scholarly journals The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

2018 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

scholarly journals The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model

2019 ◽  
Vol 12 (2) ◽  
pp. 829-847 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

A Regional Climate Model Laboratory for Understanding Coastal Climate

2021 ◽  
Author(s):  
Travis O'Brien ◽  
Thomas Burkle ◽  
Michael Krauter ◽  
Thomas Trapp
Keyword(s):  
Steady State ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Theoretical Physics ◽  
Coastal Regions ◽  
Local Climate ◽  
Coastal Climate ◽  
Mean Climate

<p>Midlatitude western coastal regions are recognized as being important for the global energy cycle, marine and terrestrial biodiversity, and regional economies.  These coastal regions exhibit a rich range of weather and climate phenomena, including persistent stratocumulus clouds, sea-breeze circulations, coastally-trapped Kelvin waves, and wind-driven upwelling. During the summer season, when impacts from transient synoptic systems are relatively reduced, the local climate is governed by a complex set of interactions among the atmosphere, land, and ocean.  This complexity has so far inhibited basic understanding of the drivers of western coastal climate, climate variability, and climate change.</p><p>As a way of simplifying the system, we have developed a hierarchical regional climate model experimental framework focused on the western United States. We modify the International Centre for Theoretical Physics RegCM4 to use steady-state initial, lateral, and top-of-model boundary conditions: average July insolation (no diurnal cycle) and average meteorological state (winds, temperature, humidity, surface pressure).  This July <em>Base State</em> simulation rapidly reaches a steady state solution that closely resembles the observed mean climate and the mean climate achieved using RegCM4 in a standard reanalysis-driven configuration.  It is particularly notable that the near-coastal stratocumulus field is spatially similar to the satellite-observed stratocumulus field during arbitrary July days: including gaps in stratocumulus coverage downwind of capes. We run similar <em>Base State</em> simulations for the other calendar months and find that these simulations mimic the annual cycle.  This suggests that the summer coastal stratocumulus field results from the steady-state response of the marine boundary layer to summertime climatological forcing; if true for the real world, this would imply that stratocumulus cloud fraction, within a given month, is temporally modulated by deviations from the summer base state (e.g., transient synoptic disturbances that interrupt the cloud field).  We describe modifications to this simplified experimental framework aimed at understanding the factors that govern stratocumulus cloud fraction and its variability.</p>

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

Projected climate change over Kuwait simulated using a WRF high resolution regional climate model

2020 ◽  
Author(s):  
Hussain Alsarraf
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Regional Climate Model ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Dynamic Downscaling ◽  
Model Simulations ◽  
Average Maximum ◽  
The Impact

<p>The purpose of this study is to examine the impact of climate change on the changes on summer surface temperatures between present (2000-2010) and future (2050-2060) over the Arabian Peninsula and Kuwait. In this study, the influence of climate change in the Arabian Peninsula and especially in Kuwait was investigated by high resolution (36, 12, and 4 km grid spacing) dynamic downscaling from the Community Climate System Model CCSM4 using the WRF Weather Research and Forecasting model. The downscaling results were first validated by comparing National Centers for Environmental Prediction NCEP model outputs with the observational data. The global climate change dynamic downscaling model was run using WRF regional climate model simulations (2000-2010) and future projections (2050-2060). The influence of climate change in the Arabian Peninsula can be projected from the differences between the two period’s model simulations. The regional model simulations of the average maximum surface temperature in summertime predicted an increase from 1◦C to 3 ◦C over the summertime in Kuwait by midcentury.</p><p><strong> </strong></p>

Extending the CM SAF global satellite-based climate data record of cloud properties

2020 ◽  
Author(s):  
Irina Solodovnik ◽  
Diana Stein ◽  
Jan Fokke Meirink ◽  
Karl-Göran Karlsson ◽  
Martin Stengel
Keyword(s):  
Data Sets ◽  
Climate Data ◽  
Cloud Parameters ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Fractional Cloud ◽  
Data Record ◽  
Level 3 ◽  
Earth’S Climate ◽  
Climate Data Record

