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F-F. Jin
,
L-L. Pan
, and
M. Watanabe

Abstract

The interaction between synoptic eddy and low-frequency flow (SELF) has been recognized for decades to play an important role in the dynamics of the low-frequency variability of the atmospheric circulation. In this three-part study a linear framework with a stochastic basic flow capturing both the climatological mean flow and climatological measures of the synoptic eddy flow is proposed. Based on this linear framework, a set of linear dynamic equations is derived for the ensemble-mean eddy forcing that is generated by anomalous time-mean flows. By assuming that such dynamically determined eddy-forcing anomalies approximately represent the time-mean anomalies of the synoptic eddy forcing and by using a quasi-equilibrium approximation, an analytical nonlocal dynamical closure is obtained for the two-way SELF feedback. This linear closure, directly relating time-mean anomalies of the synoptic eddy forcing to the anomalous time–mean flow, becomes an internal part of a new linear dynamic system for anomalous time–mean flow that is referred to as the low-frequency variability of the atmospheric circulation in this paper.

In Part I, the basic approach for the SELF closure is illustrated using a barotropic model. The SELF closure is tested through the comparison of the observed eddy-forcing patterns associated with the leading low-frequency modes with those derived using the SELF feedback closure. Examples are also given to illustrate an important role played by the SELF feedback in regulating the atmospheric responses to remote forcing. Further applications of the closure for understanding the dynamics of low-frequency modes as well as the extension of the closure to a multilevel primitive equation model will be given in Parts II and III, respectively.

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L-L. Pan
,
F-F. Jin
, and
M. Watanabe

Abstract

In this three-part study, a linear closure has been developed for the synoptic eddy and low-frequency flow (SELF) interaction and demonstrated that internal dynamics plays an important role in generating the leading low-frequency modes in the extratropical circulation anomalies during cold seasons.

In Part III, a new linearized primitive equation system is first derived for time-mean flow anomalies. The dynamical operator of the system includes a traditional part depending on the observed climatological mean state and an additional part from the SELF feedback closure utilizing the observed climatological properties of synoptic eddy activity. The latter part relates nonlocally all the anomalous eddy-forcing terms in equations of momentum, temperature, and surface pressure to the time-mean flow anomalies. Using the observational data, the closure was validated with reasonable success, and it was found that terms of the SELF feedback in the momentum and pressure equations tend to reinforce the low-frequency modes, whereas those in the thermodynamic equation tends to damp the temperature anomalies to make the leading modes equivalent barotropic. Through singular vector analysis of the linear dynamical operator, it is highlighted that the leading modes of the system resemble the observed patterns of the Arctic Oscillation, Antarctic Oscillation, and Pacific–North American pattern, in which the SELF feedback plays an essential role, consistent with the finding of the barotropic model study in Part II.

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F-F. Jin
,
L-L. Pan
, and
M. Watanabe

Abstract

Amidst stormy atmospheric circulation, there are prominent recurrent patterns of variability in the planetary circulation, such as the Antarctic Oscillation (AAO), Arctic Oscillation (AO) or North Atlantic Oscillation (NAO), and the Pacific–North America (PNA) pattern. The role of the synoptic eddy and low-frequency flow (SELF) feedback in the formation of these dominant low-frequency modes is investigated in this paper using the linear barotropic model with the SELF feedback proposed in Part I. It is found that the AO-like and AAO-like leading singular modes of the linear dynamical system emerge from the stormy background flow as the result of a positive SELF feedback. This SELF feedback also prefers a PNA-like singular vector as well among other modes under the climatological conditions of northern winters.

A model with idealized conditions of basic mean flow and activity of synoptic eddy flow and a prototype model are also used to illustrate that there is a natural scale selection for the AAO- and AO-like modes through the positive SELF feedback. The zonal scale of the localized features in the Atlantic (southern Indian Ocean) for AO (AAO) is largely related to the zonal extent of the enhanced storm track activity in the region. The meridional dipole structures of AO- and AAO-like low-frequency modes are favored because of the scale-selective positive SELF feedback, which can be heuristically understood by the tilted-trough mechanism.

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Pavel Ya. Groisman
,
Eugene L. Genikhovich
, and
Pan-Mao Zhai

This paper is a continuation of empirical studies of cloud and snow cover effects on climate based on a blend of observational meteorological data for the past several decades. It employs the idea that the analysis of climate variability observed during the period of intensive instrumental observations can provide “overall estimates” of these effects.

