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The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

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.

The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth System Model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) atmospheric model working group (AMWG) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.6, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the Quasi-biennial Oscillation (QBO), El Niño – Southern Oscillation (ENSO), streamflow, diurnal cycle of precipitation, tropical cyclones and ozone. An in-situ dataset from DOE’s Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include: customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, designed for nonexperts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits, and limitations; and (4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80; many are undergoing updates, with another 20 or so in preparation, and more than 100 are proposed and under consideration. A partial list of current global satellite-based datasets includes humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top-of-atmosphere longwave and shortwave radiation; and ozone (O3), methane (CH4), and carbon dioxide (CO2) products. A partial list of proposed products expected to be useful in analyzing CMIP6 results includes the following: alternative products for the above quantities, additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g., FAPAR, LAI, burned area fraction), ice sheet mass and height, carbon monoxide (CO), and nitrogen dioxide (NO2). While most existing obs4MIPs datasets consist of monthly-mean gridded data over the global domain, products with higher time resolution (e.g., daily) and/or regional products are now receiving more attention. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including (1) an updated obs4MIPs data specifications document that provides additional search facets and generally improves congruence with CMIP6 specifications for model datasets, (2) a set of six easily understood indicators that help guide users as to a dataset's maturity and suitability for application, and (3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth system models used in CMIP.

This paper describes the repackaging and consistent distribution of the world’s major atmospheric and oceanic reanalyses. It also presents examples of the usefulness of examining multiple reanalyses. This service will make it much easier for anybody using reanalysis to access multiple datasets using an approach similar to that of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Experienced users as well as students will find the standardized formatted data convenient to use.

The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Some examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data are climate model intercomparison (CMIP) experiments; the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Observations for Model Intercomparison Projects (Obs4MIPs), Analysis for Model Intercomparison Projects (Ana4MIPs), and Collaborative Reanalysis Technical Environment-Intercomparison Project (CREATE-IP) activities; and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC). This article provides an overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), which is the primary cyberinfrastructure currently supporting global climate research activities.

Faced with unprecedented growth in earth data volume and demand, NASA has developed the Earth Data Analytic Services (EDAS) framework, a high performance big data analytics and machine learning fra...

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.

The objective of the Observations for Model Intercomparison Projects (Obs4MIPs) is to provide observational data to the climate science community, which is analogous (in terms of variables, temporal and spatial frequency, and periods) to output from the 5th phase of the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project (CMIP5) climate model simulations. The essential aspect of the Obs4MIPs methodology is that it strictly follows the CMIP5 protocol document when selecting the observational datasets. Obs4MIPs also provides documentation that describes aspects of the observational data (e.g., data origin, instrument overview, uncertainty estimates) that are of particular relevance to scientists involved in climate model evaluation and analysis. In this paper, we focus on the activities related to the initial set of satellite observations, which are being carried out in close coordination with CMIP5 and directly engage NASA's observational (e.g., mission and instrument) science teams. Having launched Obs4MIPs with these datasets, a broader effort is also briefly discussed, striving to engage other agencies and experts who maintain datasets, including reanalysis, which can be directly used to evaluate climate models. Different strategies for using satellite observations to evaluate climate models are also briefly summarized.

Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.

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 (fCRE) 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 fCRE 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 fCRE 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 fCRE highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands.

The Modern-Era Retrospective Analysis for Research and Application (MERRA) is a reanalysis designed to produce an improved representation of the Earth’s hydrologic cycle. This study examines the representation of deep convective clouds in MERRA, comparing analyzed liquid and ice clouds with deep convective cloud objects observed by instruments on the Tropical Rainfall Measuring Mission satellite. Results show that MERRA contains deep convective cloud in 98.1% of the observed cases. MERRA-derived probability density functions (PDFs) of cloud properties have a similar form as the observed PDFs and exhibit a similar trend with changes in object size. Total water path, optical depth, and outgoing shortwave radiation (OSR) in MERRA are found to match the cloud object observations quite well; however, there appears to be a bias toward higher-than-observed cloud tops in the MERRA. The reanalysis fits the observations most closely for the largest class of convective systems, with performance generally decreasing with a transition to smaller convective systems. Comparisons of simulated total water path, optical depth, and OSR are found to be highly sensitive to the assumed subgrid distribution of condensate and indicate the need for caution when interpreting model-data comparisons that require disaggregation of grid-scale cloud to satellite pixel scales.

