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Graeme L. Stephens
and
Norman B. Wood

Abstract

This paper describes the results of analysis of over 825 000 profiles of millimeter-wave radar (MWR) reflectivities primarily collected by zenith-pointing surface radars observing tropical convection associated with various phases of activity of the large-scale tropical circulation. The data principally analyzed in this paper come from surface observations obtained at the Atmospheric Radiation Measurement Manus site during active and break episodes of the Madden–Julian oscillation (MJO) and from observations collected from a shipborne radar during an active phase of the monsoon over the Indian Ocean during the Joint Air–Sea Monsoon Interaction Experiment. It was shown, for example, in a histogram regime analysis that the MWR data produce statistics on convection regimes similar in most respects to the analogous regime analysis of the Tropical Rainfall Measuring Mission radar–radiometer observations. Attenuation of the surface MWRs by heavy precipitation, however, incorrectly shifts a small fraction of the deeper precipitation modes into the shallow modes of precipitation. The principal findings are the following. (i) The cloud and precipitation structures of the different convective regimes are largely identical regardless of the mode of synoptic forcing, that is, regardless of whether the convection occurred during an active phase of the MJO, a transition phase of the MJO, or in an active monsoon period. What changes between these synoptically forced modes of convection are the relative frequencies of occurrences of the different storm regimes. (ii) The cloud structures associated with the majority of cases of observed precipitation (ranging in occurrence from 45% to 53% of all precipitation profiles) were multilayered structures regardless of the mode of synoptic forcing. The predominant multilayered cloud mode was of higher-level cirrus of varying thickness overlying cumulus congestus–like convection. (iii) The majority of water accumulated (i.e., 53%–63%) over each of the periods assigned to the active monsoon (5 days of data), the active MJO (38 days of data), and the transition MJO (53 days of data) fell from these multiple-layered cloud systems. (iv) Solar transmittances reveal that significantly less sunlight (reductions of about 30%–50%) reaches the surface in the precipitating regimes than reaches the surface under drizzle and cloud-only conditions, suggesting that the optical thicknesses of precipitation-bearing clouds significantly exceeds those of nonprecipitating clouds.

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Norman B. Wood
,
Philip M. Gabriel
, and
Graeme L. Stephens

Abstract

The role of horizontal inhomogeneity in radiative transfer through cloud fields is investigated within the context of the two-stream approximation. Spatial correlations between cloud optical properties and the radiance field are introduced in the three-dimensional radiative transfer equation and lead to a two-stream model in which the correlations are represented by parameterizations. The behavior of the model is examined using simple single-layer single-column atmospheres. Positive correlations between extinction or scattering and the radiance field are shown to decrease transmission, increase reflection, and increase absorption within inhomogeneous media. The parameterization is used to evaluate the characteristics of inhomogeneous cloud fields observed by radar and lidar over a number of different locations and seasons, revealing that shortwave transfer is generally characterized by negative correlations between extinction and radiance, while longwave transfer is characterized by positive correlations. The results from this characterization are applied to the integration of an atmospheric general circulation model. Model surface temperatures are significantly affected, largely in response to changes in downwelling radiative fluxes at the surface induced by changes in cloud cover and water vapor distributions.

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Susan C. van den Heever
,
Graeme L. Stephens
, and
Norman B. Wood

Abstract

The impacts of enhanced aerosol concentrations such as those associated with dust intrusions on the trimodal distribution of tropical convection have been investigated through the use of large-domain (10 000 grid points), fine-resolution (1 km), long-duration (100 days), two-dimensional idealized cloud-resolving model simulations conducted under conditions of radiative–convective equilibrium (RCE). The focus of this research is on those aerosols that serve primarily as cloud condensation nuclei (CCN). The results demonstrate that the large-scale organization of convection, the domain-averaged precipitation, and the total cloud fraction show only show a weak response to enhanced aerosol concentrations. However, while the domainwide responses to enhanced aerosol concentrations are weak, aerosol indirect effects on the three tropical cloud modes are found to be quite significant and often opposite in sign, a fact that appears to contribute to the weaker domain response. The results suggest that aerosol indirect effects associated with shallow clouds may offset or compensate for the aerosol indirect effects associated with congestus and deep convection systems and vice versa, thus producing a more moderate domainwide response to aerosol indirect forcing. Finally, when assessing the impacts of aerosol indirect forcing associated with CCN on the characteristics of tropical convection, several aspects need to be considered, including which cloud mode or type is being investigated, the field of interest, and whether localized or systemwide responses are being examined.

