scholarly journals How Well Do Regional Climate Models Reproduce Radiation and Clouds in the Arctic? An Evaluation of ARCMIP Simulations

2008 ◽  
Vol 47 (9) ◽  
pp. 2405-2422 ◽  
Author(s):  
Michael Tjernström ◽  
Joseph Sedlar ◽  
Matthew D. Shupe
Keyword(s):  
Heat Budget ◽  
Regional Climate ◽  
Cloud Water ◽  
Surface Radiation ◽  
The Arctic ◽  
Cloud Base ◽  
Base Temperature ◽  
Model Errors ◽  
Cloud Properties ◽  
Low Clouds

Abstract Downwelling radiation in six regional models from the Arctic Regional Climate Model Intercomparison (ARCMIP) project is systematically biased negative in comparison with observations from the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment, although the correlations with observations are relatively good. In this paper, links between model errors and the representation of clouds in these models are investigated. Although some modeled cloud properties, such as the cloud water paths, are reasonable in a climatological sense, the temporal correlation of model cloud properties with observations is poor. The vertical distribution of cloud water is distinctly different among the different models; some common features also appear. Most models underestimate the presence of high clouds, and, although the observed preference for low clouds in the Arctic is present in most of the models, the modeled low clouds are too thin and are displaced downward. Practically all models show a preference to locate the lowest cloud base at the lowest model grid point. In some models this happens also to be where the observations show the highest occurrence of the lowest cloud base; it is not possible to determine if this result is just a coincidence. Different factors contribute to model surface radiation errors. For longwave radiation in summer, a negative bias is present both for cloudy and clear conditions, and intermodel differences are smaller when clouds are present. There is a clear relationship between errors in cloud-base temperature and radiation errors. In winter, in contrast, clear-sky cases are modeled reasonably well, but cloudy cases show a very large intermodel scatter with a significant bias in all models. This bias likely results from a complete failure in all of the models to retain liquid water in cold winter clouds. All models overestimate the cloud attenuation of summer solar radiation for thin and intermediate clouds, and some models maintain this behavior also for thick clouds.

scholarly journals Evaluation of Simulated Cloud Water in Low Clouds over the Beaufort Sea in Arctic System Reanalysis using ARISE Airborne In Situ Observations

2020 ◽  
Author(s):  
J. Brant Dodson ◽  
Patrick C. Taylor ◽  
Richard H. Moore ◽  
David H. Bromwich ◽  
Keith M. Hines ◽  
...  
Keyword(s):  
Beaufort Sea ◽  
Cloud Water ◽  
The Arctic ◽  
Model Output ◽  
Model Errors ◽  
Low Clouds ◽  
Cloud Water Content ◽  
Thermodynamic Conditions ◽  
Atmospheric State

Abstract. Arctic low clouds and the water they contain influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Atmospheric models struggle to accurately simulate the occurrence and properties of Arctic low clouds, stemming from errors in both the simulated atmospheric state and the dependence of cloud properties on the atmospheric state. Knowledge of the contributions from these two factors to the model errors allows for the isolation of the process contributions to the model-observation differences. We analyze the differences between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. The results show that ASR produces less total and liquid cloud water than observed along the flight track and is unable to simulate observed large in-cloud water content. Contributing to this bias, ASR is warmer by nearly 1.5 K and drier by 0.06 g kg−1 (relative humidity 4.3 % lower) than observed. Moreover, ASR produces cloud water over a much narrower range of thermodynamic conditions than shown in ARISE observations. Analyzing the ARISE-ASR differences by thermodynamic conditions, our results indicate that the differences are primarily attributed to disagreements in the cloud-thermodynamic relationships, and secondarily (but importantly) to differences in the occurrence frequency of thermodynamic regimes. The ratio of the factors is about 2 / 3 to 1 / 3. Substantial sampling uncertainties are found within low likelihood atmospheric regimes; sampling noise cannot be ruled out as a cause of observation-model differences, despite large differences. Thus, an important lesson from this analysis is that when comparing in situ airborne data and model output, one should not restrict the comparison to flight track-only model output.