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.

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.

What: to celebrate the twentieth anniversary of PCmDi and to honor its founder, w. lawrence gates, more than 100 specialists and leaders in climate modeling met to discuss PCmDi’s history and the future of climate modeling When: 6 april 2009 Where: bethesda, maryland T wenty years ago, W. Lawrence (Larry) Gates approached the U.S. Department of Energy (DOE) Office of Energy Research (now the Office of Science) with a plan to coordinate the comparison and documentation of climate model differences. This effort would help improve our understanding of climate change through a systematic approach to model intercomparison. Early attempts at comparing results showed a surprisingly large range in control climate from such parameters as cloud cover, precipitation, and even atmospheric temperature. The DOE agreed to fund the effort at the Lawrence Livermore National Laboratory (LLNL), in part because of the existing computing environment and because of a preexisting atmospheric science group that contained a wide variety of expertise. The project was named the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and it has changed the international landscape of climate modeling over the past 20 years. In spring 2009 the DOE hosted a 1-day symposium to celebrate the twentieth anniversary of PCMDI and to honor its founder, Larry Gates. Through their personal experiences, the morning presenters painted an image of climate science in the 1970s and 1980s, that generated early support from the international community for model intercomparison, thereby bringing PCMDI into existence. Four talks covered Gates’s early contributions to climate research at the University of California, Los Angeles (UCLA), the RAND Corporation, and Oregon State University through the founding of PCMDI to coordinate the Atmospheric Model Intercomparison Project (AMIP). The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The afternoon session emphasized the scientific ideas that are the basis of PCMDI’s success, summarizing their AFFILIATIONS: Potter—University of California Davis, Davis, California; bader—oak ridge national laboratory, oak ridge, tennessee; riChes and josePh—office of biological and environmental research, U.s. Department of energy, washington, D.C.; baMzai—Climate and large-scale Dynamics Program, national science foundation, arlington, Virginia CORRESPONDING AUTHOR: gerald l. Potter, Department of geology, University of California, Davis, one shields avenue, Davis, Ca 95616 e-mail: jpotter@ucdavis.edu

The Parallel Climate Model (PCM) Version 1.1 simulates a reasonable twentieth century climatology in the Sahara Desert. From late 1940s to the end of 1980s, the simulated Sahara Desert, bounded by the 50 mm mean annual rainfall isoline, becomes larger and shifts eastward. The model produces a decreasing rainfall trend while the surface temperature and meridional boundaries are almost stable. In the usual scenario with increasing greenhouse gases from the 1980s to the 2090s the Sahara becomes smaller, moves north and west and continues to dry. Both the size change and latitudinal shift show a century long trend. Compared to 1961–90 climatology, the average northward shift is around 1° and the surface temperature about 2.8°C warmer to the end of 21st century. The local greenhouse effect may cause such warming trend.

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 valida...

There has been a long history of unexplained anomalous absorption of solar radiation by clouds. Collocated satellite and surface measurements of solar radiation at five geographically diverse locations showed significant solar absorption by clouds, resulting in about 25 watts per square meter more global-mean absorption by the cloudy atmosphere than predicted by theoretical models. It has often been suggested that tropospheric aerosols could increase cloud absorption. But these aerosols are temporally and spatially heterogeneous, whereas the observed cloud absorption is remarkably invariant with respect to season and location. Although its physical cause is unknown, enhanced cloud absorption substantially alters our understanding of the atmosphere's energy budget.

Climate scientists have made substantial progress in understanding Earth's climate system, particularly at global and continental scales. Climate research is now focused on understanding climate changes over wider ranges of time and space scales. These efforts are generating ultra-scale data sets at very high spatial resolution. An insightful analysis in climate science depends on using software tools to discover, access, manipulate, and visualize the data sets of interest. These data exploration tasks can be complex and time-consuming, and they frequently involve many resources from both the modeling and observational climate communities. Because of the complexity of the explorations, provenance is critical, allowing scientists to ensure reproducibility, revisit existing computational pipelines, and more easily share analyses and results. In addition, as the results of this work can impact policy, having provenance available is important for decision-making. In this paper we describe, UV-CDAT, a workflow-based, provenance-enabled system that integrates climate data analysis libraries and visualization tools in an end-to-end application, making it easier for scientists to integrate and use a wide array of tools.

