Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM long-term ground-based measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Comparisons are performed for three climate regimes as represented by the Department of Energy Atmospheric Radiation Measurement (ARM) sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). Our intercomparisons of three independent measurements of CF or sky-cover reveal that the relative differences are usually less than 10% (5%) for multi-year monthly (annual) mean values, while daily differences are quite significant. The total sky imager (TSI) produces smaller total cloud fraction (TCF) compared to a radar/lidar dataset for highly cloudy days (CF > 0.8), but produces a larger TCF value than the radar/lidar for less cloudy conditions (CF < 0.3). The compensating errors in lower and higher CF days result in small biases of TCF between the vertically pointing radar/lidar dataset and the hemispheric TSI measurements as multi-year data is averaged. The unique radar/lidar CF measurements enable us to evaluate seasonal variation of cloud vertical structures in the GCMs. Both inter-model deviation and model bias against observation are investigated in this study. Another unique aspect of this study is that we use simultaneous measurements of CF and surface radiative fluxes to diagnose potential discrepancies among the GCMs in representing other cloud optical properties than TCF. The results show that the model-observation and inter-model deviations have similar magnitudes for the TCF and the normalized cloud effect, and these deviations are larger than those in surface downward solar radiation and cloud transmissivity. This implies that other dimensions of cloud in addition to cloud amount, such as cloud optical thickness and/or cloud height, have a similar magnitude of disparity as TCF within the GCMs, and suggests that the better agreement among GCMs in solar radiative fluxes could be a result of compensating effects from errors in cloud vertical structure, overlap assumption, cloud optical depth and/or cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation. The GCM performance in simulating the probability distribution, transmissivity and vertical profiles of cloud are comprehensively evaluated over the three ARM sites. The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF at SGP and cloud transmissivity is less susceptible to the change of TCF than observed. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high-level CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating discrepancies in the representation of ice cloud associated with convection in the models. While the GCMs generally capture the maximum CF in the boundary layer and vertical variability, the inter-model deviation is largest near the surface over the Arctic.

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Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-14933-2011 ◽

2011 ◽

Vol 11 (5) ◽

pp. 14933-14990

Author(s):

Y. Qian ◽

C. Long ◽

H. Wang ◽

J. Comstock ◽

S. A. McFarlane ◽

...

Keyword(s):

Seasonal Variation ◽

Optical Depth ◽

Vertical Structure ◽

Climate Models ◽

Cloud Fraction ◽

The Other ◽

Radiative Fluxes ◽

Cloud Optical Depth ◽

The Tropics ◽

Ipcc Ar4

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM ground measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Our intercomparisons of three CF or sky-cover related datasets reveal that the relative differences are usually less than 10 % (5 %) for multi-year monthly (annual) mean values, while daily differences are quite significant. The results also show that the model-observation and inter-model deviations have similar magnitudes for the total CF (TCF) and the normalized cloud effect, and these deviations are twice as large as the deviations in surface downward solar radiation and cloud transmissivity. This implies that other cloud properties, such as cloud optical depth and height, have a similar magnitude of disparity to TCF among the GCMs, and suggests that the better agreement among the GCMs in solar radiative fluxes is the result of compensating errors in cloud vertical structure, cloud optical depth and cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is very minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation. Differences are found in GCM performance over the three ARM sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF and cloud transmissivity is less susceptible to the change of TCF than observed. Much larger inter-model deviation and model bias are found over NSA than the other sites, suggesting that the Arctic region continues to challenge cloud simulations in climate models. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high altitude CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating difficulties in the representation of ice cloud associated with convection in the models.

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The Double-ITCZ Problem in IPCC AR4 Coupled GCMs: Ocean–Atmosphere Feedback Analysis

Journal of Climate ◽

10.1175/jcli4272.1 ◽

2007 ◽

Vol 20 (18) ◽

pp. 4497-4525 ◽

Cited By ~ 421

Author(s):

Jia-Lin Lin

Keyword(s):

