Search Results

You are looking at 1 - 10 of 21 items for

  • Author or Editor: Matthew Miller x
  • All content x
Clear All Modify Search
Matthew J. Bunkers, James R. Miller Jr., and Arthur T. DeGaetano

Abstract

Spatially homogeneous climate regions were developed from long-term monthly temperature and precipitation data for a subset of the U.S. Northern Plains. Climate regions were initially defined using the “best” of three agglomerative and hierarchical clustering methodologies, then the clusters were objectively modified using a “pseudohierarchical” iterative improvement technique. Under the premise of hierarchical cluster analysis, once an object has been assigned to a cluster, it cannot later he reassigned to a different cluster, even if it is statistically desirable. The objective modification technique used herein is employed to compensate for this problem.

Principal component analysis (PCA) was used to reduce a 147-station dataset, consisting of 24 climatic variables averaged over the 1931–1990 period, to three orthogonal components. The new standardized mars, which explain 93% of the original dataset variance, were then subjected to the Ward's, average linkage, and complete linkage clustering methods. The average linkage method produced the most representative statistical results in identifying the climate regions. An iterative improvement technique was then utilized to test “border station” membership and to modify the climate region houses. Fifteen climate regions resulted from the clustering (with two single-station clusters in the Black Hills alone), although they age just one possible partitioning of the data. The within-cluster variability is generally the same for the 15 climate regions and the corresponding 21 National Climatic Data Center (NCM) climate divisions. However, since data within-cluster variability tends to decrease with increasing cluster number, this result favors the new climate regions. Additionally, the new climate regions am shown to be superior to the NCDC climate, divisions in wont of between-cluster variability. These results suggest that the NCDC climate divisions could be redefined, improving their climatic homogeneity.

Full access
Amy Solomon, Matthew D. Shupe, and Nathaniel B. Miller

Abstract

Regional model simulations of the 10–13 July 2012 extreme melt event over the Greenland Ice Sheet (GIS) are used to investigate how low-level liquid-bearing clouds impact surface energy fluxes, and therefore the energy available for melt. A sensitivity study in which the radiation code is modified so that cloud liquid and ice do not emit, absorb, or reflect radiation is used to identify cloud impacts beyond the cloud radiative effect. It is found that Arctic mixed-phase stratocumuli are not produced in the sensitivity experiment, highlighting that cloud radiative fluxes are required to maintain the clouds. A number of feedbacks are found that damp the warming effect of the clouds. Thin mixed-phase clouds increase the downward longwave fluxes by 100 W m−2, but upward daytime surface longwave fluxes increase by 20 W m−2 (60 W m−2 at night) and net shortwave fluxes decrease by 40 W m−2 (partially due to a 0.05 increase in surface albedo), leaving only 40 W m−2 available for melt. This 40 W m−2 is distributed between the turbulent and conductive ground fluxes, so it is only at times of weak turbulent fluxes (i.e., at night or during melt) that this energy goes into the conductive ground flux, providing energy for melt. From these results it is concluded that it is the integrated impact of the clouds over the diurnal cycle (the preconditioning of the snowpack by the clouds at night) that made melt possible during this 3-day period. These findings are extended to understand the pattern of melt observed over the GIS.

Full access
Matthew J. Bunkers, James R. Miller Jr., and Arthur T. DeGaetano

Abstract

Monthly total precipitation and mean temperature data records extending from the late nineteenth century to 1990 were collected for 147 stations in South Dakota, North Dakota, and portions of adjacent states and provinces. This region, defined as the Northern Plains region (NPR), was examined for patterns associated with the warm phase (ENSO) and the cold phase (LNSO) of the Southern Oscillation to elucidate some of the debate concerning a signal in this area. Based on a correlation analysis, the NPR was treated as having one spatial degree of freedom.

Using Monte Carlo simulations of the Student's t-test statistic, four seasons with significant changes in mean precipitation or temperature during either ENSO or LNSO were identified. A highly significant signal was evident during the ENSO April to October season for precipitation, where the mean precipitation increased 7.21 cm for the 23 events studied. Here 20 of these 23 ENSO events exhibited precipitation above the median value, and 14 of the 23 events were in the upper quartile. In contrast, a strong signal for decreased LNSO precipitation was noted where May to August precipitation averaged 3.91 cm lower during the 17 events, with similar significance values. Complementing the enhanced ENSO warm season precipitation, the August to October ten-iperatme decreased by 2.17°C, with a significant number of events in both the lowest half and lowest quartile. Finally, temperature averaged 4.67°C cooler during LNSO winters. These results will be useful for limited-season prediction of precipitation and temperature tendencies across the NPR.

It is interesting to note that the initial ENSO years did not reveal a significant temperature increase during the NPR winter, which is in contrast to similar studies. However, by slightly modifying the years that were classified as ENSO years, a significant winter temperature response was indicated. This suggests that there is a tendency for warmer NPR winters during ENSO; however, this was not statistically significant.

Full access
Michael R. Gallagher, Matthew D. Shupe, and Nathaniel B. Miller

Abstract

The Greenland Ice Sheet (GrIS) plays a crucial role in the Arctic climate, and atmospheric conditions are the primary modifier of mass balance. This analysis establishes the relationship between large-scale atmospheric circulation and principal determinants of GrIS mass balance: moisture, cloud properties, radiative forcing, and temperature. Using self-organizing maps (SOMs), observations from the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) project are categorized by daily sea level pressure (SLP) gradient. The results describe in detail how southerly, northerly, and zonal circulation regimes impact observations at Summit Station, Greenland. This southerly regime is linked to large anomalous increases in low-level liquid cloud formation, cloud radiative forcing (CRF), and surface warming at Summit Station. An individual southerly pattern relates to the largest positive anomalies, with the most extreme 25% of cases leading to CRF anomalies above 21 W m−2 and temperature anomalies beyond 8.5°C. Finally, the July 2012 extreme melt event is analyzed, showing that the prolonged ice sheet warming was related to persistence of these southerly circulation patterns, causing an unusually extended period of anomalous CRF and temperature. These results demonstrate a novel methodology, connecting daily atmospheric circulation to a relatively brief record of observations.