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Graeme L. Stephens
,
Norman B. Wood
, and
Philip M. Gabriel

Abstract

Different approaches for parameterizing the effects of vertical variability of cloudiness on radiative transfer are assessed using a database constructed from observations derived from lidar and millimeter cloud radar data collected from three different locations. Five different methods for dealing with the vertical overlap of clouds were incorporated into a single radiation model that was applied to the lidar/radar data averaged in time. The calculated fluxes and heating rates derived with this model are compared to broadband fluxes and heating rates calculated with the independent column approximation using the time-resolved cloud data. These comparisons provide a way of evaluating the effects of different overlap assumptions on the calculation of domain-mean fluxes. It was demonstrated how two of the most commonly used overlap schemes, the random and maximum-random methods, suffer a severe problem in that the total cloud amount defined by these methods depends on the vertical resolution of the host model thus creating a vertical-resolution-dependent bias in model total cloudiness and radiative fluxes. A new method is introduced to overcome this problem by preserving the total column cloud amount.

Despite these problems, the comparisons presented show that most methods introduce a relatively small bias with respect to the single-column data. This is largely a consequence of the nature of the cloud cover statistics associated with the lidar/radar observations used in this study and might not apply in general. Among the three best-performing methods (random, overcast random, and maximum random), the more commonly used maximum-random method does not perform significantly better than the other two methods with regard to both bias and rms error despite its relative high computational cost. The comparisons also reveal the nature and magnitude of the random errors that are introduced by the subgrid-scale parameterizations. These random errors are large and an inevitable consequence of the parameterization process that treats cloud structure statistically. These errors may be thought of as a source of noise to the general circulation model in which the parameterization is embedded.

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Stephen J. Déry
,
Marc Stieglitz
,
Åsa K. Rennermalm
, and
Eric F. Wood

Abstract

A water budget study that considers precipitation, river runoff, evapotranspiration, and soil moisture for the Kuparuk River basin on the North Slope of Alaska is presented. Numerical simulations of hydrologic processes using the NASA Catchment-based Land Surface Model are conducted for the period 1991–2001 and provide the partitioning of the observed precipitation input (292 mm yr−1) onto the basin into river discharge (169 mm yr−1), evapotranspiration (127 mm yr−1), and an increase in soil moisture (1 mm yr−1). Discharge attains its annual peak during snowmelt and disposes 58% of the annual precipitation. Evapotranspiration contributes another 43% to the water budget and is mainly associated with warm summertime conditions and a snow-free surface. Combined, surface-snow and blowing-snow sublimation contribute only 5% of the total annual evaporative fluxes. Soil moisture recharge is associated with snowmelt during spring and rainfall during late summer and early fall, whereas soil drying accompanies high evapotranspiration rates during summer. An analysis of interannual variability in the water budget shows that warm, dry years favor a relatively more intense response of river discharge and evapotranspiration to the precipitation input, whereas cool, wet years tend to augment soil moisture.

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Stephen J. Déry
,
Wade T. Crow
,
Marc Stieglitz
, and
Eric F. Wood

Abstract

The small-scale (10 to 100 m) and local-scale (100 m to 10 km) effects of topography (elevation, slope, and aspect) and snow redistribution by wind on the evolution of the snowmelt are investigated. The chosen study area is the 142 km2 Upper Kuparuk River basin located on the North Slope of Alaska. Two land surface models (LSMs) designed for regional and global climate studies apply different techniques to resolve these additional processes and features and their effects on snowmelt. One model uses a distributed approach to simulate explicitly the effects of topography on snowmelt at a 131-m resolution across the entire Upper Kuparuk watershed. By contrast, the other LSM employs a simple parameterization to implicitly resolve the effects of wind-blown snow on the hydrology of the Upper Kuparuk basin. In both cases, the incorporation of these local- and small-scale features within the LSMs leads to significant heterogeneity in the 1997 end-of-winter spatial distribution of snow cover in the Upper Kuparuk watershed. It is shown that the consideration of subgrid-scale snow-cover heterogeneity over complex Arctic terrain provides a better representation of the end-of-winter snow water equivalent, an improved simulation of the timing and amount of water discharge of the Upper Kuparuk River, and an alteration of other surface energy and water budget components.

