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Axel J. Schweiger, Kevin R. Wood, and Jinlun Zhang

Abstract

PIOMAS-20C, an Arctic sea ice reconstruction for 1901–2010, is produced by forcing the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) with ERA-20C atmospheric data. ERA-20C performance over Arctic sea ice is assessed by comparisons with measurements and data from other reanalyses. ERA-20C performs similarly with respect to the annual cycle of downwelling radiation, air temperature, and wind speed compared to reanalyses with more extensive data assimilation such as ERA-Interim and MERRA. PIOMAS-20C sea ice thickness and volume are then compared with in situ and aircraft remote sensing observations for the period of ~1950–2010. Error statistics are similar to those for PIOMAS. We compare the magnitude and patterns of sea ice variability between the first half of the twentieth century (1901–40) and the more recent period (1980–2010), both marked by sea ice decline in the Arctic. The first period contains the so-called early-twentieth-century warming (ETCW; ~1920–40) during which the Atlantic sector saw a significant decline in sea ice volume, but the Pacific sector did not. The sea ice decline over the 1979–2010 period is pan-Arctic and 6 times larger than the net decline during the 1901–40 period. Sea ice volume trends reconstructed solely from surface temperature anomalies are smaller than PIOMAS-20C, suggesting that mechanisms other than warming, such as changes in ice motion and deformation, played a significant role in determining sea ice volume trends during both periods.

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Dennis P. Lettenmaier, Eric F. Wood, and James R. Wallis

Abstract

Spatial patterns in trends of four monthly variables: average temperature, precipitation, streamflow, and average of the daily temperature range were examined for the continental United States for the period 1948–88. The data used are a subset of the Historical Climatology Network (1036 stations) and a stream gage network of 1009 stations. Trend significance was determined using the nonparametric seasonal Kendall's test on a monthly and annual basis, and a robust slope estimator was used for determination of trend magnitudes. A bivariate test was used for evaluation of relative changes in the variables, specifically, streamflow relative to precipitation, streamflow relative to temperature, and precipitation relative to temperature.

Strong trends were found in all of the variables at many more stations than would be expected due to chance. There is a strong spatial and seasonal structure in the trend results. For instance, although annual temperature increases were found at many stations, mostly in the North and West, there were almost as many downtrends, especially in the South and East. Among the most important trend patterns are (a) increases in March temperature at almost half of the stations; (b) increases in precipitation from September through December at as many as 25 percent of the stations, mostly in the central part of the country; (c) strong increases in streamflow in the period November–April at a maximum of almost half of the stations, with the largest trend magnitudes in the north-central states; (d) changes in the temperature range (mostly downward) at a large number of stations beginning in late spring and continuing through winter, affecting as many as over half of the stations. The observed trends in streamflow are not entirely consistent with the changes in the climatic variables and may be due to a combination of climatic and water management effects.

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Rex C. Wood, Richard K. Olson, and Andrew R. McFarland

Abstract

The air ejector filter sampler is a balloon-borne device designed to collect particulate matter from very large volumes (105 ft2) of stratospheric air at altitudes between 50,000 and 130,000 ft. This equipment utilize an ejector pump to pull air through 2 ft2 of Institute of Paper Chemistry (IPC) #1478 filter paper at rates on the order of 1000 cfm. Use of this unit has permitted an extension of the U.S. Atomic Energy Commission operational sampling program to higher attitudes than previously allowed by battery powered electro-mechanical systems. Performance of the sampler during a successful operational series conducted in 1965 by the U.S. Air Force at San Angelo, Texas, and Eielson AFB, Alaska, has confirmed pre-program estimates of system reliability.

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Andrew J. Heymsfield, Aaron Bansemer, Michael R. Poellot, and Norm Wood

Abstract

The detailed microphysical processes and properties within the melting layer (ML)—the continued growth of the aggregates by the collection of the small particles, the breakup of these aggregates, the effects of relative humidity on particle melting—are largely unresolved. This study focuses on addressing these questions for in-cloud heights from just above to just below the ML. Observations from four field programs employing in situ measurements from above to below the ML are used to characterize the microphysics through this region. With increasing temperatures from about −4° to +1°C, and for saturated conditions, slope and intercept parameters of exponential fits to the particle size distributions (PSD) fitted to the data continue to decrease downward, the maximum particle size (largest particle sampled for each 5-s PSD) increases, and melting proceeds from the smallest to the largest particles. With increasing temperature from about −4° to +2°C for highly subsaturated conditions, the PSD slope and intercept continue to decrease downward, the maximum particle size increases, and there is relatively little melting, but all particles experience sublimation.

