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James B. Edson
,
Venkata Jampana
,
Robert A. Weller
,
Sebastien P. Bigorre
,
Albert J. Plueddemann
,
Christopher W. Fairall
,
Scott D. Miller
,
Larry Mahrt
,
Dean Vickers
, and
Hans Hersbach
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David W. Pierce
,
Daniel R. Cayan
,
Tapash Das
,
Edwin P. Maurer
,
Norman L. Miller
,
Yan Bao
,
M. Kanamitsu
,
Kei Yoshimura
,
Mark A. Snyder
,
Lisa C. Sloan
,
Guido Franco
, and
Mary Tyree

Abstract

Climate model simulations disagree on whether future precipitation will increase or decrease over California, which has impeded efforts to anticipate and adapt to human-induced climate change. This disagreement is explored in terms of daily precipitation frequency and intensity. It is found that divergent model projections of changes in the incidence of rare heavy (>60 mm day−1) daily precipitation events explain much of the model disagreement on annual time scales, yet represent only 0.3% of precipitating days and 9% of annual precipitation volume. Of the 25 downscaled model projections examined here, 21 agree that precipitation frequency will decrease by the 2060s, with a mean reduction of 6–14 days yr−1. This reduces California's mean annual precipitation by about 5.7%. Partly offsetting this, 16 of the 25 projections agree that daily precipitation intensity will increase, which accounts for a model average 5.3% increase in annual precipitation. Between these conflicting tendencies, 12 projections show drier annual conditions by the 2060s and 13 show wetter. These results are obtained from 16 global general circulation models downscaled with different combinations of dynamical methods [Weather Research and Forecasting (WRF), Regional Spectral Model (RSM), and version 3 of the Regional Climate Model (RegCM3)] and statistical methods [bias correction with spatial disaggregation (BCSD) and bias correction with constructed analogs (BCCA)], although not all downscaling methods were applied to each global model. Model disagreements in the projected change in occurrence of the heaviest precipitation days (>60 mm day−1) account for the majority of disagreement in the projected change in annual precipitation, and occur preferentially over the Sierra Nevada and Northern California. When such events are excluded, nearly twice as many projections show drier future conditions.

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Seiji Kato
,
Bruce A. Wielicki
,
Fred G. Rose
,
Xu Liu
,
Patrick C. Taylor
,
David P. Kratz
,
Martin G. Mlynczak
,
David F. Young
,
Nipa Phojanamongkolkij
,
Sunny Sun-Mack
,
Walter F. Miller
, and
Yan Chen

Abstract

Variability present at a satellite instrument sampling scale (small-scale variability) has been neglected in earlier simulations of atmospheric and cloud property change retrievals using spatially and temporally averaged spectral radiances. The effects of small-scale variability in the atmospheric change detection process are evaluated in this study. To simulate realistic atmospheric variability, top-of-the-atmosphere nadir-view longwave spectral radiances are computed at a high temporal (instantaneous) resolution with a 20-km field-of-view using cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, along with temperature humidity profiles obtained from reanalysis. Specifically, the effects of the variability on the necessary conditions for retrieving atmospheric changes by a linear regression are tested. The percentage error in the annual 10° zonal mean spectral radiance difference obtained by assuming linear combinations of individual perturbations expressed as a root-mean-square (RMS) difference computed over wavenumbers between 200 and 2000 cm−1 is 10%–15% for most of the 10° zones. However, if cloud fraction perturbation is excluded, the RMS difference decreases to less than 2%. Monthly and annual 10° zonal mean spectral radiances change linearly with atmospheric property perturbations, which occur when atmospheric properties are perturbed by an amount approximately equal to the variability of the10° zonal monthly deseasonalized anomalies or by a climate-model-predicted decadal change. Nonlinear changes in the spectral radiances of magnitudes similar to those obtained through linear estimation can arise when cloud heights and droplet radii in water cloud change. The spectral shapes computed by perturbing different atmospheric and cloud properties are different so that linear regression can separate individual spectral radiance changes from the sum of the spectral radiance change. When the effects of small-scale variability are treated as noise, however, the error in retrieved cloud properties is large. The results suggest the importance of considering small-scale variability in inferring atmospheric and cloud property changes from the satellite-observed zonally and annually averaged spectral radiance difference.