Observed Relationships between Arctic Longwave Cloud Forcing and Cloud Parameters Using a Neural Network

2006 ◽  
Vol 19 (16) ◽  
pp. 4087-4104 ◽  
Author(s):  
Yonghua Chen ◽  
Filipe Aires ◽  
Jennifer A. Francis ◽  
James R. Miller
Keyword(s):  
Neural Network ◽  
Climate Models ◽  
Heat Budget ◽  
Inversion Layer ◽  
The Arctic ◽  
Cloud Base ◽  
Base Temperature ◽  
Liquid Water Path ◽  
Cloud Forcing ◽  
Cloud Parameters

Abstract A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histogram of each sensitivity. Although the mean states of the relationships agree well with those derived in a previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the cloudiness increases with a range of 0.1–0.9 W m−2 %−1. There is a saturation effect of liquid water path (LWP) on CFL. The highest sensitivity of CFL to LWP corresponds to clouds with low LWP, and sensitivity decreases as LWP increases. The sensitivity of CFL to cloud-base height (CBH) depends on whether the clouds are below or above an inversion layer. The relationship is negative for clouds higher than 0.8 km at the SHEBA site. The strongest positive relationship corresponds to clouds with low CBH. The dominant mode of the sensitivity of CFL to cloud-base temperature (CBT) is near zero and corresponds to warm clouds with base temperatures higher than −9°C. The low and high sensitivity regimes correspond to the summer and winter seasons, respectively, especially for LWP and CBT. Overall, the neural network technique is able to separate two distinct regimes of clouds that correspond to different sensitivities; that is, it captures the nonlinear behavior in the relationships. This study demonstrates a new method for evaluating nonlinear relationships between climate variables. It could also be used as an effective tool for evaluating feedback processes in climate models.

scholarly journals Evaluation of simulated cloud liquid water in low clouds over the Beaufort Sea in the Arctic System Reanalysis using ARISE airborne in situ observations

2021 ◽  
Vol 21 (15) ◽  
pp. 11563-11580
Author(s):  
J. Brant Dodson ◽  
Patrick C. Taylor ◽  
Richard H. Moore ◽  
David H. Bromwich ◽  
Keith M. Hines ◽  
...  
Keyword(s):  
Beaufort Sea ◽  
Cloud Water ◽  
The Arctic ◽  
Model Output ◽  
Model Errors ◽  
Low Clouds ◽  
Cloud Water Content ◽  
Thermodynamic Conditions ◽  
Atmospheric State

Abstract. Arctic low clouds and the water they contain influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Atmospheric models struggle to accurately simulate the occurrence and properties of Arctic low clouds, stemming from errors in both the simulated atmospheric state and the dependence of cloud properties on the atmospheric state. Knowledge of the contributions from these two factors to the model errors allows for the isolation of the process contributions to the model–observation differences. We analyze the differences between the Arctic System Reanalysis version 2 (ASR) and data taken during the September 2014 Arctic Radiation–IceBridge Sea and Ice Experiment (ARISE) airborne campaign conducted over the Beaufort Sea. The results show that ASR produces less total and liquid cloud water than observed along the flight track and is unable to simulate observed large in-cloud water content. Contributing to this bias, ASR is warmer by nearly 1.5 K and drier by 0.06 g kg−1 (relative humidity 4.3 % lower) than observed. Moreover, ASR produces cloud water over a much narrower range of thermodynamic conditions than shown in ARISE observations. Analyzing the ARISE–ASR differences by thermodynamic conditions, our results indicate that the differences are primarily attributed to disagreements in the cloud–thermodynamic relationships and secondarily (but importantly) to differences in the occurrence frequency of thermodynamic regimes. The ratio of the factors is about 2/3 to 1/3. Substantial sampling uncertainties are found within low-likelihood atmospheric regimes; sampling noise cannot be ruled out as a cause of observation–model differences, despite large differences. Thus, an important lesson from this analysis is that when comparing in situ airborne data and model output, one should not restrict the comparison to flight-track-only model output.

scholarly journals Clouds damp the radiative impacts of polar sea ice loss

2020 ◽  
Vol 14 (8) ◽  
pp. 2673-2686 ◽  
Author(s):  
Ramdane Alkama ◽  
Patrick C. Taylor ◽  
Lorea Garcia-San Martin ◽  
Herve Douville ◽  
Gregory Duveiller ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Surface Radiation ◽  
Radiation Budget ◽  
The Arctic ◽  
Cloud Properties ◽  
Surface Radiation Budget ◽  
Cloud Radiative Effect ◽  
Radiative Impact ◽  
The Impact

