Search Results

You are looking at 1 - 10 of 10 items for

  • Author or Editor: G. L. Potter x
  • Refine by Access: All Content x
Clear All Modify Search
Robert G. Ellingson, Robert D. Cess, and Gerald L. Potter
Full access
G. L. Potter, R. D. Cess, P. Minnis, E. F. Harrison, and V. Ramanathan

Abstract

This study addresses two aspects of the planetary albedo's diurnal cycle, the first of which refers to directional models of the planetary albodo. It is found that even for clear regions there appear to be deficiencies in our knowledge of how to model this quantity. Over land surfaces, for example, Nimbus-7 data for the directional planetary albedo compare best with model calculations for which a Lambertian surface is assumed, despite ample evidence that the albedo of land surfaces is dependent upon solar zenith angle. Similarly, over ocean surfaces both GOES and Nimbus-7 data produce a weaker dependence of the planetary albedo upon solar zenith angle than would be suggested by model calculations.

The second aspect of the study concerns a comparison of the diurnal amplitude factor, defined as the ratio of the diurnally averaged planetary albedo to that at noon, between two general circulation models (GCMs) and measurements made from a geostationary satellite (GOES). While these comparisons indicate reasonable consistency between the GCMs and the satellite measurements, this is due in part to compensating differences, such as an underestimate in cloud amount by a GCM being compensated for by a corresponding underestimate of the diurnal amplitude factor for overcast regions. The comparisons further underscore difficulties associated with converting local-time albedo measurements, as made from sun-synchronous satellites, to diurnally averaged albedos.

Full access
Robert D. Cess, Seth Nemesure, Ellsworth G. Dutton, John J. Deluisi, Gerald L. Potter, and Jean-Jacques Morcrette

Abstract

Two datasets have been combined to demonstrate how the availability of more comprehensive datasets could serve to elucidate the shortwave radiative impact of clouds on both the atmospheric column and the surface. These datasets consist of two measurements of net downward shortwave radiation: one of near-surface measurements made at the Boulder Atmospheric Observatory tower, and the other of collocated top-of-the-atmosphere measurements from the Earth Radiation Budget Experiment. Output from the European Centre for Medium-Range Weather Forecasts General Circulation Model also has been used as an aid in interpreting the data, while the data have in turn been employed to validate the model's shortwave radiation code as it pertains to cloud radiation properties. Combined, the datasets and model demonstrate a strategy for determining under what conditions the shortwave radiative impact of clouds leads to a heating or cooling of the atmospheric column. The datasets also show, in terms of a linear slope-offset algorithm for retrieving the net downward shortwave radiation at the surface from satellite measurements, that the clouds present during this study produced a modest negative bias in the retrieved surface flux relative to that inferred from a clear-sky algorithm.

Full access
Gerald L Potter, George J. Huffman, David T. Bolvin, Michael G. Bosilovich, Judy Hertz, and Laura E. Carriere

ABSTRACT

We introduce a simple method for detecting changes, both transient and persistent, in reanalysis and merged satellite products due to both natural climate variability and changes to the data sources/analyses used as input. This note demonstrates this Histogram Anomaly Time Series (HATS) method using tropical ocean daily precipitation from MERRA-2 and from GPCP One-Degree Daily (1DD) precipitation estimates. Rather than averaging over space or time, we create a time series display of histograms for each increment of data (such as a day or month). Regional masks such as land–ocean can be used to isolate particular domains. While the histograms reveal subtle structures in the time series, we can amplify the signal by computing the histogram’s anomalies from its climatological seasonal cycle. The qualitative analysis provided by this scheme can then form the basis for more quantitative analyses of specific features, both real and analysis induced. As an example, in the tropical oceans the analysis clearly identifies changes in the time series of both reanalysis and observations that may be related to changing inputs.