The ability of the ECMWF model (cycle 33) to simulate the Indian and East Asian summer monsoons is evaluated at four different horizontal resolutions: T21, T42, T63, and T1O6. Generally, with respect to the large-scale features of the circulation, the largest differences among the simulations occur at T42 relative to T21. However, on regional scales, important differences among the high-frequency temporal variability serve as a further critical rest of the model's ability to simulate the monsoon. T106 best captures both the spatial and temporal characteristics of the Indian and East Asian monsoons, whereas T42 fails to correctly simulate the sequence and development of synoptic-scale milestones that characterize the monsoon flow. In particular, T106 is superior at simulating the development and migration of the monsoon trough over the Bay of Bengal. In the T42 simulation, the development of the monsoon occurs one month earlier than typically observed. At this time the trough is incorrectly locate...

Cloud radiative forcing constitutes the radiative impact of clouds upon the earth's present climate, while cloud radiative feedback is the change in this forcing associated with climatic change. The present study addresses two issues concerning cloud radiative forcing. The first is that an intercomparison of cloud radiative forcing, as predicted by six different general circulation models, shows a considerable lack of agreement, underscoring the need for an improved understanding of cloud/radiation interactions within such models. The second issue pertains to an examination of strategies by which model-predicted cloud/radiation interactions should be compared with satellite-derived data sets. DOI: 10.1111/j.1600-0870.1987.tb00321.x

Twenty years ago, W. Lawrence (Larry) Gates approached the U.S. Department of Energy (DOE) Office of Energy Research (now the Office of Science) with a plan to coordinate the comparison and documentation of climate model differences. This effort would help improve our understanding of climate change through a systematic approach to model intercomparison. Early attempts at comparing results showed a surprisingly large range in control climate from such parameters as cloud cover, precipitation, and even atmospheric temperature. The DOE agreed to fund the effort at the Lawrence Livermore National Laboratory (LLNL), in part because of the existing computing environment and because of a preexisting atmospheric science group that contained a wide variety of expertise. The project was named the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and it has changed the international landscape of climate modeling over the past 20 years. In spring 2009 the DOE hosted a 1-day symposium to celebrate the twentieth anniversary of PCMDI and to honor its founder, Larry Gates. Through their personal experiences, the morning presenters painted an image of climate science in the 1970s and 1980s, that generated early support from the international community for model intercomparison, thereby bringing PCMDI into existence. Four talks covered more » GatesAƒÂƒA‚¢AƒÂ‚A‚€AƒÂ‚A‚™s early contributions to climate research at the University of California, Los Angeles (UCLA), the RAND Corporation, and Oregon State University through the founding of PCMDI to coordinate the Atmospheric Model Intercomparison Project (AMIP). The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The afternoon session emphasized the scientific ideas that are the basis of PCMDIAƒÂƒA‚¢AƒÂ‚A‚€AƒÂ‚A‚™s success, summarizing their evolution and impact. Four speakers followed the various PCMDI-supported climate model intercomparison projects, beginning with early work on cloud representations in models, presented by Robert D. Cess (Distinguished Professor Emeritus, Stony Brook University), and then the latest Cloud Feedback Model Intercomparison Projects (CFMIPs) led by Sandrine Bony (Laboratoire de MAƒÂƒA‚ƒAƒÂ‚A‚©tAƒÂƒA‚ƒAƒÂ‚A‚©orologie Dynamique). Benjamin Santer (LLNL) presented a review of the climate change detection and attribution (D & A) work pioneered at PCMDI, and Gerald A. Meehl (NCAR) ended the day with a look toward the future of climate change research. « less

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.