Cloud Amount ◽

Equatorial Pacific ◽

Bjerknes Feedback ◽

Trade Winds ◽

Feedback Analysis ◽

Tropical Precipitation ◽

Double Itcz ◽

Ocean Atmosphere ◽

The Tropics ◽

Ipcc Ar4

Abstract This study examines the double–intertropical convergence zone (ITCZ) problem in the coupled general circulation models (CGCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The twentieth-century climate simulations of 22 IPCC AR4 CGCMs are analyzed, together with the available Atmospheric Model Intercomparison Project (AMIP) runs from 12 of them. To understand the physical mechanisms for the double-ITCZ problem, the main ocean–atmosphere feedbacks, including the zonal sea surface temperature (SST) gradient–trade wind feedback (or Bjerknes feedback), the SST–surface latent heat flux (LHF) feedback, and the SST–surface shortwave flux (SWF) feedback, are studied in detail. The results show that most of the current state-of-the-art CGCMs have some degree of the double-ITCZ problem, which is characterized by excessive precipitation over much of the Tropics (e.g., Northern Hemisphere ITCZ, South Pacific convergence zone, Maritime Continent, and equatorial Indian Ocean), and are often associated with insufficient precipitation over the equatorial Pacific. The excessive precipitation over much of the Tropics usually causes overly strong trade winds, excessive LHF, and insufficient SWF, leading to significant cold SST bias in much of the tropical oceans. Most of the models also simulate insufficient latitudinal asymmetry in precipitation and SST over the eastern Pacific and Atlantic Oceans. The AMIP runs also produce excessive precipitation over much of the Tropics, including the equatorial Pacific, which also leads to overly strong trade winds, excessive LHF, and insufficient SWF. This suggests that the excessive tropical precipitation is an intrinsic error of the atmospheric models, and that the insufficient equatorial Pacific precipitation in the coupled runs of many models comes from ocean–atmosphere feedback. Feedback analysis demonstrates that the insufficient equatorial Pacific precipitation in different models is associated with one or more of the following three biases in ocean–atmosphere feedback over the equatorial Pacific: 1) excessive Bjerknes feedback, which is caused by excessive sensitivity of precipitation to SST and overly strong time-mean surface wind speed; 2) overly positive SST–LHF feedback, which is caused by excessive sensitivity of surface air humidity to SST; and 3) insufficient SST–SWF feedback, which is caused by insufficient sensitivity of cloud amount to precipitation. Off the equator over the eastern Pacific stratus region, most of the models produce insufficient stratus–SST feedback associated with insufficient sensitivity of stratus cloud amount to SST, which may contribute to the insufficient latitudinal asymmetry of SST in their coupled runs. These results suggest that the double-ITCZ problem in CGCMs may be alleviated by reducing the excessive tropical precipitation and the above feedback-relevant errors in the atmospheric models.

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Arctic Radiative Fluxes: Present-Day Biases and Future Projections in CMIP5 Models

Journal of Climate ◽

10.1175/jcli-d-14-00801.1 ◽

2015 ◽

Vol 28 (15) ◽

pp. 6019-6038 ◽

Cited By ~ 24

Author(s):

Jason M. English ◽

Andrew Gettelman ◽

Gina R. Henderson

Keyword(s):

Sea Ice ◽

Snow Cover ◽

Radiant Energy ◽

Cloud Amount ◽

The Arctic ◽

Cmip5 Models ◽

Snow Cover Area ◽

Radiative Fluxes ◽

Future Projections ◽

Snow Cover Extent

Abstract Radiative fluxes are critical for understanding the energy budget of the Arctic region, where the climate has been changing rapidly and is projected to continue to change. This work investigates causes of present-day biases and future projections of top-of-atmosphere (TOA) Arctic radiative fluxes in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Compared to Clouds and the Earth’s Radiant Energy System Energy Balanced and Filled (CERES-EBAF), CMIP5 net TOA downward shortwave (SW) flux biases are larger than outgoing longwave radiation (OLR) biases. The primary contributions to modeled TOA SW flux biases are biases in cloud amount and snow cover extent compared to the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) and the newly developed Making Earth System Data Records for Use in Research Environments (MEaSUREs) dataset, respectively (with most models predicting insufficient cloud amount and snow cover in the Arctic), and biases with sea ice albedo. Future projections (2081–90) with representative concentration pathway 8.5 (RCP8.5) simulations suggest increasing net TOA downward SW fluxes (+8 W m−2) over the Arctic basin due to a decrease of surface albedo from melting snow and ice, and increasing OLR (+6 W m−2) due to an increase in surface temperatures. The largest contribution to future Arctic net TOA downward SW flux increases is declining sea ice area, followed by declining snow cover area on land, reductions to sea ice albedo, and reductions to snow albedo on land. Cloud amount is not projected to change significantly. These results suggest the importance of accurately representing both the surface area and albedos of sea ice and snow cover as well as cloud amount in order to accurately represent TOA radiative fluxes for the present-day climate and future projections.