Full access
Xianglei Huang, Jason N. S. Cole, Fei He, Gerald L. Potter, Lazaros Oreopoulos, Dongmin Lee, Max Suarez, and Norman G. Loeb

Abstract

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM’s radiation code is uniquely valuable for GCM evaluation because 1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and 2) the fractional contribution of each band to the LW broadband CRE (f CRE) is sensitive to cloud-top height but largely insensitive to cloud fraction, thereby presenting a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band-by-band quantities from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth’s Radiant Energy System (CERES) observations. A study is presented here that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs—the GFDL Atmospheric Model version 2 (GFDL AM2), NASA Goddard Earth Observing System version 5 (GEOS-5), and the fourth-generation AGCM of the Canadian Centre for Climate Modelling and Analysis (CCCma CanAM4)—forced by observed SST. The models agree with observation on the annual-mean LW broadband CRE over the tropical oceans within ±1 W m−2. However, the differences among these three GCMs in some bands can be as large as or even larger than ±1 W m−2. Observed seasonal cycles of f CRE in major bands are shown to be consistent with the seasonal cycle of cloud-top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of f CRE agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of f CRE highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

Full access
Xianglei Huang, Xiuhong Chen, Gerald L. Potter, Lazaros Oreopoulos, Jason N. S. Cole, Dongmin Lee, and Norman G. Loeb

Abstract

Longwave (LW) spectral flux and cloud radiative effect (CRE) are important for understanding the earth’s radiation budget and cloud–radiation interaction. Here, the authors extend their previous algorithms to collocated Atmospheric Infrared Sounder (AIRS) and Cloud and the Earth’s Radiant Energy System (CERES) observations over the entire globe and show that the algorithms yield consistently good performances for measurements over both land and ocean. As a result, the authors are able to derive spectral flux and CRE at 10-cm−1 intervals over the entire LW spectrum from all currently available collocated AIRS and CERES observations. Using this multiyear dataset, they delineate the climatology of spectral CRE, including the far IR, over the entire globe as well as in different climate zones. Furthermore, the authors define two quantities, IR-effective cloud-top height (CTHeff) and cloud amount (CAeff), based on the monthly-mean spectral (or band by band) CRE. Comparisons with cloud fields retrieved by the CERES–Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm indicate that, under many circumstances, the CTHeff and CAeff can be related to the physical retrievals of CTH and CA and thus can enhance understandings of model deficiencies in LW radiation budgets and cloud fields. Using simulations from the GFDL global atmosphere model, version 2 (AM2); NASA’s Goddard Earth Observing System, version 5 (GEOS-5); and Environment Canada’s Canadian Centre for Climate Modelling and Analysis (CCCma) Fourth Generation Canadian Atmospheric General Circulation Model (CanAM4) as case studies, the authors further demonstrate the merits of the CTHeff and CAeff concepts in providing insights on global climate model evaluations that cannot be obtained solely from broadband LW flux and CRE comparisons.

Full access
Masao Kanamitsu, Wesley Ebisuzaki, Jack Woollen, Shi-Keng Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter

The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.

Full access
Thomas J. Phillips, Gerald L. Potter, David L. Williamson, Richard T. Cederwall, James S. Boyle, Michael Fiorino, Justin J. Hnilo, Jerry G. Olson, Shaocheng Xie, and J. John Yio

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework.

To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.

Full access
A. E. Roche, J. B. Kumer, J. L. Mergenthaler, R. W. Nightingale, W. G. Uplinger, G. A. Ely, J. F. Potter, D. J. Wuebbles, P. S. Connell, and D. E. Kinnison