[1] We compare the balance of terms in moisture and temperature prediction equations during short forecasts by the Community Atmosphere Model (CAM2) with observed estimates at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site for two intensive observing periods (IOPs). The goal is to provide insight into parameterization errors which ultimately should lead to model improvements. The atmospheric initial conditions are obtained from high-resolution numerical weather prediction (NWP) analyses. The land initial conditions are spun up to be consistent with those analyses. Three cases are considered: (1) June/July 1997 when the atmosphere is relatively moist and surface evaporation corresponds to 90% of the precipitation with advection accounting for the remainder; (2) rainy days in April 1997 when the atmosphere is less moist and horizontal advection accounts for much of the precipitation with a small contribution from surface evaporation and the balance being derived from the water already present in the column; and (3) nonrainy days of the April 1997 when the moist process parameterizations are inactive and the planetary boundary layer (PBL) parameterization is dominant. For the first case the Zhang-McFarlane deep convective parameterization drives the model to a wrong state. For the second the Hack shallow convective parameterization appears to be not acting deep enough. During both periods inconsistencies between CAM2 and ARM surface fluxes, land surface conditions and the net surface radiative fluxes indicate that the exchange parameterizations should be examined further. For the third case the PBL parameterization does not appear to create the correct vertical structure. In addition, the individual components of the dynamical tendency are very different between CAM2 and ARM, although the total dynamical tendency is similar in the two. Although these observations do not imply that those components are themselves wrong since they may be responding to other errors, each of these components should be examined further to determine the cause of their behaviors.

[1] The Community Atmospheric Model 2 (CAM2) is run as a short-term (1–5 days) forecast model initialized with reanalysis data. The intent is to reveal model deficiencies before complex interactions obscure the root error sources. Integrations are carried out for three Atmospheric Radiation Measurement (ARM) Program intensive operational periods (IOPs): June/July 1997, April 1997, and March 2000. The ARM data are used to validate the model in detail for the Southern Great Plains (SGP) site for all the periods and in the tropical west Pacific for the March 2000 period. The model errors establish themselves quickly, and within 3 days the model has evolved into a state distinctly different from the ARM observations. The summer forecasts evince a systematic error in convective rainfall. This error manifests itself in the temperature and moisture profiles after a single diurnal cycle. The same error characteristics are seen in the March 2000 tropical west Pacific forecasts. The model performs well in the spring cases at the SGP. Most of the error is manifested during rainy periods. The ARM cloud radar comparison to the model reveals cloud errors which are consistent with the relative humidity profile errors. The cloud errors are similar to those seen in climatological integrations, but the state variable errors are different. Thus there is the possibility that the some basic parameterization errors are obscured in the climatological integrations. The approach described here will facilitate parameterization experimentation, diagnoses, and validation. One way of reducing cloud feedback uncertainty is to make the physical processes behave in the most realistic manner possible. Paradoxically, perhaps the best way to reduce uncertainty in cloud feedback mechanisms is to evaluate the model processes with realistic forcing before such feedbacks have any significant affect.

[1] Version 3 of the Hadley Centre Atmospheric Model (HadAM3) has been used to demonstrate one means of comparing a general circulation model with observations for a specific climate perturbation, namely the strong 1997/98 El Nino. This event was characterized by the collapse of the tropical Pacific's Walker circulation, caused by the lack of a zonal sea surface temperature gradient during the El Nino. Relative to normal years, cloud altitudes were lower in the western portion of the Pacific and higher in the eastern portion. HadAM3 likewise produced the observed collapse of the Walker circulation, and it did a reasonable job of reproducing the west/east cloud structure changes. This illustrates that the 1997/98 El Nino serves as a useful means of testing cloud-climate interactions in climate models.