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Evaluation and Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses over the Arctic Using BSRN Surface Observations

Journal of Climate ◽

10.1175/jcli-d-11-00147.1 ◽

2012 ◽

Vol 25 (7) ◽

pp. 2291-2305 ◽

Cited By ~ 64

Author(s):

Behnjamin J. Zib ◽

Xiquan Dong ◽

Baike Xi ◽

Aaron Kennedy

Keyword(s):

Radiation Flux ◽

Department Of Energy ◽

Surface Radiation ◽

The Arctic ◽

Climate Forecast System Reanalysis ◽

Radiative Fluxes ◽

Radiation Fields ◽

Cloud Properties ◽

Modern Era ◽

Observing Systems

Abstract With continual advancements in data assimilation systems, new observing systems, and improvements in model parameterizations, several new atmospheric reanalysis datasets have recently become available. Before using these new reanalyses it is important to assess the strengths and underlying biases contained in each dataset. A study has been performed to evaluate and compare cloud fractions (CFs) and surface radiative fluxes in several of these latest reanalyses over the Arctic using 15 years (1994–2008) of high-quality Baseline Surface Radiation Network (BSRN) observations from Barrow (BAR) and Ny-Alesund (NYA) surface stations. The five reanalyses being evaluated in this study are (i) NASA's Modern-Era Retrospective analysis for Research and Applications (MERRA), (ii) NCEP's Climate Forecast System Reanalysis (CFSR), (iii) NOAA's Twentieth Century Reanalysis Project (20CR), (iv) ECMWF's Interim Reanalysis (ERA-I), and (v) NCEP–Department of Energy (DOE)'s Reanalysis II (R2). All of the reanalyses show considerable bias in reanalyzed CF during the year, especially in winter. The large CF biases have been reflected in the surface radiation fields, as monthly biases in shortwave (SW) and longwave (LW) fluxes are more than 90 (June) and 60 W m−2 (March), respectively, in some reanalyses. ERA-I and CFSR performed the best in reanalyzing surface downwelling fluxes with annual mean biases less than 4.7 (SW) and 3.4 W m−2 (LW) over both Arctic sites. Even when producing the observed CF, radiation flux errors were found to exist in the reanalyses suggesting that they may not always be dependent on CF errors but rather on variations of more complex cloud properties, water vapor content, or aerosol loading within the reanalyses.

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The Identity and Distribution of Species of Phytoseius Ribaga (Acarina: Phytoseiidae) in Canada,

The Canadian Entomologist ◽

10.4039/ent97897-9 ◽

1965 ◽

Vol 97 (9) ◽

pp. 897-909 ◽

Cited By ~ 7

Author(s):

D. A. Chant

Keyword(s):

The Arctic ◽

Deciduous Trees ◽

The Tropics

AbstractMites of the genus Phytoseius Ribaga largely inhabit plants and are at least partly predacious, feeding on tetranychid, eriophyid, and other mites. They probably also feed on pollen, honeydew, and plant juices, as do other phytoseiids that have been studied (Chant 1959; Dosse 1961; McMurty and Scriven 1964). They are not usually found in soil or humus but occur on many kinds of low growing plants as well as coniferous and deciduous trees. They have been collected on all continents and from the arctic to the tropics.

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A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part II: Single-Column Modeling of Arctic Clouds

Journal of the Atmospheric Sciences ◽

10.1175/jas3447.1 ◽

2005 ◽

Vol 62 (6) ◽

pp. 1678-1693 ◽

Cited By ~ 72

Author(s):

H. Morrison ◽

J. A. Curry ◽

M. D. Shupe ◽

P. Zuidema

Keyword(s):

Climate Models ◽

Heat Budget ◽

Number Concentration ◽

The Arctic ◽

Liquid Water Path ◽

Microphysics Scheme ◽

Radiative Fluxes ◽

Arctic Clouds ◽

Single Column ◽

Double Moment

Abstract The new double-moment microphysics scheme described in Part I of this paper is implemented into a single-column model to simulate clouds and radiation observed during the period 1 April–15 May 1998 of the Surface Heat Budget of the Arctic (SHEBA) and First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) field projects. Mean predicted cloud boundaries and total cloud fraction compare reasonably well with observations. Cloud phase partitioning, which is crucial in determining the surface radiative fluxes, is fairly similar to ground-based retrievals. However, the fraction of time that liquid is present in the column is somewhat underpredicted, leading to small biases in the downwelling shortwave and longwave radiative fluxes at the surface. Results using the new scheme are compared to parallel simulations using other microphysics parameterizations of varying complexity. The predicted liquid water path and cloud phase is significantly improved using the new scheme relative to a single-moment parameterization predicting only the mixing ratio of the water species. Results indicate that a realistic treatment of cloud ice number concentration (prognosing rather than diagnosing) is needed to simulate arctic clouds. Sensitivity tests are also performed by varying the aerosol size, solubility, and number concentration to explore potential cloud–aerosol–radiation interactions in arctic stratus.

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Potential predictability of Southwest US rainfall : role of tropical and high-latitude variability

Journal of Climate ◽

10.1175/jcli-d-21-0775.1 ◽

2021 ◽

pp. 1-45

Keyword(s):

Time Scale ◽

Climate Model ◽

Southern Oscillation ◽

Seasonal Prediction ◽

Seasonal Rainfall ◽

The Arctic ◽

Sea Ice Concentration ◽

Potential Predictability ◽

Subseasonal Variability ◽

The Tropics

Abstract This study explores the potential predictability of Southwest US (SWUS) precipitation for the November-March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden-Julian Oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely.