A climatology of clear skies for northern extratropical lands is presented in the form of deviations from the average climate conditions. Clouds are an internal component of the climate system, and these deviations indicate specific climate conditions associated with clear skies. At the same time, they may be considered as estimates of the overall cloud effect on the regional climate. A similar approach is applied to estimate the potential effect of snow on the ground, and an attempt is made to divide the effects of snow and clouds.

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Amanda L. Siemann
,
Gabriele Coccia
,
Ming Pan
, and
Eric F. Wood

Abstract

Land surface temperature (LST) is a critical state variable for surface energy exchanges as it is one of the controls on emitted radiation at Earth’s surface. LST also exerts an important control on turbulent fluxes through the temperature gradient between LST and air temperature. Although observations of surface energy balance components are widely accessible from in situ stations in most developed regions, these ground-based observations are not available in many underdeveloped regions. Satellite remote sensing measurements provide wider spatial coverage to derive LST over land and are used in this study to form a high-resolution, long-term LST data product. As selected by the Global Energy and Water Exchanges project (GEWEX) Data and Assessments Panel (GDAP) for development of internally consistent datasets, the High Resolution Infrared Radiation Sounder (HIRS) data are used for the primary satellite observations because of the data record length. The final HIRS-consistent, hourly, global, 0.5° resolution LST dataset for clear and cloudy conditions from 1979 to 2009 is developed through merging the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) LST estimates with the HIRS retrievals using a Bayesian postprocessing procedure. The Baseline Surface Radiation Network (BSRN) observations are used to validate the HIRS retrievals, the CFSR LST estimates, and the final merged LST dataset. An intercomparison between the original retrievals and CFSR LST datasets, before and after merging, is also presented with an analysis of the datasets, including an error assessment of the final LST dataset.

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Fang Pan
,
Xianglei Huang
,
L. Larabbe Strow
, and
Huan Guo

Abstract

The Atmospheric Infrared Sounder (AIRS) level-1b radiances have been shown to be well calibrated (~0.3 K or higher) and have little secular drift (~4 mK yr−1) since operation started in September 2002. This paper investigates the linear trends of 10 years (2003–12) of AIRS global-mean radiances in the CO2 v 2 band that are sensitive to emissions from the stratosphere (stratospheric channels). AIRS lower-stratospheric channels have a cooling trend of no more than 0.23 K decade−1 whereas the midstratospheric channels consistently show a statistically significant cooling trend as large as 0.58 K decade−1. The 95% confidence interval for the trend is ~±0.20 K decade−1. Two sets of synthetic AIRS radiances are computed using the principal component–based radiative transfer model (PCRTM), one based on a free-running GFDL Atmospheric Model, version 3 (AM3), over the same period and one based on ERA-Interim. The GFDL AM3 simulations overestimate the cooling trends in the mid- to upper-stratospheric channels but slightly underestimate them in the lower-stratospheric channels. The synthetic radiances based on ERA-Interim, however, have statistically significant positive trends at virtually all stratospheric channels. This confirms the challenge to the GCM modeling and reanalysis community to create a better simulation or assimilation of the stratospheric climate. It is shown that the linear trends in AIRS radiances can be reproduced to a large extent by the spectral radiative kernel technique and the trends from the AIRS L2 temperature retrievals and from the change of CO2. This suggests a closure between AIRS L1 radiances and L2 retrievals and the potential merit of AIRS data in studies of stratosphere changes.

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A. A. M. Holtslag
,
E. I. F. De Bruijn
, and
H-L. Pan

Abstract

This paper describes a high resolution air mass transformation (AMT) model. The model is intended for short-range weather forecasts of the temperature and humidity profiles in the lower atmosphere, the structure of the boundary layer, the boundary layer height, and the amount of boundary layer clouds. The AMT model consists of a one-dimensional, multilayer boundary layer model, which is advected along trajectories from a source region to a receptor point. The trajectories are calculated within a larger scale (limited area) model. The initial profiles for temperature and humidity are obtained from observed radiosondes. The paper describes the physical and dynamical background of the model. With the model we have made case studies of the development of stratocumulus over the North Sea, and have simulated the representation of clear skies over land. The output of the model is compared with the output of the ECMWF model and the current operational bulk AMT model. Sensitivity of the model to boundary and initial conditions is discussed. In addition to the case studies the model has been used as an operational forecast tool in 77 cases. These cases have been evaluated by independent forecasters. Since the model performed well and because no large computing facilities are needed, it is concluded that the model is a useful tool for the short-range weather forecaster.