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Theodore M. McHardy
,
James R. Campbell
,
David A. Peterson
,
Simone Lolli
,
Anne Garnier
,
Arunas P. Kuciauskas
,
Melinda L. Surratt
,
Jared W. Marquis
,
Steven D. Miller
,
Erica K. Dolinar
, and
Xiquan Dong

Abstract

This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of <0.4 cm produce false alarms. Enforcing an above-cloud PWV minimum threshold of ∼1 cm ensures that most low-/midlevel clouds are not misclassified as cirrus by the algorithm. Pixel-filtering based on the total column PWV and the PWV for a layer between the top of the atmosphere (TOA) and a predetermined altitude H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

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Kevin A. Scharfenberg
,
Daniel J. Miller
,
Terry J. Schuur
,
Paul T. Schlatter
,
Scott E. Giangrande
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Valery M. Melnikov
,
Donald W. Burgess
,
David L. Andra Jr.
,
Michael P. Foster
, and
John M. Krause

Abstract

To test the utility and added value of polarimetric radar products in an operational environment, data from the Norman, Oklahoma (KOUN), polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) were delivered to the National Weather Service Weather Forecast Office (WFO) in Norman as part of the Joint Polarization Experiment (JPOLE). KOUN polarimetric base data and algorithms were used at the WFO during the decision-making and forecasting processes for severe convection, flash floods, and winter storms. The delivery included conventional WSR-88D radar products, base polarimetric radar variables, a polarimetric hydrometeor classification algorithm, and experimental polarimetric quantitative precipitation estimation algorithms. The JPOLE data collection, delivery, and operational demonstration are described, with examples of several forecast and warning decision-making successes. Polarimetric data aided WFO forecasters during several periods of heavy rain, numerous large-hail-producing thunderstorms, tornadic and nontornadic supercell thunderstorms, and a major winter storm. Upcoming opportunities and challenges associated with the emergence of polarimetric radar data in the operational community are also described.

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S. Kondragunta
,
L. E. Flynn
,
A. Neuendorffer
,
A. J. Miller
,
C. Long
,
R. Nagatani
,
S. Zhou
,
T. Beck
,
E. Beach
,
R. McPeters
,
R. Stolarski
,
P. K. Bhartia
,
M. T. DeLand
, and
L.-K. Huang

Abstract

Ozone estimates from observations by the NOAA-16 Solar Backscattered Ultraviolet (SBUV/2) instrument and Television Infrared Observation Satellite (TIROS-N) Operational Vertical Sounder (TOVS) are used to describe the vertical structure of ozone in the anomalous 2002 polar vortex. The SBUV/2 total ozone maps show that the ozone hole was pushed off the Pole and split into two halves due to a split in the midstratospheric polar vortex in late September. The vortex split and the associated transport of high ozone from midlatitudes to the polar region reduced the ozone hole area from 18 × 106 km2 on 20 September to 3 × 106 km2 on 27 September 2002. A 23-yr time series of SBUV/2 daily zonal mean total ozone amounts between 70° and 80°S shows record high values [385 Dobson units (DU)] during the late-September 2002 warming event. The transport and descent of high ozone from low latitudes to high latitudes between 60 and 15 mb contributed to the unusual increase in total column ozone and a small ozone hole estimated using the standard criterion (area with total ozone < 220 DU). In contrast, TOVS observations show an ozone-depleted region between 0 and 24 km, indicating that ozone destruction was present in the elongated but unsplit vortex in the lower stratosphere. During the warming event, the low-ozone regions in the middle and upper stratosphere were not vertically aligned with the low-ozone regions in the upper troposphere and lower stratosphere. This offset in the vertical distribution of ozone resulted in higher total column ozone masking the ozone depletion in the lower stratosphere and resulting in a smaller ozone hole size estimate from satellite total ozone data.

