Evaluation of Clouds in the E3SM Atmosphere Model with Satellite Simulators

2020 ◽  
Author(s):  
Yuying Zhang ◽  
Shaocheng Xie ◽  
Wuyin Lin ◽  
Stephen A. Klein ◽  
Mark Zelinka ◽  
...  
Keyword(s):  
Deep Convection ◽  
Model Performance ◽  
Supercooled Liquid ◽  
The Arctic ◽  
Cmip5 Models ◽  
Model Errors ◽  
Tropical Deep Convection ◽  
Resolution Model ◽  
High Resolution Model ◽  
Atmosphere Model

<div> <div> <div> <div> <div> <div>This study systematically evaluates clouds simulated by the Energy Exascale Earth System Model (E3SM) Atmosphere Model version one (EAMv1) against satellite cloud observations. The simulator package, COSP, is used to facilitate a meaningful “apples-to-apples” comparison between model and observation by considering the different definitions of geophysical quantities among models and observations and the limitations/features of the observing process. EAMv1 is configured at two horizontal resolutions (1<span>º</span> and 0.25<span>º</span>) and one vertical resolution of 72 layers for different scientific applications. To provide a more complete picture of the model performance in simulating clouds and insights into modeled cloud biases, the evaluation is performed by utilizing unique features of individual instrument contained in COSP in observing different aspects of clouds.</div> <div> </div> <div>Both low (1deg) and high (0.25deg) resolution EAMv1 configurations generally underestimat clouds in low and midlatitudes and overestimate clouds in the Arctic although the error is smaller in the high-resolution model. The underestimate of clouds is due to the underestimate of optically thin to intermediate clouds, as EAMv1 generally overestimates optically intermediate to thick clouds. Other model errors include the largely under-predicted marine stratocumulus along the coasts and high clouds over the tropical deep convection regions. The underestimate of thin clouds results in too much LW radiation being emitted to space and too little SW radiation being reflected back to space while the overestimate of optically intermediate and thick clouds leads to too little LW radiation being emitted to space and too much SW radiation being reflected back to space. EAMv1 shows better skill in reproducing the observed distribution of clouds and their properties and has smaller radiatively relevant errors in the distribution of clouds than most of the CFMIP1 and CFMIP2 models. It produces more supercooled liquid cloud fraction than CAM5 and most CMIP5 models primarily due to a new ice nucleation scheme and secondarily due to a reduction of the ice deposition growth rate.</div> </div> </div> </div> </div> </div>

scholarly journals Tuning and assessment of the HYCOM-NORWECOM V2.1 modeling system

2014 ◽  
Vol 7 (6) ◽  
pp. 8399-8432 ◽  
Author(s):  
A. Samuelsen ◽  
C. Hansen ◽  
H. Wehde
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
Basic Research ◽  
Grazing Rate ◽  
The Arctic ◽  
Atlantic Sector ◽  
In Situ Data ◽  
Resolution Model ◽  
High Resolution Model

Abstract. The HYCOM-NORWECOM modeling system is used both for basic research and as a part of the forecasting system for the Arctic Marine Forecasting Centre through the MyOcean project. Here we present a revised version of this model. The present model, as well as the sensitivity simulations leading up to this version, has been compared to a dataset of in-situ measurements of nutrient and chlorophyll from the Norwegian Sea and the Atlantic sector of the Arctic Ocean. The revisions having most impact included adding diatoms to the diet of micro-zooplankton, increasing micro-zooplankton grazing rate and decreased silicate-to-nitrate ratio in diatoms. Model runs are performed both with a coarse- (~50 km) and higher-resolution (~15 km) model configuration, both covering the North Atlantic and Arctic Ocean. While the new model formulation improves the results in both the coarse- and high-resolution model, the nutrient bias is smaller in the high-resolution model, probably as a result of the better resolution of the main processes and with that improved circulation. The final revised version delivers satisfactory results for all three nutrients as well as improved result for chlorophyll in terms of the annual cycle amplitude. However, for chlorophyll the correlation with in-situ data remains relatively low. Besides the large uncertainties associated with observational data this is possibly caused by the fact that constant C / N and Chl / N ratios are implemented in the model.