<p>Global data records of cloud properties are an important part for the analysis of the Earth's climate system and its variability. One of the few sources facilitating such records are the measurements of the satellite-based Advanced Very High Resolution Radiometer (AVHRR) sensor that provides spatially homogeneous and high resolved information in multiple spectral bands. This information can be used to retrieve global cloud properties covering multiple decades, as, for example, composed as part of the CM SAF Cloud, Albedo, Radiation data record based on AVHRR (CLARA) series.</p><p>In this presentation we introduce the edition 2.1 (CLARA-A2.1) of this record series, which is the temporally extended version of CLARA-A2. This extension includes three and a half more years at the end of the data record, which now covers the time period January 1982 to June 2019 (37.5 years). CLARA-A2.1 includes a comprehensive set of cloud parameters: fractional cloud cover, cloud top products, cloud thermodynamic phase and cloud physical properties, such as cloud optical thickness, particle effective radius and cloud water path. Cloud products are available as daily and monthly averages and histograms (Level 3) on a regular 0.25°×0.25° global grid and as daily, global composite products (Level 2b) with a spatial resolution of 0.05°×0.05°. Time series analyses of the CLARA-A2.1 cloud products show the homogeneity and stability of the extension.</p><p>In addition to the general characteristics of the CLARA-A2.1 record, we will summarize the results of the thorough evaluation efforts that were conducted by validation against reference observations (e.g. SYNOP, DARDAR, CALIOP) and by comparisons to similar well established data records (e.g. Patmos-X, ISCCP-H and MODIS C6.1). CLARA-A2.1 cloud products show generally a very good agreement with all the compared data sets and fulfil CM SAF's accuracy, precision and decadal stability requirements. As an additional aspect, we will touch upon the CLARA Interim Climate Data Record (ICDR) concept that will soon be used for extending CLARA-A2.1 in near-real-time mode.</p>

Climate Change Explorer: Extracting localized data for developing Climate Services

2021 ◽  
Author(s):  
Christine Nam ◽  
Bente Tiedje ◽  
Susanne Pfeifer ◽  
Diana Rechid ◽  
Daniel Eggert
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Climate Projections ◽  
Climate Data ◽  
Climate Services ◽  
Mitigation And Adaptation ◽  
Data Formats

<p>Everyone, politicians, public administrations, business owners, and citizens want to know how climate changes will affect them locally. Having such knowledge offers everyone the opportunity to make informed choices and take action towards mitigation and adaptation.</p><p> </p><p>In order to develop locally relevant climate service products and climate advisory services, as we do at GERICS, we must extract localized climate change information from Regional Climate Model ensemble simulations.</p><p> </p><p>Common challenges associated with developing such services include the transformation of petabytes of data from physical quantities such as precipitation, temperature, or wind, into user-applicable quantities such as return periods of heavy precipitation, e.g. for legislative or construction design frequency. Other challenges include the technical and physical barriers in the use and interpretation of climate data, due to large data volume, unfamiliar software and data formats, or limited technical infrastructure. The interpretation of climate data also requires scientific background knowledge, which limit or influence the interpretation of results.</p><p> </p><p>These barriers hinder the efficient and effective transformation of big data into user relevant information in a timely and reliable manner. To enable our society to adapt and become more resilient to climate change, we must overcome these barriers. In the Helmholtz funded Digital Earth project we are tackling these challenges by developing a Climate Change Workflow.</p><p> </p><p>In the scope of this Workflow, the user can <span>easily define a region of interest and extract </span><span>the</span><span> relevant </span><span>climate data </span><span>from the simulations available </span><span>at</span><span> the Earth System Grid Federation (ESGF). Following which, </span><span>a general overview of the projected changes, in precipitation </span><span>for example, for multiple climate projections is presented</span><span>. It conveys the bandwidth, </span><span>i.e. </span><span>the minimum/maximum range by an ensemble of regional climate model projections. </span><span>We implemented the sketched workflow in a web-based tool called </span><span>The Climate Change Explorer. </span><span>It</span> addresses barriers associated with extracting locally relevant climate data from petabytes of data, in unfamilar data formats, and deals with interpolation issues, using a more intuitive and user-friendly web interface.</p><p> </p><p>Ultimately, the Climate Change Explorer provides concise information on the magnitude of projected climate change and the range of these changes for individually defined regions, such as found in GERICS ‘Climate Fact Sheets’. This tool has the capacity to also improve other workflows of climate services, allowing them to dedicate more time in deriving user relevant climate indicies; enabling politicians, public administrations, and businesses to take action.</p>

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