Full access
David B. Mechem, Carly S. Wittman, Matthew A. Miller, Sandra E. Yuter, and Simon P. de Szoeke

Abstract

Marine boundary layer clouds are modified by processes at different spatial and temporal scales. To isolate the processes governing aerosol–cloud–precipitation interactions, multiday synoptic variability of the environment must be accounted for. Information on the location of low clouds relative to the ridge–trough pattern gives insight into how cloud properties vary as a function of environmental subsidence and stability. The technique of self-organizing maps (SOMs) is employed to objectively classify the 500-hPa geopotential height patterns for 33 years of reanalysis fields (ERA-Interim) into pretrough, trough, posttrough, ridge, and zonal-flow categories. The SOM technique is applied to a region of prevalent marine low cloudiness over the eastern North Atlantic Ocean that is centered on the Azores island chain, the location of a long-term U.S. Department of Energy observation site. The Azores consistently lie in an area of substantial variability in synoptic configuration, thermodynamic environment, and cloud properties. The SOM method was run in two ways to emphasize multiday and seasonal variability separately. Over and near the Azores, there is an east-to-west sloshing back and forth of the western edge of marine low clouds associated with different synoptic states. The different synoptic states also exhibit substantial north–south variability in the position of high clouds. For any given month of the year, there is large year-to-year variability in the occurrence of different synoptic states. Hence, estimating the climatological behavior of clouds from short-term field campaigns has large uncertainties. This SOM approach is a robust method that is broadly applicable to characterizing synoptic regimes for any location.

Full access
Steven D. Miller, Fang Wang, Ann B. Burgess, S. McKenzie Skiles, Matthew Rogers, and Thomas H. Painter

Abstract

Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.

Full access
Matthew D. Parker, Brett S. Borchardt, Rachel L. Miller, and Conrad L. Ziegler

Abstract

The 25–26 June 2015 nocturnal mesoscale convective system (MCS) from the Plains Elevated Convection at Night (PECAN) field project produced severe winds within an environment that might customarily be associated with elevated convection. This work incorporates both a full-physics real-world simulation and an idealized single-sounding simulation to explore the MCS’s evolution. Initially, the simulated convective systems were elevated, being maintained by wavelike disturbances and lacking surface cold pools. As the systems matured, surface outflows began to appear, particularly where heavy precipitation was occurring, with air in the surface cold pools originating from up to 4–5 km AGL. Via this progression, the MCSs exhibited a degree of self-organization (i.e., structures that are dependent upon an MCS’s particular history). The cold pools eventually became 1.5–3.5 km deep, by which point passive tracers revealed that the convection was at least partly surface based. Soon after becoming surface based, both simulations produced severe surface winds, the strongest of which were associated with embedded low-level mesovortices and their attendant outflow surges and bowing segments. The origin of the simulated mesovortices was likely the downward tilting of system-generated horizontal vorticity (from baroclinity, but also possibly friction) within the simulated MCSs’ outflow, as has been argued in a number of previous studies. Taken altogether, it appears that severe nocturnal MCSs may often resemble their cold pool-driven, surface-based afternoon counterparts.

Free access
Adrien Lacour, Helene Chepfer, Matthew D. Shupe, Nathaniel B. Miller, Vincent Noel, Jennifer Kay, David D. Turner, and Rodrigo Guzman

Abstract

Spaceborne lidar observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite provide the first-ever observations of cloud vertical structure and phase over the entire Greenland Ice Sheet. This study leverages CALIPSO observations over Greenland to pursue two investigations. First, the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) observations are compared with collocated ground-based radar and lidar observations at Summit, Greenland. The liquid cloud cover agrees well between the spaceborne and ground-based observations. In contrast, ground–satellite differences reach 30% in total cloud cover and 40% in cloud fraction below 2 km above ground level, due to optically very thin ice clouds (IWC < 2.5 × 10−3 g m−3) missed by CALIPSO-GOCCP. Those results are compared with satellite cloud climatologies from the GEWEX cloud assessment. Most passive sensors detect fewer clouds than CALIPSO-GOCCP and the Summit ground observations, due to different detection methods. Second, the distribution of clouds over the Greenland is analyzed using CALIPSO-GOCCP. Central Greenland is the cloudiest area in summer, at +7% and +4% above the Greenland-wide average for total and liquid cloud cover, respectively. Southern Greenland contains free-tropospheric thin ice clouds in all seasons and liquid clouds in summer. In northern Greenland, fewer ice clouds are detected than in other areas, but the liquid cloud cover seasonal cycle in that region drives the total Greenland cloud annual variability with a maximum in summer. In 2010 and 2012, large ice-sheet melting events have a positive liquid cloud cover anomaly (from +1% to +2%). In contrast, fewer clouds (−7%) are observed during low ice-sheet melt years (e.g., 2009).

Full access
Anson H. Cheung, Michael E. Mann, Byron A. Steinman, Leela M. Frankcombe, Matthew H. England, and Sonya K. Miller

Abstract

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al.

Full access
Anson H. Cheung, Michael E. Mann, Byron A. Steinman, Leela M. Frankcombe, Matthew H. England, and Sonya K. Miller

Abstract

Low-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (>20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low-frequency time scales (>20 yr in the NA; >40 yr in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal-to-interdecadal variability in the NH, whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low-frequency ICV, which may help improve attribution and decadal prediction.

Full access