Contribution of IASI to the observation of the dust aerosol diurnal cycle over Sahara

2020 ◽  
Author(s):  
virginie capelle ◽  
alain chedin ◽  
Noelle Scott ◽  
Martin Todd
Keyword(s):  
Diurnal Cycle ◽  
Deep Convection ◽  
Dust Emission ◽  
Saharan Dust ◽  
Dust Aerosols ◽  
Source Regions ◽  
Resolution Model ◽  
High Resolution Model ◽  
Dust Distribution ◽  
Realistic Information

<p>The Infrared Atmospheric Sounder Interferometer (IASI) is well suited for monitoring of dust aerosols because of its capability to determine both AOD and altitude of the dust layer, and because of the good match between the IASI times of observation (9.30 am and pm, local time) and the time of occurrence of the main Saharan dust uplift mechanisms. Here, starting from IASI-derived dust characteristics for an 11-year period, we assess the capability of IASI to bring realistic information on the dust diurnal cycle. We first show the morning and nighttime climatology of IASI-derived dust AOD for two major dust source regions of the Sahara: The Bodele Depression and the Adrar region. Compared with simulations from a high resolution model, permitting deep convection to be explicitly resolved, IASI performs well. In a second step, a Dust Emission Index specific to IASI is constructed, combining simultaneous information on dust AOD and mean altitude, with the aim of observing the main dust emission areas, daytime and nighttime. Comparisons are then made with other equivalent existing results derived from ground based or other satellite observations. Results demonstrate the capability of IASI to improve the documentation of dust distribution over Sahara over a long period of time. Associating observations of dust aerosols in the visible, on which a majority of aerosol studies are so far based, and in the infrared thus appears as a way to complement the results from other satellite instruments in view of improving our knowledge of their impact on climate.</p>

scholarly journals Improved Simulation of Regional Climate by Global Models with Higher Resolution: Skill Scores Correlated with Grid Length*

2015 ◽  
Vol 28 (15) ◽  
pp. 5985-6000 ◽  
Author(s):  
I. G. Watterson
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Coupled Model ◽  
Skill Score ◽  
Cmip5 Models ◽  
Four Seasons ◽  
Skill Scores ◽  
Resolution Model ◽  
High Resolution Model ◽  
Grid Length

Abstract The current generation of climate models, as represented by phase 5 of the Coupled Model Intercomparison Project (CMIP5), has previously been assessed as having more skill in simulating the observed climate than the previous ensemble from phase 3 of CMIP (CMIP3). Furthermore, the skill of models in reproducing seasonal means of precipitation, temperature, and pressure from two observational datasets, quantified by the nondimensional Arcsin–Mielke skill score, appeared to be influenced by model resolution. The analysis is extended to 42 CMIP5 and 24 CMIP3 models. For the combined skill scores for six continents, averaged over the three variables and four seasons, the correlation with model grid length in the 66-model ensemble is −0.73. Focusing on the comparison with ERA-Interim data at higher resolution and with greater regional detail, correlations are nearly as strong for scores over the ocean domain as for land. For the global domain (excluding the Antarctic cap), the correlation of the overall skill score with grid length is −0.61, and it is nearly as strong for each variable. For most tests the improved averaged score of CMIP5 models relative to those from CMIP3 is largely consistent with their increased resolution. However, the improvement for precipitation and the correlations with length are both smaller if rmse is used as a metric. They are smaller again using the GPCP observational data, as the regional detail from a high-resolution model can lead to larger differences when compared to relatively smooth observational fields.

Polar outflow from the Arctic Ocean: A high resolution model study

2010 ◽  
Vol 83 (1-2) ◽  
pp. 14-37 ◽  
Author(s):  
Yevgeny Aksenov ◽  
Sheldon Bacon ◽  
Andrew C. Coward ◽  
N. Penny Holliday
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
The Arctic ◽  
Model Study ◽  
The Arctic Ocean ◽  
Resolution Model ◽  
High Resolution Model

scholarly journals Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

2007 ◽  
Vol 7 (4) ◽  
pp. 9717-9767
Author(s):  
◽  
K. Raeder ◽  
J. L. Anderson ◽  
P. G. Hess ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Source Region ◽  
Model Performance ◽  
Chemical Data ◽  
Horizontal Wind ◽  
Model Errors ◽  
Atmosphere Model ◽  
Ne Pacific ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrates the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