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Randy J. Chase
,
Stephen W. Nesbitt
,
Greg M. McFarquhar
,
Norman B. Wood
, and
Gerald M. Heymsfield

Abstract

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

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Gail Skofronick-Jackson
,
Mark Kulie
,
Lisa Milani
,
Stephen J. Munchak
,
Norman B. Wood
, and
Vincenzo Levizzani

Abstract

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

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Stephen J. Déry
,
Marc Stieglitz
,
Edward C. McKenna
, and
Eric F. Wood

Abstract

The characteristics and trends of observed river discharge into the Hudson, James, and Ungava Bays (HJUBs) for the period 1964–2000 are investigated. Forty-two rivers with outlets into these bays contribute on average 714 km3 yr−1 [= 0.023 Sv (1 Sv ≡ 106 m3 s−1)] of freshwater to high-latitude oceans. For the system as a whole, discharge attains an annual peak of 4.2 km3 day−1 on average in mid-June, whereas the minimum of 0.68 km3 day−1 occurs on average during the last week of March. The Nelson River contributes as much as 34% of the daily discharge for the entire system during winter but diminishes in relative importance during spring and summer. Runoff rates per contributing area are highest (lowest) on the eastern (western) shores of the Hudson and James Bays. Linear trend analyses reveal decreasing discharge over the 37-yr period in 36 out of the 42 rivers. By 2000, the total annual freshwater discharge into HJUBs diminished by 96 km3 (−13%) from its value in 1964, equivalent to a reduction of 0.003 Sv. The annual peak discharge rate associated with snowmelt has advanced by 8 days between 1964 and 2000 and has diminished by 0.036 km3 day−1 in intensity. There is a direct correlation between the timing of peak spring discharge rates and the latitude of a river’s mouth; the spring freshet varies by 5 days for each degree of latitude. Continental snowmelt induces a seasonal pulse of freshwater from HJUBs that is tracked along its path into the Labrador Current. It is suggested that the annual upper-ocean salinity minimum observed on the inner Newfoundland Shelf can be explained by freshwater pulses composed of meltwater from three successive winter seasons in the river basins draining into HJUBs. A gradual salinization of the upper ocean during summer over the period 1966–94 on the inner Newfoundland Shelf is in accord with a decadal trend of a diminishing intensity in the continental meltwater pulses.

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Louise Arnal
,
Andrew W. Wood
,
Elisabeth Stephens
,
Hannah L. Cloke
, and
Florian Pappenberger

Abstract

Seasonal streamflow prediction skill can derive from catchment initial hydrological conditions (IHCs) and from the future seasonal climate forecasts (SCFs) used to produce the hydrological forecasts. Although much effort has gone into producing state-of-the-art seasonal streamflow forecasts from improving IHCs and SCFs, these developments are expensive and time consuming and the forecasting skill is still limited in most parts of the world. Hence, sensitivity analyses are crucial to funnel the resources into useful modeling and forecasting developments. It is in this context that a sensitivity analysis technique, the variational ensemble streamflow prediction assessment (VESPA) approach, was recently introduced. VESPA can be used to quantify the expected improvements in seasonal streamflow forecast skill as a result of realistic improvements in its predictability sources (i.e., the IHCs and the SCFs)—termed “skill elasticity”—and to indicate where efforts should be targeted. The VESPA approach is, however, computationally expensive, relying on multiple hindcasts having varying levels of skill in IHCs and SCFs. This paper presents two approximations of the approach that are computationally inexpensive alternatives. These new methods were tested against the original VESPA results using 30 years of ensemble hindcasts for 18 catchments of the contiguous United States. The results suggest that one of the methods, end point blending, is an effective alternative for estimating the forecast skill elasticities yielded by the VESPA approach. The results also highlight the importance of the choice of verification score for a goal-oriented sensitivity analysis.

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