scholarly journals Arctic cloud annual cycle biases in climate models

2018 ◽  
Author(s):  
Patrick C. Taylor ◽  
Robyn C. Boeke ◽  
Ying Li ◽  
David W. J. Thompson
Keyword(s):  
Influencing Factors ◽  
Annual Cycle ◽  
Climate Models ◽  
Cloud Amount ◽  
Thermodynamic Characteristics ◽  
The Arctic ◽  
Total Cloud Amount ◽  
Air Temperatures ◽  
Low Cloud ◽  
Cloud Ice

Abstract. Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud influencing factors that contribute to winter-summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, not high, clouds; the largest differences occur between the surface and 950 hPa. Stratifying cloud amount by cloud influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Inter-group differences in low cloud amount are found to be a function of the dependence of low cloud amount on the lower tropospheric thermodynamic characteristics. We find that models with a larger low cloud amount in winter produce more cloud ice, whereas models with a larger low cloud amount in summer produce more cloud liquid. Thus, the parameterization of ice microphysics, specifically the ice formation mechanism (deposition vs. immersion freezing) and cloud liquid and ice partitioning, contributes to the inter-model differences in the Arctic cloud annual cycle and provides further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.

scholarly journals Arctic cloud annual cycle biases in climate models

2019 ◽  
Vol 19 (13) ◽  
pp. 8759-8782 ◽  
Author(s):  
Patrick C. Taylor ◽  
Robyn C. Boeke ◽  
Ying Li ◽  
David W. J. Thompson
Keyword(s):  
Influencing Factors ◽  
Annual Cycle ◽  
Climate Models ◽  
Cloud Amount ◽  
Thermodynamic Characteristics ◽  
The Arctic ◽  
Condensate Fraction ◽  
Total Cloud Amount ◽  
Low Cloud ◽  
Cloud Ice

Abstract. Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950 hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system.

International Satellite Cloud Climatology Project: Extending the Record

2021 ◽  
pp. 1-62
Author(s):  
William B. Rossow ◽  
Kenneth R. Knapp ◽  
Alisa H Young
Keyword(s):  
Product Design ◽  
Cloud Amount ◽  
Cloud Microphysics ◽  
Temperature Threshold ◽  
Ancillary Data ◽  
Total Cloud Amount ◽  
High Cloud ◽  
Enso Events ◽  
Cloud Properties ◽  
Low Cloud

AbstractISCCP continues to quantify the global distribution and diurnal-to-interannual variations of cloud properties in a revised version. This paper summarizes assessments of the previous version, describes refinements of the analysis and enhanced features of the product design, discusses the few notable changes in the results, and illustrates the long-term variations of global mean cloud properties and differing high cloud changes associated with ENSO. The new product design includes a global, pixel-level product on a 0.1°?grid, all other gridded products at 1.0°-equivalent equal-area, separate-satellite products with ancillary data for regional studies, more detailed, embedded quality information, and all gridded products in netCDF format. All the data products including all input data), expanded documentation, the processing code and an Operations Guide are available online. Notable changes are: (1) a lowered ice-liquid temperature threshold, (2) a treatment of the radiative effects of aerosols and surface temperature inversions, (3) refined specification of the assumed cloud microphysics, and (4) interpolation of the main daytime cloud information overnight. The changes very slightly increase the global monthly mean cloud amount with a little more high and a little less middle and low cloud. Over the whole period, total cloud amount slowly decreases caused by decreases in cumulus/altocumulus; consequently, average cloud top temperature and optical thickness have increased. The diurnal and seasonal cloud variations are very similar to earlier versions. Analysis of the whole record shows that high cloud variations, but not low clouds, exhibit different patterns in different ENSO events.

Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth

2012 ◽  
Vol 25 (11) ◽  
pp. 3736-3754 ◽  
Author(s):  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
Dennis L. Hartmann
Keyword(s):  
Optical Depth ◽  
Climate Models ◽  
Negative Impact ◽  
Positive Impact ◽  
Cloud Amount ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Positive Contribution ◽  
Total Cloud Amount ◽  
Cloud Feedbacks