Abstract. Clouds play an important role in the climate system: (1) cooling Earth by reflecting incoming sunlight to space and (2) warming Earth by reducing thermal energy loss to space. Cloud radiative effects are especially important in polar regions and have the potential to significantly alter the impact of sea ice decline on the surface radiation budget. Using CERES (Clouds and the Earth's Radiant Energy System) data and 32 CMIP5 (Coupled Model Intercomparison Project) climate models, we quantify the influence of polar clouds on the radiative impact of polar sea ice variability. Our results show that the cloud short-wave cooling effect strongly influences the impact of sea ice variability on the surface radiation budget and does so in a counter-intuitive manner over the polar seas: years with less sea ice and a larger net surface radiative flux show a more negative cloud radiative effect. Our results indicate that 66±2% of this change in the net cloud radiative effect is due to the reduction in surface albedo and that the remaining 34±1 % is due to an increase in cloud cover and optical thickness. The overall cloud radiative damping effect is 56±2 % over the Antarctic and 47±3 % over the Arctic. Thus, present-day cloud properties significantly reduce the net radiative impact of sea ice loss on the Arctic and Antarctic surface radiation budgets. As a result, climate models must accurately represent present-day polar cloud properties in order to capture the surface radiation budget impact of polar sea ice loss and thus the surface albedo feedback.

scholarly journals Aggregate-area radiative flux biases

2002 ◽  
Vol 34 ◽  
pp. 101-105 ◽  
Author(s):  
Xuanji Wang ◽  
Jeffrey R. Key
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Heat Budget ◽  
Numerical Models ◽  
Radiative Flux ◽  
The Arctic ◽  
Radiative Fluxes ◽  
Cloud Properties ◽  
Model Cell ◽  
The Difference

AbstractMost climate models treat surface and atmospheric properties as being horizontally homogeneous and compute surface radiative fluxes with average gridcell properties. In this study it is found that large biases can occur if sub-gridcell variability is ignored, where bias is defined as the difference between the average of fluxes computed at high resolution within a model cell and the flux computed with the average surface and cloud properties within the cell. Data from the Advanced Very High Resolution Radiometer for the year-long Surface Heat Budget of the Arctic Ocean (SHEBA) experiment are used to determine biases in aggregate-area fluxes. A simple regression approach to correct for biases that result from horizontal variability was found to reduce the average radiative flux bias to near zero. The correction can be easily implemented in numerical models.

scholarly journals Evaluation of a Forward Operator to Assimilate Cloud Water Path into WRF-DART

2013 ◽  
Vol 141 (7) ◽  
pp. 2272-2289 ◽  
Author(s):  
Thomas A. Jones ◽  
David J. Stensrud ◽  
Patrick Minnis ◽  
Rabindra Palikonda
Keyword(s):  
Cloud Water ◽  
Cloud Base ◽  
Limited Attention ◽  
Water Path ◽  
Cloud Properties ◽  
Oklahoma Mesonet ◽  
Cloud Water Path ◽  
Mean Square Difference ◽  
The Impact ◽  
Forward Operator

Abstract Assimilating satellite-retrieved cloud properties into storm-scale models has received limited attention despite its potential to provide a wide array of information to a model analysis. Available retrievals include cloud water path (CWP), which represents the amount of cloud water and cloud ice present in an integrated column, and cloud-top and cloud-base pressures, which represent the top and bottom pressure levels of the cloud layers, respectively. These interrelated data are assimilated into an Advanced Research Weather Research and Forecasting Model (ARW-WRF) 40-member ensemble with 3-km grid spacing using the Data Assimilation Research Testbed (DART) ensemble Kalman filter. A new CWP forward operator combines the satellite-derived cloud information with similar variables generated by WRF. This approach is tested using a severe weather event on 10 May 2010. One experiment only assimilates conventional (CONV) observations, while the second assimilates the identical conventional observations and the satellite-derived CWP (PATH). Comparison of the CWP observations at 2045 UTC to CONV and PATH analyses shows that PATH has an improved representation of both the magnitude and spatial orientation of CWP compared to CONV. Assimilating CWP acts both to suppress convection in the model where none is present in satellite data and to encourage convection where it is observed. Oklahoma Mesonet observations of downward shortwave flux at 2100 UTC indicate that PATH reduces the root-mean-square difference errors in downward shortwave flux by 75 W m−2 compared to CONV. Reduction in model error is generally maximized during the initial 30-min forecast period with the impact of CWP observations decreasing for longer forecast times.

scholarly journals KARAKTERISTIK KETINGGIAN DASAR AWAN YANG DIUKUR DENGAN SENSOR INFRA MERAH RADIOMETER PADA PUNCAK MUSIM HUJAN DI JABODETABEK.

