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H. F. Dacre, P. A. Clark, O. Martinez-Alvarado, M. A. Stringer, and D. A. Lavers

Abstract

The term “atmospheric river” is used to describe corridors of strong water vapor transport in the troposphere. Filaments of enhanced water vapor, commonly observed in satellite imagery extending from the subtropics to the extratropics, are routinely used as a proxy for identifying these regions of strong water vapor transport. The precipitation associated with these filaments of enhanced water vapor can lead to high-impact flooding events. However, there remains some debate as to how these filaments form. In this paper, the authors analyze the transport of water vapor within a climatology of wintertime North Atlantic extratropical cyclones. Results show that atmospheric rivers are formed by the cold front that sweeps up water vapor in the warm sector as it catches up with the warm front. This causes a narrow band of high water vapor content to form ahead of the cold front at the base of the warm conveyor belt airflow. Thus, water vapor in the cyclone’s warm sector, not long-distance transport of water vapor from the subtropics, is responsible for the generation of filaments of high water vapor content. A continuous cycle of evaporation and moisture convergence within the cyclone replenishes water vapor lost via precipitation. Thus, rather than representing a direct and continuous feed of moist air from the subtropics into the center of a cyclone (as suggested by the term “atmospheric river”), these filaments are, in fact, the result of water vapor exported from the cyclone, and thus they represent the footprints left behind as cyclones travel poleward from the subtropics.

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P. A. Clark, C. E. Halliwell, and D. L. A. Flack

Abstract

We present a simple, physically consistent stochastic boundary layer scheme implemented in the Met Office’s Unified Model. It is expressed as temporally correlated multiplicative Poisson noise with a distribution that depends on physical scales. The distribution can be highly skewed at convection-permitting scales (horizontal grid lengths around 1 km) when temporal correlation is far more important than spatial. The scheme is evaluated using small ensemble forecasts of two case studies of severe convective storms over the United Kingdom. Perturbations are temporally correlated over an eddy-turnover time scale, and may be similar in magnitude to or larger than the mean boundary layer forcing. However, their mean is zero and hence they, in practice, they have very little impact on the energetics of the forecast, so overall domain-averaged precipitation, for example, is essentially unchanged. Differences between ensemble members grow; after around 12 h they appear to be roughly saturated; this represents the time scale to achieve a balance between addition of new perturbations, perturbation growth, and dissipation, not just saturation of initial perturbations. The scheme takes into account the area chosen to average over, and results are insensitive to this area at least where this remains within an order of magnitude of the grid scale.

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Pablo A. Mendoza, Balaji Rajagopalan, Martyn P. Clark, Kyoko Ikeda, and Roy M. Rasmussen

Abstract

Statistical postprocessing techniques have become essential tools for downscaling large-scale information to the point scale, and also for providing a better probabilistic characterization of hydrometeorological variables in simulation and forecasting applications at both short and long time scales. In this paper, the authors assess the utility of statistical postprocessing methods for generating probabilistic estimates of daily precipitation totals, using deterministic high-resolution outputs obtained with the Weather Research and Forecasting (WRF) Model. After a preliminary assessment of WRF simulations over a historical period, the performance of three postprocessing techniques is compared: multinomial logistic regression (MnLR), quantile regression (QR), and Bayesian model averaging (BMA)—all of which use WRF outputs as potential predictors. Results demonstrate that the WRF Model has skill in reproducing observed precipitation events, especially during fall/winter. Furthermore, it is shown that the spatial distribution of skill obtained from statistical postprocessing is closely linked with the quality of WRF precipitation outputs. A detailed comparison of statistical precipitation postprocessing approaches reveals that, although the poorest performance was obtained using MnLR, there is not an overall best technique. While QR should be preferred if skill (i.e., small probability forecast errors) and reliability (i.e., match between forecast probabilities and observed frequencies) are target properties, BMA is recommended in cases when discrimination (i.e., prediction of occurrence versus nonoccurrence) and statistical consistency (i.e., equiprobability of the observations within their ensemble distributions) are desired. Based on the results obtained here, the authors believe that future research should explore frameworks reconciling hierarchical Bayesian models with the use of the extreme value theory for high precipitation events.

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Sergey Frolov, Carolyn A. Reynolds, Michael Alexander, Maria Flatau, Neil P. Barton, Patrick Hogan, and Clark Rowley

Abstract

Patterns of correlations between the ocean and the atmosphere are examined using a high-resolution (1/12° ocean and ice, and 1/3° atmosphere) ensemble of data assimilative, coupled, global, ocean-atmosphere forecasts. This provides a unique perspective into atmosphere-ocean interactions constrained by assimilated observations, allowing for the contrast of patterns of coupled processes across regions and the examination of processes affected by ocean mesoscale eddies. Correlations during the first 24 hours of the coupled forecast between ocean surface temperature and atmospheric variables, and between ocean mixed layer depth and surface winds are examined as a function of region and season. Three distinct coupling regimes emerge: (1) regions characterized by strong sea surface temperature fronts, where uncertainty in the ocean mesoscale influences ocean-atmosphere exchanges; (2) regions with intense atmospheric convection over the tropical oceans, where uncertainty in the modeled atmospheric convection impacts the upper ocean; and (3) regions where the depth of the seasonal mixed layer (MLD) determines the magnitude of the coupling, which is stronger when the MLD is shallow and weaker when the MLD is deep. A comparison with models at lower horizontal (1/12? vs. 1? and 1/4? ) and vertical (1-meter vs. 10-meter depth of the first layer) ocean resolution reveals that coupling in the boundary currents, the Tropical Indian Ocean, and the Warm Pool regions requires high levels of horizontal and vertical resolution. Implications for coupled data assimilation and short-term forecasting are discussed.