Cloud radiative kernels and histograms of cloud fraction, both as functions of cloud-top pressure and optical depth, are used to quantify cloud amount, altitude, and optical depth feedbacks. The analysis is applied to doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. Global, annual, and ensemble mean longwave (LW) and shortwave (SW) cloud feedbacks are positive, with the latter nearly twice as large as the former. The robust increase in cloud-top altitude in both the tropics and extratropics is the dominant contributor to the positive LW cloud feedback. The negative impact of reductions in cloud amount offsets more than half of the positive impact of rising clouds on LW cloud feedback, but the magnitude of compensation varies considerably across the models. In contrast, robust reductions in cloud amount make a large and virtually unopposed positive contribution to SW cloud feedback, though the intermodel spread is greater than for any other individual feedback component. Overall reductions in cloud amount have twice as large an impact on SW fluxes as on LW fluxes, such that the net cloud amount feedback is moderately positive, with no models exhibiting a negative value. As a consequence of large but partially offsetting effects of cloud amount reductions on LW and SW feedbacks, both the mean and intermodel spread in net cloud amount feedback are smaller than those of the net cloud altitude feedback. Finally, the study finds that the large negative cloud feedback at high latitudes results from robust increases in cloud optical depth, not from increases in total cloud amount as is commonly assumed.

scholarly journals Arctic Energy Budget in Relation to Sea Ice Variability on Monthly-to-Annual Time Scales

2015 ◽  
Vol 28 (16) ◽  
pp. 6335-6350 ◽  
Author(s):  
F. Krikken ◽  
W. Hazeleger
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Energy Budget ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Albedo Feedback ◽  
Arctic Sea ◽  
Cloud Ice

Abstract The large decrease in Arctic sea ice in recent years has triggered a strong interest in Arctic sea ice predictions on seasonal-to-decadal time scales. Hence, it is important to understand physical processes that provide enhanced predictability beyond persistence of sea ice anomalies. This study analyzes the natural variability of Arctic sea ice from an energy budget perspective, using 15 climate models from phase 5 of CMIP (CMIP5), and compares these results to reanalysis data. The authors quantify the persistence of sea ice anomalies and the cross correlation with the surface and top-of-atmosphere energy budget components. The Arctic energy balance components primarily indicate the important role of the seasonal ice–albedo feedback, through which sea ice anomalies in the melt season reemerge in the growth season. This is a robust anomaly reemergence mechanism among all 15 climate models. The role of the ocean lies mainly in storing heat content anomalies in spring and releasing them in autumn. Ocean heat flux variations play only a minor role. Confirming a previous (observational) study, the authors demonstrate that there is no direct atmospheric response of clouds to spring sea ice anomalies, but a delayed response is evident in autumn. Hence, there is no cloud–ice feedback in late spring and summer, but there is a cloud–ice feedback in autumn, which strengthens the ice–albedo feedback. Anomalies in insolation are positively correlated with sea ice variability. This is primarily a result of reduced multiple reflection of insolation due to an albedo decrease. This effect counteracts the ice-albedo effect up to 50%. ERA-Interim and Ocean Reanalysis System 4 (ORAS4) confirm the main findings from the climate models.

scholarly journals Scheme for calculation of multi-layer cloudiness and precipitation for climate models of intermediate complexity

2013 ◽  
Vol 6 (2) ◽  
pp. 3241-3287
Author(s):  
A. V. Eliseev ◽  
D. Coumou ◽  
A. V. Chernokulsky ◽  
V. Petoukhov ◽  
S. Petri
Keyword(s):  
Climate Models ◽  
Cloud Amount ◽  
Life Time ◽  
Cloud Water ◽  
Cloud Type ◽  
Total Cloud Amount ◽  
Convective Clouds ◽  
Intermediate Complexity ◽  
Single Column ◽  
Basic Features

Abstract. In this study we present a scheme for calculating the characteristics of multi-layer cloudiness and precipitation for climate models of intermediate complexity (EMICs). This scheme considers three-layer stratiform cloudiness and single column convective clouds. It distinguishes between ice and droplet clouds as well. Precipitation is calculated by using cloud life time, which depends on cloud type and phase as well as on statistics of synoptic and convective disturbances. The scheme is tuned to observations by using an ensemble simulation forced by the ERA-40-derived climatology for 1979–2001. Upon calibration, the scheme realistically reproduces basic features of fields of cloud amounts, cloud water path, and precipitation. The simulated globally and annually averaged total cloud amount is 0.59, and the simulated globally averaged annual precipitation is 109 cm yr-1. Both values agree with empirically-derived values. Geographical distribution and seasonal changes of calculated variables are broadly realistic as well. However, some important regional biases still remain in the scheme.

scholarly journals Arctic cloud cover bias in ECHAM6 and its sensitivity to cloud microphysics and surface fluxes

2018 ◽  
Author(s):  
Jan Kretzschmar ◽  
Marc Salzmann ◽  
Johannes Mülmenstädt ◽  
Johannes Quaas
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Cloud Amount ◽  
Atmospheric Model ◽  
Cloud Microphysics ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
The Arctic ◽  
Phase Partitioning ◽  
Low Level