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Joshua K. Roundy, Craig R. Ferguson, and Eric F. Wood

Abstract

Droughts represent a significant source of social and economic damage in the southeast United States. Having sufficient warning of these extreme events enables managers to prepare for and potentially mitigate the severity of their impacts. A seasonal hydrologic forecast system can provide such warning, but current forecast skill is low during the convective season when precipitation is affected by regionally varying land surface heat flux contributions. Previous studies have classified regions into coupling regimes based on the tendency of surface soil moisture anomalies to trigger convective rainfall. Until now, these classifications have been aimed at assessing the long-term dominant feedback signal. Sufficient focus has not been placed on the temporal variability that underlies this signal. To better understand this aspect of coupling, a new classification methodology suitable at daily time scales is developed. The methodology is based on the joint probability space of surface soil moisture, convective triggering potential, and the low-level humidity index. The methodology is demonstrated over the U.S. Southeast using satellite remote sensing, reanalysis, and hydrological model data. The results show strong persistence in coupling events that is linked to the land surface state. A coupling-based drought index shows good agreement with the temporal and spatial variability of drought and highlights the role of coupling in drought recovery. The implications of the findings for drought and forecasting are discussed.

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Ryan Eastman, Christopher R. Terai, Daniel P. Grosvenor, and Robert Wood

Abstract

A Lagrangian framework is developed to show the daily-scale time evolution of low clouds over the eastern subtropical oceans. An identical framework is applied to two general circulation models (GCMs): the CAM5 and UKMET and a set of satellite observations. This approach follows thousands of parcels as they advect downwind in the subtropical trade winds, comparing cloud evolution in time and space. This study tracks cloud cover, in-cloud liquid water path (CLWP), droplet concentration N d, planetary boundary layer (PBL) depth, and rain rate as clouds transition from regions with predominately stratiform clouds to regions containing mostly trade cumulus. The two models generate fewer clouds with greater N d relative to observations. Models show stronger Lagrangian cloud cover decline and greater PBL deepening when compared with observations. In comparing frequency distributions of cloud variables over time, it is seen that models generate increasing frequencies of nearly clear conditions at the expense of overcast conditions, whereas observations show transitions from overcast to cloud amounts between 50% and 90%. Lagrangian decorrelation time scales (e-folding time τ) of cloud cover and CLWP are between 11 and 19 h for models and observations, although they are a bit shorter for models. A Lagrangian framework applied here resolves and compares the time evolution of cloud systems as they adjust to environmental perturbations in models and observations. Increasing subsidence in the overlying troposphere leads to declining cloud cover, CLWP, PBL depth, and rain rates in models and observations. Modeled cloud responses to other meteorological variables are less consistent with observations, suggesting a need for continuing mechanical improvements in GCMs.

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Craig R. Ferguson, Eric F. Wood, and Raghuveer K. Vinukollu

Abstract

Land–atmosphere coupling strength or the degree to which land surface anomalies influence boundary layer development—and in extreme cases, rainfall—is arguably the single most fundamental criterion for evaluating hydrological model performance. The Global Land–Atmosphere Coupling Experiment (GLACE) showed that strength of coupling and its representation can affect a model’s ability to simulate climate predictability at the seasonal time scale. And yet, the lack of sufficient observations of coupling at appropriate temporal and spatial scales has made achieving “true” coupling in models an elusive goal. This study uses Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) soil moisture (SM), multisensor remote sensing (RS) evaporative fraction (EF), and Atmospheric Infrared Sounder (AIRS) lifting condensation level (LCL) to evaluate the realism of coupling in the Global Land Data Assimilation System (GLDAS) suite of land surface models (LSMs), Princeton Global Forcing Variable Infiltration Capacity model (PGF–VIC), seven global reanalyses, and the North American Regional Reanalysis (NARR) over a 5-yr period (2003–07). First, RS and modeled estimates of SM, EF, and LCL are intercompared. Then, emphasis is placed on quantifying RS and modeled differences in convective-season daily correlations between SM–LCL, SM–EF, and EF–LCL for global, regional, and conditional samples. RS is found to yield a substantially weaker state of coupling than model products. However, the rank order of basins by coupling strength calculated from RS and models do roughly agree. Using a mixture of satellite and modeled variables, a map of hybrid coupling strength was produced, which supports the findings of GLACE that transitional zones tend to have the strongest coupling.