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James D. Doyle, Clark Amerault, Carolyn A. Reynolds, and P. Alex Reinecke

Abstract

The sensitivity and predictability of a rapidly developing extratropical cyclone, Xynthia, that had a severe impact on Europe is explored using a high-resolution moist adjoint modeling system. The adjoint diagnostics indicate that the intensity of severe winds associated with the front just prior to landfall was particularly sensitive to perturbations in the moisture and temperature fields and to a lesser degree the wind fields. The sensitivity maxima are found in the low- and midlevels, oriented in a sloped region along the warm front, and maximized within the warm conveyor belt. The moisture sensitivity indicates that only a relatively small filament of moisture within an atmospheric river present at the initial time was critically important for the development of Xynthia. Adjoint-based optimal perturbations introduced into the tangent linear and nonlinear models exhibit rapid growth over 36 h, while initial perturbations of the opposite sign show substantial weakening of the low-level jet and a marked reduction in the spatial extent of the strong low-level winds. The sensitivity fields exhibit an upshear tilt along the sloping warm conveyor belt and front, and the perturbations extract energy from the mean flow as they are untilted by the shear, consistent with the PV unshielding mechanism. The results of this study underscore the need for accurate moisture observations and data assimilation systems that can adequately assimilate these observations in order to reduce the forecast uncertainties for these severe extratropical cyclones. However, given the nature of the sensitivities and the potential for rapid perturbation and error growth, the intrinsic predictability of severe cyclones such as Xynthia is likely limited.

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Jennifer C. Adam, Elizabeth A. Clark, Dennis P. Lettenmaier, and Eric F. Wood

Abstract

Underestimation of precipitation in topographically complex regions plagues most gauge-based gridded precipitation datasets. Gauge locations are usually in or near population centers, which tend to lie at low elevations relative to the surrounding terrain. For hydrologic modeling purposes, the resulting bias can result in serious underprediction of observed flows. A hydrologic water balance approach to develop a globally consistent correction for the underestimation of gridded precipitation in mountainous regions is described. The adjustment is based on a combination of the catchment water balances and variations of the Budyko E/P versus/P curve. The method overlays streamflow measurements onto watershed boundaries and then performs watershed water balances to determine “true” precipitation. Rather than relying on a modeled runoff ratio, evaporation is estimated using the Budyko curves. The average correction ratios for each of 357 mountainous river basins worldwide are spatially distributed across the basins and are then interpolated to ungauged areas. Following application of adjustments for precipitation catch deficiencies, the correction ratios are used to scale monthly precipitation from an existing monthly global dataset (1979–99, 0.5° resolution). The correction for orographic effects resulted in a net increase in global terrestrial precipitation of 6.2% (20.2% in orographically influenced regions only) for the 1979–99 climatology. The approach developed here is applicable to any precipitation dataset in regions where good streamflow data exist. As a cautionary note, the correction factors are dataset dependent, and therefore the adjustments are strictly applicable only to the data from which they were derived.

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Elizabeth A. Clark, Justin Sheffield, Michelle T. H. van Vliet, Bart Nijssen, and Dennis P. Lettenmaier

Abstract

A common term in the continental and oceanic components of the global water cycle is freshwater discharge to the oceans. Many estimates of the annual average global discharge have been made over the past 100 yr with a surprisingly wide range. As more observations have become available and continental-scale land surface model simulations of runoff have improved, these past estimates are cast in a somewhat different light. In this paper, a combination of observations from 839 river gauging stations near the outlets of large river basins is used in combination with simulated runoff fields from two implementations of the Variable Infiltration Capacity land surface model to estimate continental runoff into the world’s oceans from 1950 to 2008. The gauges used account for ~58% of continental areas draining to the ocean worldwide, excluding Greenland and Antarctica. This study estimates that flows to the world’s oceans globally are 44 200 (±2660) km3 yr−1 (9% from Africa, 37% from Eurasia, 30% from South America, 16% from North America, and 8% from Australia–Oceania). These estimates are generally higher than previous estimates, with the largest differences in South America and Australia–Oceania. Given that roughly 42% of ocean-draining continental areas are ungauged, it is not surprising that estimates are sensitive to the land surface and hydrologic model (LSM) used, even with a correction applied to adjust for model bias. The results show that more and better in situ streamflow measurements would be most useful in reducing uncertainties, in particular in the southern tip of South America, the islands of Oceania, and central Africa.