[1] This study implements a revised convective triggering condition in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, Version 2 (CAM2), model to reduce its excessive warm season daytime precipitation over land. The new triggering mechanism introduces a simple dynamic constraint on the initiation of convection that emulates the collective effects of lower level moistening and upward motion of the large-scale circulation. It requires a positive contribution from the large-scale advection of temperature and moisture to the existing positive convective available potential energy (CAPE) for model convection to start. In contrast, the original convection triggering function in CAM2 assumes that convection is triggered whenever there is positive CAPE, which results in too frequent warm season convection over land arising from strong diurnal variation of solar radiation. We examine the impact of the new trigger on CAM2 simulations by running the climate model in numerical weather prediction (NWP) mode so that more available observations and high-frequency NWP analysis data can be used to evaluate model performance. We show that the modified triggering mechanism has led to considerable improvements in the simulation of precipitation, temperature, moisture, clouds, radiations, surface temperature, and surface sensible and latent heat fluxes when compared to the data collected from the Atmospheric Radiation Measurement (ARM) Program at its Southern Great Plains (SGP) site. Similar improvements are also seen over other parts of the globe. In particular, the surface precipitation simulation has been significantly improved over both the continental United States and around the globe; the overestimation of high clouds in the equatorial tropics has been substantially reduced; and the temperature, moisture, and zonal wind are more realistically simulated. Results from this study also show that some systematic errors in the CAM2 climate simulations can be detected in the early stage of model integration. Examples are the extremely overestimated high clouds in the tropics in the vicinity of Intertropical Convergence Zone and the spurious precipitation maximum to the east of the Rockies. This has important implications in studies of these model errors since running the climate model in NWP mode allows us to perform a more in-depth analysis during a short time period where more observations are available and different model errors from various processes have not compensated for the systematic errors.

[1] We compare cloud-radiative forcing (CRF) at the top-of-the atmosphere from 19 atmospheric general circulation models, employing simulations with prescribed sea-surface temperatures, to observations from the Earth Radiation Budget Experiment (ERBE). With respect to 60°N to 60°S means, a surprising result is that many of the 19 models produce unusually large biases in Net CRF that are all of the same sign (negative), meaning that many of the models significantly overestimate cloud radiative cooling. The primary focus of this study, however, is to demonstrate a diagnostic procedure, using ERBE data, to test if a model might produce, for a given region, reasonable CRF as a consequence of compensating errors caused either by unrealistic cloud vertical structure, cloud optical depth or cloud fraction. For this purpose we have chosen two regions, one in the western tropical Pacific characterized by high clouds spanning the range from thin cirrus to deep convective clouds, and the other in the southeastern Pacific characterized by trade cumulus. For a subset of eight models, it is found that most typically produce more realistic regionally-averaged CRF (and its longwave and shortwave components) for the southeastern region as opposed to the western region. However, when the diagnostic procedure for investigating cloud vertical structure and cloud optical depth is imposed, this somewhat better agreement in the southeastern region is found to be the result of compensating errors in either cloud vertical structure, cloud optical depth or cloud fraction. The comparison with ERBE data also shows large errors in clear-sky fluxes for many of the models.

Model output from a selected set of AMIP models shows that the tropical shortwave cloud radiative forcing is systematically too strong when compared to satellite observations. The models also produce a negative systematic error in cloud amount when compared to ISCCP in the extratropics. The cloud amount error accounts for much of the error in short-wave cloud forcing because short-wave cloud forcing has a linear dependence on cloud amount. Some of the errors in short-wave cloud radiative forcing cannot be explained by errors in cloud amount and may be due to errors in cloud optical properties.

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements.

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Snow feedbacks produced by 14 atmospheric general circulation models have been analyzed through idealized numerical experiments. Included in the analysis is an investigation of the surface energy budgets of the models. Negative or weak positive snow feedbacks occurred in some of the models, while others produced strong positive snow feedbacks. These feedbacks are due not only to melting snow, but also to increases in boundary temperature, changes in air temperature, changes in water vapor, and changes in cloudiness. As a result, the net response of each model is quite complex. We analyze in detail the responses of one model with a strong positive snow feedback and another with a weak negative snow feedback. Some of the models include a temperature dependence of the snow albedo, and this has significantly affected the results.

A clear-sky flux computation method is described which is representative of the Earth Radiation Budget Experiment data processing, while at the same time being enough straightforward for implementation in a general circulation model (GCM). The method is a hybrid version of Cess and Potter (1987) Method I and Method II clear-sky top-of-the-atmosphere flux computations for GCMs. The procedure is demonstrated using the ECMWF GCM.