2020 ◽  
Vol 20 (1) ◽  
pp. 39-45
Author(s):  
Findy Renggono
Keyword(s):  
Lapse Rate ◽  
Cloud Base ◽  
Base Temperature ◽  
Cloud Detection ◽  
Weather Modification ◽  
Temperature Lapse Rate ◽  
Cloud Properties ◽  
Comparison Results ◽  
Rain Radar ◽  
Cloud Base Height

IntisariInformasi mengenai tinggi dasar awan penting bagi penelitian atmosfer dan juga sebagai masukan bagi pemodelan cuaca. Pada kegiatan modifikasi cuaca, informasi ini juga sangat penting dalam menentukan awan yang akan disemai. Dalam tulisan ini, pengukuran tinggi dasar awan dilakukan dengan menggunakan sensor infra merah yang terpasang pada radiometer. Sensor infra merah ini akan mengukur suhu dasar awan yang kemudian dapat diketahui ketinggiannya dengan melihat temperatur lapse rate. Hasil pengukuran dibandingkan dengan hasil pengamatan awan oleh micro rain radar yang terletak di lokasi yang sama. Hasil pengukuran dari kedua peralatan ini menunjukkan kesesuaian antara kemunculan awan pada micro rain radar yang ditunjukkan dengan struktur vertikal awan dengan hasil pengamatan dengan IRT dari radiometer. Pengamatan selama puncak musim hujan di Jabodetabek (Januari – Maret 2019) menunjukan adanya pola harian yang cukup jelas. AbstractInformation on cloud properties is important for atmospheric research and as well as for weather modeling. In weather modification, this information is very important for cloud seeding strategy. The observation of cloud base height is carried out using infrared sensors mounted on a radiometer. These infrared thermometer sensors are capable of detecting the cloud base temperature, the cloud base height is obtained by looking at the temperature lapse rate retrieved from radiometer observation. The results were compared with the cloud observation by micro rain radar which is located at the same location. The comparison results of these two instruments show that the consistency of cloud detection was good. Based on the observation during the peak of the rainy season in Jabodetabek (January-March 2019), it is shown a fairly clear daily pattern

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

2012 ◽  
Vol 25 (7) ◽  
pp. 2291-2305 ◽  
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.

scholarly journals Observations of Stratocumulus Clouds and Their Effect on the Eastern Pacific Surface Heat Budget along 20°S

2012 ◽  
Vol 25 (24) ◽  
pp. 8542-8567 ◽  
Author(s):  
Simon P. de Szoeke ◽  
Sandra Yuter ◽  
David Mechem ◽  
Chris W. Fairall ◽  
Casey D. Burleyson ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Heat Budget ◽  
Coupled Model ◽  
Longwave Radiation ◽  
Cloud Fraction ◽  
Surface Radiation ◽  
Cloud Base ◽  
Diurnal Cycles ◽  
Downward Longwave Radiation ◽  
Stratocumulus Clouds

Abstract Widespread stratocumulus clouds were observed on nine transects from seven research cruises to the southeastern tropical Pacific Ocean along 20°S, 75°–85°W in October–November of 2001–08. The nine transects sample a unique combination of synoptic and interannual variability affecting the clouds; their ensemble diagnoses longitude–vertical sections of the atmosphere, diurnal cycles of cloud properties and drizzle statistics, and the effect of stratocumulus clouds on surface radiation. Mean cloud fraction was 0.88, and 67% of 10-min overhead cloud fraction observations were overcast. Clouds cleared in the afternoon [1500 local time (LT)] to a minimum of fraction of 0.7. Precipitation radar found strong drizzle with reflectivity above 40 dBZ. Cloud-base (CB) heights rise with longitude from 1.0 km at 75°W to 1.2 km at 85°W in the mean, but the slope varies from cruise to cruise. CB–lifting condensation level (LCL) displacement, a measure of decoupling, increases westward. At night CB–LCL is 0–200 m and increases 400 m from dawn to 1600 LT, before collapsing in the evening. Despite zonal gradients in boundary layer and cloud vertical structure, surface radiation and cloud radiative forcing are relatively uniform in longitude. When present, clouds reduce solar radiation by 160 W m−2 and radiate 70 W m−2 more downward longwave radiation than clear skies. Coupled Model Intercomparison Project phase 3 (CMIP3) simulations of the climate of the twentieth century show 40 ± 20 W m−2 too little net cloud radiative cooling at the surface. Simulated clouds have correct radiative forcing when present, but models have ~50% too few clouds.

scholarly journals A comparison of ship and satellite measurements of cloud properties with global climate model simulations in the southeast Pacific stratus deck

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng
Keyword(s):  
Diurnal Cycle ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Fraction ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Southeast Pacific ◽  
Cloud Thickness

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.

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