest resolution examined here (T239), the simulated intensity of the mean and of high-percentile daily precipitation amounts is too low. This is especially true in the southeast USA, where the most extreme events occur. A new GCM, in which a cloud-resolving model (CSRM) is embedded in each grid cell and replaces convective and stratiform cloud parameterizations, solves this problem, and actually produces too much precipitation in the form of extreme events. However, in contrast to high-resolution versions of CCM3, this model produces little improvement in spatial patterns of seasonal-mean precipitation compared to models at the same resolution using traditional parameterizations. [ABSTRACT FROM AUTHOR]