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Weinan Pan
,
R. P. Boyles
,
J. G. White
, and
J. L. Heitman

Abstract

Soil moisture has important implications for meteorology, climatology, hydrology, and agriculture. This has led to growing interest in development of in situ soil moisture monitoring networks. Measurement interpretation is severely limited without soil property data. In North Carolina, soil moisture has been monitored since 1999 as a routine parameter in the statewide Environment and Climate Observing Network (ECONet), but with little soils information available for ECONet sites. The objective of this paper is to provide soils data for ECONet development. The authors studied soil physical properties at 27 ECONet sites and generated a database with 13 soil physical parameters, including sand, silt, and clay contents; bulk density; total porosity; saturated hydraulic conductivity; air-dried water content; and water retention at six pressures. Soil properties were highly variable among individual ECONet sites [coefficients of variation (CVs) ranging from 12% to 80%]. This wide range of properties suggests very different behavior among sites with respect to soil moisture. A principal component analysis indicated parameter groupings associated primarily with soil texture, bulk density, and air-dried water content accounted for 80% of the total variance in the dataset. These results suggested that a few specific soil properties could be measured to provide an understanding of differences in sites with respect to major soil properties. The authors also illustrate how the measured soil properties have been used to develop new soil moisture products and data screening for the North Carolina ECONet. The methods, analysis, and results presented here have applications to North Carolina and for other regions with heterogeneous soils where soil moisture monitoring is valuable.

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Jinxue Wang
,
John C. Gille
,
Paul L. Bailey
,
Liwen Pan
,
David Edwards
, and
James R. Drummond

Abstract

Global tropospheric carbon monoxide (CO) distributions can be retrieved from observations by spaceborne gas correlation radiometers and high-resolution interferometers. The Measurement of Pollution in the Troposphere (MOPITT) is a gas correlation radiometer designed for tropospheric CO and CH4 remote sensing. It is being developed at the University of Toronto and the National Center for Atmospheric Research for launch on the EOS/AM-1 platform in 1999. Spaceborne high-resolution interferometers with troposphere CO remote sensing capability include the Interferometric Monitor for Greenhouse gases (IMG) instrument and the Troposphere Emission Spectrometer (TES). IMG was developed by the Ministry of International Trade and Industry (MITI) of Japan. It was on the ADEOS-1 spacecraft launched in October 1996. TES is being developed by the Jet Propulsion Laboratory for launch on the EOS/CHEM-1 platform in 2002.

For the purpose of testing the MOPITT data processing algorithms before launch, a new digital gas correlation (DGC) method was developed. This method makes it possible to use existing IMG observations to validate the MOPITT retrieval algorithms. The DGC method also allows the retrieval of global troposphere CO from MOPITT, IMG, and TES observations with a consistent algorithm. The retrieved CO profiles can be intercompared, and a consistent long time series of tropospheric CO measurements can be created. In this paper, the DGC method is described. The procedures for using the DGC method to retrieve atmospheric trace species profiles are discussed. As an example, CO profiles from IMG observations have been retrieved with the DGC method as a demonstration of its feasibility and application in MOPITT retrieval algorithm validation.

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Jinxue Wang
,
John C. Gille
,
Paul L. Bailey
,
James R. Drummond
, and
Liwen Pan

Abstract

Measurement of Pollution in the Troposphere (MOPITT) is an eight-channel gas correlation radiometer selected for the Earth Observing System AM-1 platform to be launched in 1999. Its primary objectives are the measurement of tropospheric carbon monoxide (CO) and methane (CH4). In this paper, the sensitivities of instrument signals and CO retrieval errors to various instrument parameters, especially the gas cell pressure and temperature variations, instrument radiometric noise, and ancillary data errors (such as atmospheric temperature and water vapor profile errors), are presented and discussed. In the MOPITT pressure modulator cell pressure sensitivity study, the instrument calibration process is considered, which leads to the relaxation of previous stringent requirements on the accuracy of in-orbit cell pressure monitoring. The approach of MOPITT CO retrieval error analysis is described, and the error analysis results are compared with retrieval simulation statistics. The error analysis results indicate that tropospheric CO distributions can be retrieved with a precision of 10% for most of the troposphere.

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