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Global mapping of seasonal variations in tides from tide gauge and satellite altimeter data

10.5194/egusphere-egu21-10717 ◽

2021 ◽

Author(s):

Inger Bij de Vaate ◽

Henrique Guarneri ◽

Cornelis Slobbe ◽

Martin Verlaan

Keyword(s):

Seasonal Variation ◽

Sea Level ◽

Seasonal Variations ◽

Large Scale ◽

Arctic Region ◽

The Arctic ◽

Altimeter Data ◽

Tide Gauges ◽

Tidal Constituents ◽

The Arctic Region

The existence of seasonal variations in major tides has been recognized since decades. Where Corkan (1934) was the first to describe the seasonal perturbation of the M2 tide, many others have studied seasonal variations in the main tidal constituents since. However, most of these studies are based on sea level observations from tide gauges and are often restricted to coastal and shelf regions. Hence, observed seasonal variations are typically dominated by local processes and the large-scale patterns cannot be clearly distinguished. Moreover, most tide models still perceive tides as annually constant and seasonal variation in tides is ignored in the correction process of satellite altimetry. This results in reduced accuracy of obtained sea level anomalies. To gain more insight in the large-scale seasonal variations in tides, we supplemented the clustered and sparsely distributed sea level observations from tide gauges by the wealth of data from satellite altimeters. Although altimeter-derived water levels are being widely used to obtain tidal constants, only few of these implementations consider seasonal variation in tides. For that reason, we have set out to explore the opportunities provided by altimeter data for deriving seasonal modulation of the main tidal constituents. Different methods were implemented and compared for the principal tidal constituents and a range of geographical domains, using data from a selection of satellite altimeters. Specific attention was paid to the Arctic region where seasonal variation in tides was expected to be significant as a result of the seasonal sea ice cycle, yet data availability is particularly limited. Our study demonstrates the potential of satellite altimetry for the quantification of seasonal modulation of tides and suggests the seasonal modulation to be considerable. Already for M2 we observed changes in tidal amplitude of the order of decimeters for the Arctic region, and centimeters for lower latitude regions.&#160;<div>Corkan, R. H. (1934). An annual perturbation in the range of tide. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 144(853), 537-559.</div>

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Radar-based Bayesian estimation of ice crystal growth parameters within a microphysical model

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-20-0134.1 ◽

2020 ◽

Author(s):

Robert S. Schrom ◽

Marcus van Lier-Walqui ◽

Matthew R. Kumjian ◽

Jerry Y. Harrington ◽

Anders A. Jensen ◽

...

Keyword(s):

Vapor Deposition ◽

Growth Parameters ◽

Particle Density ◽

Optimal Parameter ◽

Number Concentration ◽

Department Of Energy ◽

The Arctic ◽

Model Parameters ◽

Growth Ratio ◽

Radar Measurements

AbstractThe potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model-observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. The combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

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Persistence and variability of Earth’s inter-hemispheric albedo symmetry in 19 years of CERES EBAF observations

Journal of Climate ◽

10.1175/jcli-d-20-0970.1 ◽

2021 ◽

pp. 1-62

Author(s):

Aiden Jönsson ◽

Frida A.-M. Bender

Keyword(s):

Southern Oscillation ◽

Global Scale ◽

Coupled Models ◽

Radiative Fluxes ◽

Clear Sky ◽

Data Record ◽

Aerosol Sources ◽

Mean Climate ◽

The Tropics ◽

Observational Record

AbstractDespite the unequal partitioning of land and aerosol sources between the hemispheres, Earth’s albedo is observed to be persistently symmetric about the equator. This symmetry is determined by the compensation of clouds to the clear-sky albedo. Here, the variability of this inter-hemispheric albedo symmetry is explored by decomposing observed radiative fluxes in the CERES EBAF satellite data record into components reflected by the atmosphere, clouds, and the surface. We find that the degree of inter-hemispheric albedo symmetry has not changed significantly throughout the observational record. The variability of the inter-hemispheric difference in reflected solar radiation (asymmetry) is strongly determined by tropical and subtropical cloud cover, particularly those related to non-neutral phases of the El Niño-Southern Oscillation (ENSO). As the ENSO is the most significant source of interannual variability in reflected radiation on a global scale, this underscores the inter-hemispheric albedo symmetry as a robust feature of Earth’s current annual mean climate. Comparing this feature in observations with simulations from coupled models reveals that the degree of modeled albedo symmetry is mostly dependent on biases in reflected radiation in the midlatitudes, and that models that overestimate its variability the most have larger biases in reflected radiation in the tropics. The degree of model albedo symmetry is improved when driven with historical sea surface temperatures, indicating that the degree of symmetry in Earth’s albedo is dependent on the representation of cloud responses to coupled ocean-atmosphere processes.

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