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Konstantinos M. Andreadis, Elizabeth A. Clark, Andrew W. Wood, Alan F. Hamlet, and Dennis P. Lettenmaier

Abstract

Droughts can be characterized by their severity, frequency and duration, and areal extent. Depth–area–duration analysis, widely used to characterize precipitation extremes, provides a basis for the evaluation of drought severity when storm depth is replaced by an appropriate measure of drought severity. Gridded precipitation and temperature data were used to force a physically based macroscale hydrologic model at 1/2° spatial resolution over the continental United States, and construct a drought history from 1920 to 2003 based on the model-simulated soil moisture and runoff. A clustering algorithm was used to identify individual drought events and their spatial extent from monthly summaries of the simulated data. A series of severity–area–duration (SAD) curves were constructed to relate the area of each drought to its severity. An envelope of the most severe drought events in terms of their SAD characteristics was then constructed. The results show that (a) the droughts of the 1930s and 1950s were the most severe of the twentieth century for large areas; (b) the early 2000s drought in the western United States is among the most severe in the period of record, especially for small areas and short durations; (c) the most severe agricultural droughts were also among the most severe hydrologic droughts, however, the early 2000s western U.S. drought occupies a larger portion of the hydrologic drought envelope curve than does its agricultural companion; and (d) runoff tends to recover in response to precipitation more quickly than soil moisture, so the severity of hydrologic drought during the 1930s and 1950s was dampened by short wet spells, while the severity of the early 2000s drought remained high because of the relative absence of these short-term phenomena.

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Mark C. Serreze, Martyn P. Clark, David L. McGinnis, and David A. Robinson

Abstract

Monthly data from 206 stations for the period 1947–93 are used to examine characteristics of snowfall over the eastern half of the United States and relationships with precipitation and the maximum temperature on precipitation days. Linkages between snowfall and modes of low-frequency circulation variability are diagnosed through composite analyses, based on results from a rotated Principal Component Analysis (PCA) of monthly 500-hPa geopotential height. Results are examined for the 2-month windows of November–December, January–February, and March–April. The three dominant PCAs for each window capture regional components of the Pacific–North American (PNA), Tropical-Northern Hemisphere (TNH), and east Pacific (EP) teleconnection patterns.

Two general snowfall regimes are identified: 1) the dry and cold upper midwest, Nebraska and Kansas, where snowfall is strongly a function of precipitation; and 2) the Midwest, southeast, and northeast, where snowfall is more closely tied to the mean maximum temperature on precipitation days. The PNA (the dominant circulation mode) and the EP pattern are both associated with strong snowfall signals, best expressed for November–December and January–February. Snowfall for the PNA over the southeast, midwest, and mid-Atlantic states increases (decreases) under positive (negative) extremes, when the eastern United States is dominated by a strong 500-hPa trough (zonal flow or weak ridge) with associated lower (higher) precipitation-day temperatures. Snowfall signals are more extensive under positive PNA extremes where the lower temperatures have a greater impact on precipitation phase. An opposing precipitation-controlled snowfall signal is found over the upper Midwest. The positive phase of the EP pattern, describing a western ridge–eastern trough, is associated with negative snowfall signals clustered over the midwest and upper midwest. Opposing signals are found under the midwestern trough–eastern ridge pattern of the negative mode. These signals are primarily precipitation controlled, which for the Midwest are counter to the climatological control by temperature. TNH snowfall signals are fairly weak except for March–April, when significant differences are found for the upper Midwest and from Missouri northeast into New England. No coherent trends are observed in snowfall or in the strength of the circulation patterns derived from the PCA.

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J. D. Price, S. Vosper, A. Brown, A. Ross, P. Clark, F. Davies, V. Horlacher, B. Claxton, J. R. McGregor, J. S. Hoare, B. Jemmett-Smith, and P. Sheridan

During stable nighttime periods, large variations in temperature and visibility often occur over short distances in regions of only moderate topography. These are of great practical significance and yet pose major forecasting challenges because of a lack of detailed understanding of the processes involved and because crucial topographic variations are often not resolved in current forecast models. This paper describes a field and numerical modeling campaign, Cold-Air Pooling Experiment (COLPEX), which addresses many of the issues.

The observational campaign was run for 15 months in Shropshire, United Kingdom, in a region of small hills and valleys with typical ridge–valley heights of 75–150 m and valley widths of 1–3 km. The instrumentation consisted of three sites with instrumented flux towers, a Doppler lidar, and a network of 30 simpler meteorological stations. Further instrumentation was deployed during intensive observation periods including radiosonde launches from two sites, a cloud droplet probe, aerosol monitoring equipment, and an instrumented car. Some initial results from the observations are presented illustrating the range of conditions encountered.

The modeling phase of COLPEX includes use of the Met Office Unified Model at 100-m resolution, and some brief results for a simulation of an intensive observation period are presented showing the model capturing a cold-pool event. As well as aiding interpretation of the observations, results from this study are expected to inform the design of future generations of operational forecasting systems

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