CORDEX-WRF v1.3: Development of a module for theWeather Research and Forecasting (WRF) model to support the CORDEX community

Abstract. The Coordinated Regional Climate Downscaling Experiment (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains and data access/archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent goal in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stake holders, and scientists working on climate change mitigation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency, priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods. Most commonly, scientists need to post-process the raw output of regional climate models, since the latter was not originally designed to meet the specific CORDEX data requirements. This post-processing procedure includes the computation of diagnostics, statistics, and final homogenization of the data, which is often computationally costly and time consuming. Therefore, the development of specialized software and/or code is required. The current paper presents the development of a specialized module (version 1.3) for the Weather Research and Forecasting (WRF) model, capable of outputting the required CORDEX variables. Additional diagnostic variables not required by CORDEX, but of potential interest to the regional climate modeling community, are also included in the module. Generic definitions of variables are adopted in order to overcome model and/or physics parameterization dependence of certain diagnostics and variables, thus facilitating a robust comparison among simulations. The module is computationally optimized, and the output is divided in different priority levels following CORDEX specifications (Core, Tier1, and additional) by selecting pre-compilation flags. This implementation of the module does not add a significant extra cost when running the model, for example the addition of the Core variables slows the model time-step by less than a 5 %. The use of the module reduces the requirements of disk storage by about a 50 %.

Download Full-text

CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community

Geoscientific Model Development ◽

10.5194/gmd-12-1029-2019 ◽

2019 ◽

Vol 12 (3) ◽

pp. 1029-1066 ◽

Cited By ~ 2

Author(s):

Lluís Fita ◽

Jan Polcher ◽

Theodore M. Giannaros ◽

Torge Lorenz ◽

Josipa Milovac ◽

...

Keyword(s):

Regional Climate ◽

Regional Scale ◽

Wrf Model ◽

Data Access ◽

Quality Data ◽

Weather Research And Forecasting ◽

Time Step ◽

Mitigation And Adaptation ◽

Data Requirements ◽

Weather Research

Abstract. The Coordinated Regional Climate Downscaling Experiment (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains, and data access and archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent role in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stakeholders and scientists working on climate change mitigation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency and priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods. Most commonly, scientists need to post-process the raw output of regional climate models, since the latter was not originally designed to meet the specific CORDEX data requirements. This post-processing procedure includes the computation of diagnostics, statistics, and final homogenization of the data, which is often computationally costly and time-consuming. Therefore, the development of specialized software and/or code is required. The current paper presents the development of a specialized module (version 1.3) for the Weather Research and Forecasting (WRF) model capable of outputting the required CORDEX variables. Additional diagnostic variables not required by CORDEX, but of potential interest to the regional climate modeling community, are also included in the module. “Generic” definitions of variables are adopted in order to overcome the model and/or physics parameterization dependence of certain diagnostics and variables, thus facilitating a robust comparison among simulations. The module is computationally optimized, and the output is divided into different priority levels following CORDEX specifications (Core, Tier 1, and additional) by selecting pre-compilation flags. This implementation of the module does not add a significant extra cost when running the model; for example, the addition of the Core variables slows the model time step by less than a 5 %. The use of the module reduces the requirements of disk storage by about a 50 %. The module performs neither additional statistics over different periods of time nor homogenization of the output data.

Download Full-text

Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

10.5194/egusphere-egu21-12054 ◽

2021 ◽

Author(s):

Gaby S. Langendijk ◽

Diana Rechid ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Climate Data ◽

Urban Rural

<p>Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2&#176;C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.</p>

Download Full-text

Forecasting long-term shoreline evolution in highly antrhopized coastal areas

10.5194/egusphere-egu21-10849 ◽

2021 ◽

Author(s):

Moisés Álvarez-Cuesta ◽

Alexandra Toimil ◽

Iñigo J. Losada

Keyword(s):