Responses of the surface energy budgets and hydrologic cycles of 19 atmospheric general circulation models to an imposed, globally uniform sea surface temperature perturbation of 4 K were analyzed. The responses of the simulated surface energy budgets are extremely diverse and are closely linked to the responses of the simulated hydrologic cycles. The response of the net surface energy flux is not controlled by cloud effects; instead, it is determined primarily by the response of the latent heat flux. The prescribed warming of the oceans leads to major increases in the atmospheric water vapor content and the rates of evaporation and precipitation. The increased water vapor amount drastically increases the downwelling IR radiation at the earth's surface, but the amount of the change varies dramatically from one model to another.

Extended (180-day) high resolution (T106) perpetual January and July integrations of the European Centre for Medium-Range Weather Forecasts (ECMWF) model have been analyzed in terms of the spatial and temporal characteristics of the model's convective activity in the tropics. The model's outgoing longwave radiation (OLR) is used as a surrogate for convective activity, consistent with similar studies based on satellite observations. The 3 hourly temporal sampling is sufficient to allow diagnosis of intradiurnal and interdiurnal variability; the length of the integrations is adequate for identifying lower-frequency, intraseasonal phenomena. Wherever possible, use is made of results from surface or satellite observations of the temporal characteristics of convection to verify the model results. At intradiurnal time scales the model captures the amplitude and phase of the diurnal harmonic over both land and sea. The largest amplitudes occur over the summer continents, with contrasting phases of maximum OLR depending on the presence of convective activity. Over the oceans the model shows a coherent structure to the diurnal cycle associated with regions of convection. Analysis of synoptic (2 to 10 days) and low-frequency (greater than 10 days) variability shows that in many instances the model agrees well with observations. For both seasons the modelmore » simulates westward moving phenomena over the oceans, whose phase speed is reasonable. In July these easterly waves display well-defined periodicities, in agreement with observations, while in January they are more episodic. Low-frequency variability is more prevalent in January, particularly over the convectively active regions of the eastern hemisphere. In general, this variability has a larger spatial scale than the synoptic variability; its periodicities, some in excess of 30 days, are typical of intraseasonal time scales. 56 refs., 10 figs.« less

Radiation fields from a perpetual July integration of a T106 version of the ECMWF operational model are used to identify the most appropriate way to diagnose cloud radiative forcing in a general circulation model, for the purposes of intercomparison between models. Differences between the methods 1 and 2 of Cess and Potter (1987) and a variant method are addressed. Method 1 is shown to be the least robust of all methods, due to the potential uncertainties related to persistent cloudiness, length of the sampling period, and biases in retrieved clear sky quantities due to insufficient sampling of the diurnal cycle. Method 2 is proposed as an unambiguous way to produce consistent radiative diagnostics for intercomparing model results. The impact of the three methods on the derived sensitivites and cloud feedbacks following an imposed change in sea surface temperature is discussed. The sensitivity of the results to horizontal resolution is considered by using the diagnostics from parallel integrations with T21 version of the model. 20 refs., 2 figs., 1 tab.

A method is described for analyzing the feedback and synergistic contributions of temperature, water vapor, cloud cover, surface albedo and CO2 to the change in the radiation balance at the top of the atmosphere due to a perturbation in an annual-averaged zonal atmospheric climate model. The method is illustrated through analysis of a doubled CO2 experiment with the Lawrence Livermore. National Laboratory Statistical Dynamical Model (LLNL SDM). The method provides insight into the sensitivity of the model to feedback changes in individual parameters and how each parameter influences the effects of the others.

An atmospheric radiation model is used here to illustrate several features associated with modeling the diurnal cycle of the planetary albedo. It is found that even for clear regions there appear to be deficiencies in our knowledge of how to model this quantity. The diurnal amplitude factor, defined as the ratio of the diurnally averaged planetary albedo to that at noon, between two GCMs and measurements made from a geostationary satellite. While reasonable consistency is found, the comparisons underscore difficulties associated with converting local-time albedo measurements, as made from sun-synchronous satellites, to diurnally averaged albedos.

In this paper we examine and clarify several arguments that have been put forth by Dr. S. B. Idso in an article appearing within this issue of Climatic Change.