scholarly journals Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission

2007 ◽  
Vol 7 (21) ◽  
pp. 5695-5710 ◽  
Author(s):  
◽  
K. Raeder ◽  
J. L. Anderson ◽  
P. G. Hess ◽  
L. K. Emmons ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Source Region ◽  
Model Performance ◽  
Chemical Data ◽  
Horizontal Wind ◽  
Model Errors ◽  
Atmosphere Model ◽  
Ne Pacific ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. We present a global chemical data assimilation system using a global atmosphere model, the Community Atmosphere Model (CAM3) with simplified chemistry and the Data Assimilation Research Testbed (DART) assimilation package. DART is a community software facility for assimilation studies using the ensemble Kalman filter approach. Here, we apply the assimilation system to constrain global tropospheric carbon monoxide (CO) by assimilating meteorological observations of temperature and horizontal wind velocity and satellite CO retrievals from the Measurement of Pollution in the Troposphere (MOPITT) satellite instrument. We verify the system performance using independent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8 aircrafts during the April 2006 part of the Intercontinental Chemical Transport Experiment (INTEX-B). Our evaluations show that MOPITT data assimilation provides significant improvements in terms of capturing the observed CO variability relative to no MOPITT assimilation (i.e. the correlation improves from 0.62 to 0.71, significant at 99% confidence). The assimilation provides evidence of median CO loading of about 150 ppbv at 700 hPa over the NE Pacific during April 2006. This is marginally higher than the modeled CO with no MOPITT assimilation (~140 ppbv). Our ensemble-based estimates of model uncertainty also show model overprediction over the source region (i.e. China) and underprediction over the NE Pacific, suggesting model errors that cannot be readily explained by emissions alone. These results have important implications for improving regional chemical forecasts and for inverse modeling of CO sources and further demonstrate the utility of the assimilation system in comparing non-coincident measurements, e.g. comparing satellite retrievals of CO with in-situ aircraft measurements.

scholarly journals Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

2021 ◽  
Author(s):  
Guokun Lyu ◽  
Nuno Serra ◽  
Meng Zhou ◽  
Detlef Stammer
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
Sea Level ◽  
The Arctic ◽  
Sea Level Variability ◽  
Model Simulations ◽  
Marginal Seas ◽  
Resolution Model ◽  
High Resolution Model ◽  
Canadian Basin

Abstract. Two high-resolution model simulations are used to investigate the spatio-temporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods < 30 days, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume ex-changes with the Pacific and Atlantic Oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation (EmPmR), ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. Satellite altimetric observations and Gravity Re-covery and Climate Experiment (GRACE) measurements could be used to infer freshwater content changes in the Canadian Basin at periods longer than one year. The increasing number of profiles seems to capture fresh-water content changes since 2007, encouraging further data synthesis work with a more complicated interpola-tion method. Further, in-situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

scholarly journals Assessment of Arctic sea ice simulations in CMIP5 models

2018 ◽  
Author(s):  
Liping Wu ◽  
Xiao-Yi Yang ◽  
Jianyu Hu
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Model Performance ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Cmip5 Models ◽  
Late 20Th Century ◽  
Arctic Sea ◽  
Potential Factors

Abstract. The Arctic sea ice cover has experienced an unprecedented decline since the late 20th century. As a result, the feedback of sea ice anomalies to atmospheric circulation has been increasingly evidenced. While the climate models almost consistently reproduce the downward trend of sea ice cover, great dispersion between them still exists. To evaluate the model performance in simulating Arctic sea ice and its potential role in climate change, we constructed a reasonable metric by synthesizing the linear trends and anomalies of the sea ice. We particularly focus on the Barents and Kara seas, where the sea ice anomalies have the greatest potential to feedback the atmosphere. Models can be grouped into three categories according to this criterion. The strong contrast among the multi-model ensemble means in different groups demonstrates the robustness and rationality of this method. The potential factors accounting for the different performance of climate models are further explored. The result shows that the model performance depends more on the ozone datasets prescribed by model rather than on the chemistry representation of ozone.

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