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P. R. Field, A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison

Abstract

Identical composite analysis of midlatitude cyclones over oceanic regions has been carried out on both output from the NCAR Community Atmosphere Model, version 3 (CAM3) and multisensor satellite data. By focusing on mean fields associated with a single phenomenon, the ability of the CAM3 to reproduce realistic midlatitude cyclones is critically appraised. A number of perturbations to the control model were tested against observations, including a candidate new microphysics package for the CAM. The new microphysics removes the temperature-dependent phase determination of the old scheme and introduces representations of microphysical processes to convert from one phase to another and from cloud to precipitation species. By subsampling composite cyclones based on systemwide mean strength (mean wind speed) and systemwide mean moisture the authors believe they are able to make meaningful like-with-like comparisons between observations and model output. All variations of the CAM tested overestimate the optical thickness of high-topped clouds in regions of precipitation. Over a system as a whole, the model can both over- and underestimate total high-topped cloud amounts. However, systemwide mean rainfall rates and composite structure appear to be in broad agreement with satellite estimates. When cyclone strength is taken into account, changes in moisture and rainfall rates from both satellite-derived observations and model output as a function of changes in sea surface temperature are in accordance with the Clausius–Clapeyron equation. The authors find that the proposed new microphysics package shows improvement to composite liquid water path fields and cloud amounts.

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B. Poschlod, Ø. Hodnebrog, R. R. Wood, K. Alterskjær, R. Ludwig, G. Myhre, and J. Sillmann

Abstract

Representative methods of statistical disaggregation and dynamical downscaling are compared in terms of their ability to disaggregate precipitation data into hourly resolution in an urban area with complex terrain. The nonparametric statistical Method of Fragments (MoF) uses hourly data from rain gauges to split the daily data at the location of interest into hourly fragments. The high-resolution, convection-permitting Weather Research and Forecasting (WRF) regional climate model is driven by reanalysis data. The MoF can reconstruct the variance, dry proportion, wet hours per month, number and length of wet spells per rainy day, timing of the maximum rainfall burst, and intensities of extreme precipitation with errors of less than 10%. However, the MoF cannot capture the spatial coherence and temporal interday connectivity of precipitation events due to the random elements involved in the algorithm. Otherwise, the statistical method is well suited for filling gaps in subdaily historical records. The WRF Model is able to reproduce dry proportion, lag-1 autocorrelation, wet hours per month, number and length of wet spells per rainy day, spatial correlation, and 6- and 12-h intensities of extreme precipitation with errors of 10% or less. The WRF approach tends to underestimate peak rainfall of 1- and 3-h aggregates but can be used where no observations are available or when areal precipitation data are needed.

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P. R. Field, R. Wood, P. R. A. Brown, P. H. Kaye, E. Hirst, R. Greenaway, and J. A. Smith

Abstract

Ice particle interarrival times have been measured with a fast forward scattering spectrometer probe (FSSP). The distribution of interarrival times is bimodal instead of the exponential distribution expected for a Poisson process. The interarrival time modes are located at ∼10−2 and ∼10−4 s. This equates to horizontal spacings on both the centimeter and meter scales. The characteristics of the interarrival times are well modeled by a Markov chain process that couples together two independent Poisson processes operating at different scales. The possibility that ice crystals shattering on the probe tip causes the bimodal interarrival times is explored and cannot be ruled out. If the observations are indicating real spacings of particles in clouds, then the observations show very localized (centimeter scale) concentrations of ∼100 s cm−3 embedded within an average concentration of typically ∼1 cm−3. If the localized high concentrations are produced by the ice crystals shattering, then the concentration measured by the FSSP is overcounted by a factor of 5 in the worst case measured here, but more typically by a factor of 2. This uncertainty in concentration will adversely affect the predicted radiative influence of these clouds.

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