Climate Models ◽

Regional Scale ◽

Mediterranean Coast ◽

Multiple Time Scales ◽

Multiple Time ◽

Coastal Zones ◽

Climate Data ◽

Shoreline Evolution ◽

Automated Technique

<p>A new numerical model for addressing long-term coastline evolution on a local to regional scale on highly anthropized coasts is presented. The model, named IH-LANS (Long-term ANthropized coastlines Simulation tool), is validated over the period 1990-2020 and applied to obtain an ensemble of end-of-century shoreline evolutions. IH-LANS combines a hybrid (statistical-numerical) deep-water propagation module and a shoreline evolution model. Longshore and cross-shore processes are integrated together with the effects of man-made interventions. For the ease of calibration, an automated technique is implemented to assimilate observations. The model is applied to a highly anthropized 40 km stretch located along the Spanish Mediterranean coast. High space-time resolution climate data and satellite-derived shorelines are used to drive IH-LANS. Observed shoreline evolution (<10 meters of root mean square error, RMSE) is successfully represented while accounting for the effects of nourishments and the construction and removal of groynes, seawalls and breakwaters over time. Then, in order to drive the ensemble of end-of-century shoreline evolutions, wave and water level projections downscaled from different climate models for various emissions scenarios are employed to force the calibrated model. From the forecasted shoreline time-series, information from multiple time-scales is unraveled yielding valuable information for coastal planners. The efficiency and accuracy of the model make IH-LANS a powerful tool for management and climate change adaptation in coastal zones.</p>

Download Full-text

The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble

Journal of Climate ◽

10.1175/jcli-d-21-0061.1 ◽

2021 ◽

pp. 1-56

Keyword(s):

Global Warming ◽

Pacific Northwest ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Wrf Model ◽

Regional Climate Models ◽

Winter Precipitation ◽

Historical Period ◽

Continental Interior

This paper describes the downscaling of an ensemble of twelve GCMs using the WRF model at 12-km grid spacing over the period 1970-2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP 8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. April 1 snowpack declines are large over the lower to middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCM’s producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the 21st century.

Download Full-text

Projecting Wet Season Rainfall Extremes Using Regional Climate Models Ensemble and the Advanced Delta Change Model: Impact on the Streamflow Peaks in Mkurumudzi Catchment, Kenya

Hydrology ◽

10.3390/hydrology6030076 ◽

2019 ◽

Vol 6 (3) ◽

pp. 76

Author(s):

Ouédraogo ◽

Gathenya ◽

Raude

Keyword(s):

Rainy Season ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Design Flow ◽

Quality Data ◽

Economic Losses ◽

Rainfall Extremes ◽

Delta Change ◽

Streamflow Extremes

Each year, many African countries experience natural hazards such as floods and, because of their low adaptative capabilities, they hardly have the means to face the consequences, and therefore suffer huge economic losses. Extreme rainfall plays a key role in the occurrence of these hazards. Therefore, climate projection studies should focus more on extremes in order to provide a wider range of future scenarios of extremes which can aid policy decision making in African societies. Some researchers have attempted to analyze climate extremes through indices reflecting extremes in climate variables such as rainfall. However, it is difficult to assess impacts on streamflow based on these indices alone, as most hydrological models require daily data as inputs. Others have analyzed climate projections through general circulation models (GCMs) but have found their resolution too coarse for regional studies. Dynamic downscaling using regional climate models (RCMs) seem to address the limitation of GCMs, although RCMs might still lack accuracy due to the fact that they also contain biases that need to be eliminated. Given these limitations, the current study combined both dynamic and statistical downscaling methods to correct biases and improve the reproduction of high extremes by the models. This study’s aim was to analyze extreme high flows under the projection of extreme wet rainfall for the horizon of 2041 of a Kenyan South Coast catchment. The advanced delta change (ADC) method was applied on observed data (1982–2005), control (1982–2005) and near future (2018–2041) from an ensemble mean of multiple regional climate models (RCMs). The created future daily rainfall time series was introduced in the HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System) hydrological model and the generated future flow were compared to the baseline flow at the gaging station 3KD06, where the observed flow was available. The findings suggested that in the study area, the RCMs, bias corrected by the ADC method, projected an increase in rainfall wet extremes in the first rainy season of the year MAMJ (March–April–May–June) and a decrease in the second rainy season OND (October–November–December). The changes in rainfall extremes, induced a similar change pattern in streamflow extremes at the gaging station 3KD06, meaning that an increase/decrease in rainfall extremes generated an increase/decrease in the streamflow extremes. Due to lack of long-term good quality data, the researchers decided to perform a frequency analysis for up to a 50 year return period in order to assess the changes induced by the ADC method. After getting a longer data series, further analysis could be done to forecast the maximum flow to up to 1000 years, which could serve as design flow for different infrastructure.

