CDOs for CMIP6 and Climate Extremes Indices

2020 ◽  
Author(s):  
Fabian Wachsmann
Keyword(s):  
Coupled Model ◽  
Climate Extremes ◽  
Ministry Of Education ◽  
Climate Data ◽  
Post Processing ◽  
Max Planck ◽  
Data Standard ◽  
Expert Team ◽  
Project Data ◽  
Processing Optimization

<p>The Climate Data Operators [1] tool kit (CDO) is a worldwide popular infrastructure software developed and maintained at the Max Planck Institute for Meteorology (MPI-M). It comprises a large number of command line operators for gridded data, including statistics, interpolation, or arithmetics. Users benefit from the extensive support facilities provided by the MPI-M and the DKRZ.</p><p>As a part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), the German Federal Ministry of Education and Research (BMBF) is funding activities promoting the use of the CDOs for CMIP6 data preparation and analysis.  </p><p>The operator ‘cmor’ has been developed to enable users to prepare their data according to the CMIP6 data standard. It is part of the web-based CMIP6 post-processing infrastructure [2] which is developed at DKRZ and used by different Earth System Models. The CDO metadata and its data model have been expanded to include the CMIP6 data standard so that users can use the tool for project data evaluation.</p><p>As a second activity, operators for 27 climate extremes indices, which were defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), have been integrated into the tool. As with CMIP5, the ETCCDI climate extremes indices will be part of CMIP6 model analyses due to their robustness and straightforward interpretation.</p><p>This contribution provides an insight into advanced CDO application and offers ideas for post-processing optimization. </p><p>[1] Schulzweida, U. (2019): CDO user guide. code.mpimet.mpg.de/projects/cdo , last access: 01.13.2020.</p><p>[2] Schupfner, M. (2020):  The CMIP6 Data Request WebGUI. c6dreq.dkrz.de , last access: 01.13.2020.</p>

Web-based post-processing workflow composition for CMIP6

2020 ◽  
Author(s):  
Martin Schupfner ◽  
Fabian Wachsmann
Keyword(s):  
Climate Models ◽  
Controlled Vocabulary ◽  
Model Output ◽  
Ministry Of Education ◽  
Climate Data ◽  
Post Processing ◽  
Max Planck ◽  
Web Based ◽  
Diagnostic Algorithms ◽  
Data Request

<p>CMIP6 defines a data standard as well as a data request (DReq) in order to facilitate analysis across results from different climate models. For most model output, post-processing is required to make it CMIP6 compliant. The German Federal Ministry of Education and Research (BMBF) is funding a project [1] providing services which help with the production of quality-assured CMIP6 compliant data according to the DReq. </p><p> </p><p>In that project, a web-based GUI [2] has been developed which guides the modelers through the different steps of the data post-processing workflow, allowing to orchestrate the aggregation, diagnostic and standardizing of the model data in a modular manner. Therefor the website provides several functionalities:<br>1. A DReq generator, based on Martin Juckes’ DreqPy API [3], can be used to tailor the DReq according to the envisaged experiments and supported MIPs. Moreover, the expected data volume can be calculated.</p><p>2. The mapping between variables of the DReq and of the raw model output can be specified. These specifications (model variable names, units, etc.) may include diagnostic algorithms and are stored in a database. </p><p>3. The variable mapping information can be retrieved as a mapping table (MT). Additionally, this information can be used to create post-processing script fragments. One of the script fragments contains processing commands based on the diagnostic algorithms entered into the mapping GUI, whereas the other rewrites the (diagnosed) data in a CMIP6 compliant format. Both script fragments use the CDO tool kit [4] developed at the Max Planck Institute for Meteorology, namely the CDO expr and cmor [5] operators. The latter makes use of the CMOR3 library [6] and parses the MT. The script fragments are meant to be integrated into CMIP6 data workflows or scripts. A template for such a script, that allows for a modular and flexible process control of the single workflow steps, will be included when downloading the script fragments.</p><p>4. User specific metadata can be generated, which supply the CDO cmor operator with the required and correct metadata as specified in the CMIP6 controlled vocabulary (CV).</p><p> </p><p>[1] National CMIP6 Support Activities. https://www.dkrz.de/c6de , last access 9.1.2020.</p><p>[2] Martin Schupfner (2018): CMIP6 Data Request WebGUI. https://c6dreq.dkrz.de/ , last access 9.1.2020.</p><p>[3] Martin Juckes (2018): Data Request Python API. Vers. 01.00.28. http://proj.badc.rl.ac.uk/svn/exarch/CMIP6dreq/tags/latest/dreqPy/docs/dreqPy.pdf , last access 9.1.2020.  </p><p>[4] Uwe Schulzweida (2019): CDO User Guide. Climate Data Operators. Vers. 1.9.8. https://code.mpimet.mpg.de/projects/cdo/embedded/cdo.pdf , last access 9.1.2020.</p><p>[5] Fabian Wachsmann (2017): The cdo cmor operator. https://code.mpimet.mpg.de/attachments/19411/cdo_cmor.pdf , last access 9.1.2020.</p><p>[6] Denis Nadeau (2018): CMOR version 3.3. https://cmor.llnl.gov/pdf/mydoc.pdf , last access 9.1.2020.</p>