Abstract

This paper discusses simultaneous measurements of stratospheric CIONO2, HNO3, temperature, and aerosol extinction coefficient by the Cryogenic Limb Array Etalon Spectrometer (CLAES) on the NASA Upper Atmosphere Research Satellite (UARS), obtained over the period 9 January 1992 through 23 April 1993. The discussion concentrates on the stratosphere region near 21 km of particular interest to heterogeneously driven ozone depletion. For periods between 12 June and 1 September 1992 at latitudes poleward of about 60°S, when temperatures were below type I polar stratospheric cloud (PSC) formation thresholds throughout the lower stratosphere, CLAES observed high levels of PSCs coincident with highly depleted fields of both HNO3 and CIONO2. By 17 September, the incidence of PSCs had greatly diminished in the lower stratosphere, but both CIONO2 and HNO3 remained highly depleted. These observations are consistent with the removal of gaseous HNO3 through the formation of nitric acid trihydrate (NAT) particles and the removal of CIONO2 through heterogeneous reactions on the particle surfaces. They also suggest substantial denitrification of the lower Antarctic vortex through sedimentation of PSC particles. In the Northern Hemisphere winter of 1992/93 far fewer PSCs were observed in the Arctic lower-stratosphere vortex, which had shorter periods and more localized regions of cold temperatures. Both HNO3 and CIONO2 maintained much higher levels inside the Arctic vortex than those seen in the Antarctic throughout the winter/spring period. Following 28 February 1993 when Arctic vortex temperatures rose above 195 K, CIONO2 was observed in large quantities [>2.1 ppbv near 21 km] inside the vortex. The persistence of relatively high levels of HNO3 inside the Arctic spring vortex compared with the low levels seen in the Antarctic spring vortex suggest a much lower level of denitrification in the Arctic.

Full access
W. Lawrence Gates, James S. Boyle, Curt Covey, Clyde G. Dease, Charles M. Doutriaux, Robert S. Drach, Michael Fiorino, Peter J. Gleckler, Justin J. Hnilo, Susan M. Marlais, Thomas J. Phillips, Gerald L. Potter, Benjamin D. Santer, Kenneth R. Sperber, Karl E. Taylor, and Dean N. Williams

The Atmospheric Model Intercomparison Project (AMIP), initiated in 1989 under the auspices of the World Climate Research Programme, undertook the systematic validation, diagnosis, and intercomparison of the performance of atmospheric general circulation models. For this purpose all models were required to simulate the evolution of the climate during the decade 1979–88, subject to the observed monthly average temperature and sea ice and a common prescribed atmospheric CO2 concentration and solar constant. By 1995, 31 modeling groups, representing virtually the entire international atmospheric modeling community, had contributed the required standard output of the monthly means of selected statistics. These data have been analyzed by the participating modeling groups, by the Program for Climate Model Diagnosis and Intercomparison, and by the more than two dozen AMIP diagnostic subprojects that have been established to examine specific aspects of the models' performance. Here the analysis and validation of the AMIP results as a whole are summarized in order to document the overall performance of atmospheric general circulation–climate models as of the early 1990s. The infrastructure and plans for continuation of the AMIP project are also reported on.

Although there are apparent model outliers in each simulated variable examined, validation of the AMIP models' ensemble mean shows that the average large-scale seasonal distributions of pressure, temperature, and circulation are reasonably close to what are believed to be the best observational estimates available. The large-scale structure of the ensemble mean precipitation and ocean surface heat flux also resemble the observed estimates but show particularly large intermodel differences in low latitudes. The total cloudiness, on the other hand, is rather poorly simulated, especially in the Southern Hemisphere. The models' simulation of the seasonal cycle (as represented by the amplitude and phase of the first annual harmonic of sea level pressure) closely resembles the observed variation in almost all regions. The ensemble's simulation of the interannual variability of sea level pressure in the tropical Pacific is reasonably close to that observed (except for its underestimate of the amplitude of major El Niños), while the interannual variability is less well simulated in midlatitudes. When analyzed in terms of the variability of the evolution of their combined space–time patterns in comparison to observations, the AMIP models are seen to exhibit a wide range of accuracy, with no single model performing best in all respects considered.

Analysis of the subset of the original AMIP models for which revised versions have subsequently been used to revisit the experiment shows a substantial reduction of the models' systematic errors in simulating cloudiness but only a slight reduction of the mean seasonal errors of most other variables. In order to understand better the nature of these errors and to accelerate the rate of model improvement, an expanded and continuing project (AMIP II) is being undertaken in which analysis and intercomparison will address a wider range of variables and processes, using an improved diagnostic and experimental infrastructure.

Full access