The Oregon State University/Lawrence Livermore National Laboratory general circulation model has been employed as a vehicle for suggesting and exploring various means of converting narrow-band measurements of reflected solar radiation from the earth-atmosphere system to broad-band quantities. For purely illustrative purposes within the model's solar radiation routine, a narrow-band filter function consisting of a square-wave window extending from 0.5 to 0.9 microns is adopted. A limitation of the model, for this sort of endeavor, is that it does not include the wavelength dependence of surface albedos. Nevertheless, the model simulations tend to mimic the calibration of a narrow-band instrument, utilizing reflected solar radiation from the earth-atmosphere system as simultaneously measured by a collocated broad-band instrument; for the model, however, this is done in terms of fluxes, in contrast to instrument-measured radiances. The model results suggest that it might be preferable to perform narrow- to broad-band conversions in terms of planetary albedo (or an equivalent quantity), rather than in terms of reflected fluxes or radiances. Further improvement is achieved if, for instruments that can differentiate between clear and overcast conditions, separate clear and overcast calibrations are performed.

The sensitivity of outgoing longwave flux to changes in cloud cover (∂F/∂Ac) as defined by Cess (1976) must be evaluated carefully to avoid discrepancies arising from the interchange of averaging conventions. In a recent zonal atmospheric model experiment the global value of ∂F/∂Ac was different in sign than in other calculations. This difference in behavior was traced to a latitudinal redistribution of cloud amount and height that occurred in the doubled CO2 experiment. However, when ∂F/∂Ac was evaluated at individual latitudes and then weighted globally, the value of this parameter was consistent with those found by Cess (1976) and Budyko (1974).

Charney's (1975) suggestion that high desert albedo initiates a biogeophysical positive feedback response by the atmosphere has been tested in the 2-D zonal atmospheric model, ZAM2. The results agree with the earlier test in the NASA GISS 3-D GCM model (Charney et al. 1975) in confirming a positive feedback through precipitation but indicate a negative feedback in terms of temperature. The latter suggests that tropical deserts may play a role in air conditioning or placing an upper-bound on the temperature of our planet.

In order to better identify and more fully understand the differences in sensitivity among climate models, two quite different models are systematically compared in terms of their seasonal response. The two-dimensional statistical dynamical model (SDM) developed at the Lawrence Livermore National Laboratory and the Oregon State University three-dimensional general circulation model (GCM) were integrated using as closely comparable boundary conditions and forcing as possible. Comparison of the seasonal anomaly (defined as the departure of the monthly zonal average from the zonal annual mean at each latitude) shows that the models agree quite well in terms of the seasonal phase and amplitude of net radiation simulated at the top of the atmosphere, the tropospheric average temperature and surface temperature and the precipitation. The models also resemble the observed seasonal anomalies of these variables to a reasonable degree, although there are significant errors in each formulation.

This research note presents some of the major radiation data of a high desert mountain (approximately 14,000 ft elevation) peak during a ‘typical’ day in mid-July. The daily amounts of solar radiation and net radiation exceeded most of those reported in the literature.

A previously determined model [9], based on the theoretical knowledge of the physical modifications of solar energy by the atmosphere and its transformation at the surface, has been used to determine the monthly distribution of net radiation in southern California at a topoclimatic scale. Parameters utilized in the equations were cloud cover, cloud type, surface albedo, precipitable water vapor, surface temperature, slope angle, slope orientation, elevation, and urban sinog. A remarkable contrast was found between relatively high net radiation values for the littoral and coastal mountains and those of the lower values of the arid eastern parts of southern California.

Sagan et al.1 have suggested that the cumulative impact of anthropogenic albedo changes may have contributed to global climate changes in the past and that its effect may be continuing. Using a statistical dynamic climate model (SDM) with more realistic surface albedo changes than previously used2, we have computed the combined impact of desertification of the Sahara and deforestation of the tropical rain forest. While the model computed a surface cooling of 0.6 K for the Northern Hemisphere, the global mean of ∼0.2 K was substantially less than the 1 K suggested by Sagan et al.1. We infer that man's cumulative impact on planetary surface albedo over the past few thousand years has had a small and probably undetectable effect on global climate.