Polyphonic Sound Event Detection Based on CapsNet-RNN and Post Processing Optimization

2020 ◽  
Author(s):  
Liujun zhang ◽  
Liyan Luo ◽  
Mei Wang ◽  
Xiyu Song ◽  
Shuting Guo ◽  
...  
Keyword(s):  
Event Detection ◽  
Post Processing ◽  
Sound Event ◽  
Sound Event Detection ◽  
Processing Optimization

scholarly journals Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

2014 ◽  
Vol 1 (1) ◽  
pp. 51-96 ◽  
Author(s):  
A. R. Ganguly ◽  
E. A. Kodra ◽  
A. Banerjee ◽  
S. Boriah ◽  
S. Chatterjee ◽  
...  
Keyword(s):  
Data Mining ◽  
Heat Waves ◽  
Climate Extremes ◽  
Computer Models ◽  
Climate Projections ◽  
Small Data ◽  
Climate Data ◽  
Statistics Of Extremes ◽  
Data Mining Techniques ◽  
Grand Challenges

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems, and human communities, worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean-land-atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "Big Data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically-based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

scholarly journals PLASIM-ENTSem: a spatio-temporal emulator of future climate change for impacts assessment

2013 ◽  
Vol 6 (2) ◽  
pp. 3349-3380 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS. Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, and the validation of the results against empirical data and higher-complexity models. We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

scholarly journals Climatological Drought Forecasting Using Bias Corrected CMIP6 Climate Data: A Case Study for India

Forecasting ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 59-84 ◽  
Author(s):  
Alen Shrestha ◽  
Md Mafuzur Rahaman ◽  
Ajay Kalra ◽  
Rohit Jogineedi ◽  
Pankaj Maheshwari
Keyword(s):  
Coupled Model ◽  
Severity Index ◽  
Extreme Drought ◽  
Drought Severity ◽  
Climate Data ◽  
Drought Forecasting ◽  
Drought Duration ◽  
Intercomparison Project ◽  
Near Future

This study forecasts and assesses drought situations in various regions of India (the Araveli region, the Bundelkhand region, and the Kansabati river basin) based on seven simulated climates in the near future (2015–2044). The self-calibrating Palmer Drought Severity Index (scPDSI) was used based on its fairness in identifying drought conditions that account for the temperature as well. Gridded temperature and rainfall data of spatial resolution of 1 km were used to bias correct the multi-model ensemble mean of the Global Climatic Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data, which were corrected on a monthly scale. The outcome of the forecast suggests multiple severe-to-extreme drought events of appreciable durations, mostly after the 2030s, under most climate scenarios in all the three study areas. The severe-to-extreme drought duration was found to last at least 20 to 30 months in the near future in all three study areas. A high-resolution drought index was developed and proven to be a key to assessing the drought situation.

scholarly journals Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1793 ◽  
Author(s):  
Najeebullah Khan ◽  
Shamsuddin Shahid ◽  
Kamal Ahmed ◽  
Tarmizi Ismail ◽  
Nadeem Nawaz ◽  
...  
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
General Circulation Models ◽  
Daily Maximum ◽  
Gridded Data ◽  
Max Planck ◽  
Meteorological Forcing ◽  
Temperature And Precipitation ◽  
Feature Selection Approach

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

scholarly journals Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques

2014 ◽  
Vol 21 (4) ◽  
pp. 777-795 ◽  
Author(s):  
A. R. Ganguly ◽  
E. A. Kodra ◽  
A. Agrawal ◽  
A. Banerjee ◽  
S. Boriah ◽  
...  
Keyword(s):  
Data Mining ◽  
Heat Waves ◽  
Climate Extremes ◽  
Computer Models ◽  
Climate Projections ◽  
Small Data ◽  
Climate Data ◽  
Statistics Of Extremes ◽  
Data Mining Techniques ◽  
Grand Challenges

Abstract. Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models, and the importance of intrinsic climate system variability at decadal horizons. Meanwhile, the growing size and complexity of climate data from model simulations and remote sensors increases opportunities to address these scientific gaps. This perspectives article explores the possibility that physically cognizant mining of massive climate data may lead to significant advances in generating credible predictive insights about climate extremes and in turn translating them to actionable metrics and information for adaptation and policy. Specifically, we propose that data mining techniques geared towards extremes can help tackle the grand challenges in the development of interpretable climate projections, predictability, and uncertainty assessments. To be successful, scalable methods will need to handle what has been called "big data" to tease out elusive but robust statistics of extremes and change from what is ultimately small data. Physically based relationships (where available) and conceptual understanding (where appropriate) are needed to guide methods development and interpretation of results. Such approaches may be especially relevant in situations where computer models may not be able to fully encapsulate current process understanding, yet the wealth of data may offer additional insights. Large-scale interdisciplinary team efforts, involving domain experts and individual researchers who span disciplines, will be necessary to address the challenge.

scholarly journals Climate Extremes over the Arabian Peninsula Using RegCM4 for Present Conditions Forced by Several CMIP5 Models

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 675 ◽  
Author(s):  
Almazroui
Keyword(s):  
Land Surface ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Coupled Model ◽  
Climate Extremes ◽  
Maximum Temperature ◽  
Cmip5 Models ◽  
Study Region ◽  
Temperature And Precipitation

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.

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