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D. D. Turner
,
A. M. Vogelmann
,
R. T. Austin
,
J. C. Barnard
,
K. Cady-Pereira
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J. C. Chiu
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S. A. Clough
,
C. Flynn
,
M. M. Khaiyer
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J. Liljegren
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K. Johnson
,
B. Lin
,
C. Long
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A. Marshak
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S. Y. Matrosov
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S. A. McFarlane
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M. Miller
,
Q. Min
,
P. Minimis
,
W. O'Hirok
,
Z. Wang
, and
W. Wiscombe

Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and included 18 different algorithms to evaluate their retrieved LWP, optical depth, and effective radii. Surprisingly, evaluation of the simplest case, a single-layer overcast stratocumulus, revealed that huge discrepancies exist among the various techniques, even among different algorithms that are in the same general classification. This suggests that, despite considerable advances that have occurred in the field, much more work must be done, and we discuss potential avenues for future research.)

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T. Jung
,
M. J. Miller
,
T. N. Palmer
,
P. Towers
,
N. Wedi
,
D. Achuthavarier
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J. M. Adams
,
E. L. Altshuler
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B. A. Cash
,
J. L. Kinter III
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L. Marx
,
C. Stan
, and
K. I. Hodges

Abstract

The sensitivity to the horizontal resolution of the climate, anthropogenic climate change, and seasonal predictive skill of the ECMWF model has been studied as part of Project Athena—an international collaboration formed to test the hypothesis that substantial progress in simulating and predicting climate can be achieved if mesoscale and subsynoptic atmospheric phenomena are more realistically represented in climate models.

In this study the experiments carried out with the ECMWF model (atmosphere only) are described in detail. Here, the focus is on the tropics and the Northern Hemisphere extratropics during boreal winter. The resolutions considered in Project Athena for the ECMWF model are T159 (126 km), T511 (39 km), T1279 (16 km), and T2047 (10 km). It was found that increasing horizontal resolution improves the tropical precipitation, the tropical atmospheric circulation, the frequency of occurrence of Euro-Atlantic blocking, and the representation of extratropical cyclones in large parts of the Northern Hemisphere extratropics. All of these improvements come from the increase in resolution from T159 to T511 with relatively small changes for further resolution increases to T1279 and T2047, although it should be noted that results from this very highest resolution are from a previously untested model version. Problems in simulating the Madden–Julian oscillation remain unchanged for all resolutions tested. There is some evidence that increasing horizontal resolution to T1279 leads to moderate increases in seasonal forecast skill during boreal winter in the tropics and Northern Hemisphere extratropics. Sensitivity experiments are discussed, which helps to foster a better understanding of some of the resolution dependence found for the ECMWF model in Project Athena.

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Theodore M. McHardy
,
James R. Campbell
,
David A. Peterson
,
Simone Lolli
,
Richard L. Bankert
,
Anne Garnier
,
Arunas P. Kuciauskas
,
Melinda L. Surratt
,
Jared W. Marquis
,
Steven D. Miller
,
Erica K. Dolinar
, and
Xiquan Dong

Abstract

We describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite 16 (GOES-16) and developed with collocated Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) channel-4 (1.378 μm) radiance and CALIOP 0.532-μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378-μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine-learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the channel-4 radiance as a function of AMF. The algorithm detects nearly 50% of subvisual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semiquantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378-μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an overland algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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Robert Wood
,
Michael P. Jensen
,
Jian Wang
,
Christopher S. Bretherton
,
Susannah M. Burrows
,
Anthony D. Del Genio
,
Ann M. Fridlind
,
Steven J. Ghan
,
Virendra P. Ghate
,
Pavlos Kollias
,
Steven K. Krueger
,
Robert L. McGraw
,
Mark A. Miller
,
David Painemal
,
Lynn M. Russell
,
Sandra E. Yuter
, and
Paquita Zuidema
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