Download Full-text

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

10.5194/gmd-2018-113 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text

The KLIWAS North Sea Climatology. Part I: Processing of the Atmospheric Data

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0044.1 ◽

2018 ◽

Vol 35 (1) ◽

pp. 111-126 ◽

Cited By ~ 2

Author(s):

Remon Sadikni ◽

Nils H. Schade ◽

Axel Andersson ◽

Annika Jahnke-Bornemann ◽

Iris Hinrichs ◽

...

Keyword(s):

North Sea ◽

Climate Models ◽

Sampling Error ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Data ◽

The North ◽

Dewpoint Temperature ◽

The Baltic ◽

The University

AbstractClimatological reference data serve as validation of regional climate models, as the boundary condition for the model runs, and as input for assimilation systems used by reanalyses. Within the framework of the interdisciplinary research program Climate Water Navigation (KLIWAS): Impacts of Climate Change on Waterways and Navigation of the German Federal Ministry of Transport and Digital Infrastructure, a new climatology of the North Sea and adjacent regions was developed in an joint effort by the Federal Maritime and Hydrographic Agency, the German Weather Service [Deutscher Wetterdienst (DWD)], and the Integrated Climate Data Center (ICDC) of the University of Hamburg. Long-term records of monthly and annual mean 2-m air temperature, dewpoint temperature, and sea level pressure data from 1950 to 2010 were calculated on a horizontal 1° × 1° grid. All products were based on quality-controlled data from DWD’s Marine Data Centre. Correction methods were implemented for each parameter to reduce the sampling error resulting from the sparse coverage of observations in certain regions. Comparisons between sampling error estimates based on ERA-40 and the climatology products show that the sampling error was reduced effectively. The climatologies are available for download on the ICDC’s website and will be updated regularly regarding new observations and additional parameters. An extension to the Baltic Sea is in progress.

Download Full-text

Effects of Land Surface Schemes on WRF-Simulated Geopotential Heights over China in Summer 2003*

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0239.1 ◽

2016 ◽

Vol 17 (3) ◽

pp. 829-851 ◽

Cited By ~ 8

Author(s):

Xin-Min Zeng ◽

B. Wang ◽

Y. Zhang ◽

Y. Zheng ◽

N. Wang ◽

...

Keyword(s):