Abstract. Among the many different feedback mechanisms contributing to the Arctic Amplification, clouds play a very important role in the Arctic climate system through their cloud radiative effect. It is therefore important that climate models simulate basic cloud properties like cloud cover and cloud phase correctly. We compare results from the global atmospheric model ECHAM6 to observations from the CALIPSO satellite active lidar instrument using the COSP satellite simulator. Our results show that the model is able to reproduce the spatial distribution and cloud amount in the Arctic to some extent, but that cloud cover has a positive bias (caused by an overestimation of low-level, liquid containing cloud) in regions where the surface is covered by snow or ice. We explored the sensitivity of cloud cover to the strength of surface heat fluxes, but only by increasing surface mixing the observed cloud cover bias cloud be reduced. As ECHAM6 already mixes too strongly in the Arctic, the cloud cover bias can mainly be attributed to cloud microphysical processes. Improvements in the phase partitioning of Arctic low-level clouds could be achieved by a more effective Wegener–Bergeron–Findeisen process but total cloud cover remained still overestimated. By allowing for a slight supersaturation with respect to ice within the cloud cover scheme, we were able to also reduce this positive cloud cover bias.

scholarly journals Cloud vertical distribution from combined surface and space radar/lidar observations at two Arctic atmospheric observations

2017 ◽  
Author(s):  
Yinghui Liu ◽  
Matthew D. Shupe ◽  
Zhien Wang ◽  
Gerald Mace
Keyword(s):  
Vertical Distribution ◽  
Annual Cycle ◽  
Climate Models ◽  
Cloud Fraction ◽  
The Arctic ◽  
Active Sensors ◽  
Cloud Property ◽  
Microphysical Properties ◽  
Cloud Water Content ◽  
Vertical Distributions

Abstract. Detailed and accurate vertical distributions of cloud properties (such as cloud fraction, cloud phase, and cloud water content) and their changes are essential to accurately calculate the surface radiative flux and to depict the mean climate state. Surface- and space-based active sensors including radar and lidar are ideal to provide this information due to their superior capability to detect clouds and retrieve cloud microphysical properties. In this study, we compared the annual cycles of cloud property vertical distributions from satellite active sensors and surface-based active sensors at two Arctic atmospheric observation stations, Barrow and Eureka. We used this data to identify the sensors’ respective strengths and limitations and to develop a blended cloud property vertical distribution by combining both sets of observations. Results show that surface-based observations offer a more detailed cloud property vertical distribution from the surface up to 11 km above mean sea level (AMSL) with limitations in the middle and high altitudes; the annual mean total cloud fraction from space-based observations see 25–40 % fewer clouds below 0.5 km than that from surface-based observations, and space-based observations also show much less ice cloud and mixed phase cloud, and slightly greater liquid cloud from the surface to 1 km; space-based observations show comparable cloud fraction between 1 km and 2 km AMSL, and greater cloud fraction above 2 km AMSL than that from surface-based observations. The blended product combines the strength of both products to provide a more reliable annual cycle of cloud property vertical distribution annual cycle from the surface to 11 km AMSL. This information can be valuable for deriving an accurate surface radiative budget in the Arctic and for cloud parameterization evaluation in weather and climate models.

scholarly journals Elucidating ice formation pathways in the aerosol–climate model ECHAM6-HAM2

2019 ◽  
Vol 19 (14) ◽  
pp. 9061-9080 ◽  
Author(s):  
Remo Dietlicher ◽  
David Neubauer ◽  
Ulrike Lohmann
Keyword(s):  
Temperature Regime ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Cloud Amount ◽  
Supercooled Liquid ◽  
Cloud Microphysics ◽  
Global Climate Models ◽  
Ice Formation ◽  
Total Cloud Amount

Abstract. Cloud microphysics schemes in global climate models have long suffered from a lack of reliable satellite observations of cloud ice. At the same time there is a broad consensus that the correct simulation of cloud phase is imperative for a reliable assessment of Earth's climate sensitivity. At the core of this problem is understanding the causes for the inter-model spread of the predicted cloud phase partitioning. This work introduces a new method to build a sound cause-and-effect relation between the microphysical parameterizations employed in our model and the resulting cloud field by analysing ice formation pathways. We find that freezing processes in supercooled liquid clouds only dominate ice formation in roughly 6 % of the simulated clouds, a small fraction compared to roughly 63 % of the clouds governed by freezing in the cirrus temperature regime below −35 ∘C. This pathway analysis further reveals that even in the mixed-phase temperature regime between −35 and 0 ∘C, the dominant source of ice is the sedimentation of ice crystals that originated in the cirrus regime. The simulated fraction of ice cloud to total cloud amount in our model is lower than that reported by the CALIPSO-GOCCP satellite product. This is most likely caused by structural differences of the cloud and aerosol fields in our model rather than the microphysical parametrizations employed.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

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