Land Surface ◽

Climate Models ◽

Regional Climate ◽

Sensible Heat Flux ◽

Wrf Model ◽

Surface Fluxes ◽

Forecast Errors ◽

Large Area ◽

Pressure Fields ◽

Temperature And Pressure

Abstract To quantify and explain effects of different land surface schemes (LSSs) on simulated geopotential height (GPH) fields, we performed simulations over China for the summer of 2003 using 12-member ensembles with the Weather Research and Forecasting (WRF) Model, version 3. The results show that while the model can generally simulate the seasonal and monthly mean GPH patterns, the effects of the LSS choice on simulated GPH fields are substantial, with the LSS-induced differences exceeding 10 gpm over a large area (especially the northwest) of China, which is very large compared with climate anomalies and forecast errors. In terms of the assessment measures for the four LSS ensembles [namely, the five-layer thermal diffusion scheme (SLAB), the Noah LSS (NOAH), the Rapid Update Cycle LSS (RUC), and the Pleim–Xiu LSS (PLEX)] in the WRF, the PLEX ensemble is the best, followed by the NOAH, RUC, and SLAB ensembles. The sensitivity of the simulated 850-hPa GPH is more significant than that of the 500-hPa GPH, with the 500-hPa GPH difference fields generally characterized by two large areas with opposite signs due to the smoothly varying nature of GPHs. LSS-induced GPH sensitivity is found to be higher than the GPH sensitivity induced by atmospheric boundary layer schemes. Moreover, theoretical analyses show that the LSS-induced GPH sensitivity is mainly caused by changes in surface fluxes (in particular, sensible heat flux), which further modify atmospheric temperature and pressure fields. The temperature and pressure fields generally have opposite contributions to changes in the GPH. This study emphasizes the importance of choosing and improving LSSs for simulating seasonal and monthly GPHs using regional climate models.

Download Full-text

Climate Change Projections of Aridity Conditions in the Iberian Peninsula

10.20944/preprints202106.0536.v1 ◽

2021 ◽

Author(s):

Cristina Andrade ◽

Joana Contente ◽

João Andrade Santos

Keyword(s):

Climate Change ◽

Water Management ◽

Iberian Peninsula ◽

Radiative Forcing ◽

Climate Models ◽

Regional Climate ◽

Aridity Index ◽

Agroforestry Systems ◽

Mitigation And Adaptation ◽

La Mancha

The assessment of aridity conditions is a key factor for water management and the implementation of mitigation and adaptation policies in agroforestry systems. Towards this aim three aridity indices were computed for the Iberian Peninsula (IP): the De Martonne Index (DMI), the Pinna Combinative Index (PCI), and the Erinç Aridity Index (EAI). These three indices were first computed for the baseline period 1961‒1990, using a gridded observational data (E-OBS), and, subsequently, for the periods 2011‒2040 (short-range) and 2041‒2070 (medium-range) using an ensemble of six Regional Climate Models (RCMs) experiments generated by the EURO-CORDEX project. Two Representative Concentration Pathways (RCPs) were analyzed, an intermediate anthropogenic radiative forcing scenario (RCP4.5) and a fossil-intensive emission scenario (RCP8.5). Overall, the three indices disclose a strengthening of aridity and dry conditions in central and southern Iberia until 2070, mainly under RCP8.5. Strong(weak) statistically significant correlations were found between these indices and the total mean precipitation (mean temperature) along with projected significant decreasing(increasing) trends for precipitation(temperature). The prevalence of years with arid conditions (above 70% for 2041‒2070 under both RCPs) are projected to have major impacts in some regions, such as southern Portugal, Extremadura, Castilla-La Mancha, Comunidad de Madrid, Andalucía, Región de Murcia, Comunidad Valenciana, and certain regions within the Aragón province. The projected increase in both the intensity and persistence of aridity conditions in a broader southern half of Iberia will exacerbate the exposure and vulnerability of this region to climate change, while the risk of multi-level desertification should be thoroughly integrated into regional and national water management and planning.

Download Full-text

Simulation performance of selected global and regional climate models for temperature and rainfall in some locations in India

Journal of Agrometeorology ◽

10.54386/jam.v22i4.443 ◽

2021 ◽

Vol 22 (4) ◽

pp. 407-418

Author(s):

SHWETA PANJWANI ◽

S. NARESH KUMAR ◽

LAXMI AHUJA

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

India Meteorological Department ◽

Regional Climate Models ◽

Climate Data ◽

Simulation Performance ◽

Climate Change Assessment ◽

Better Than

Global and regional climate models are reported to have inherent bias in simulating the observed climatology of a region. This bias of climate models is the major source of uncertainties in climate change impact assessments. Therefore, use of bias corrected simulated climate data is important. In this study, the bias corrected climate data for 30 years’ period (1976-2005) from selected common fourGCMs and RCMs for six Indian locations are compared with the respective observed data of India Meteorological Department. The analysis indicated that the RCMs performance is much better than GCMs after bias correction for minimum and maximum temperatures. Also, RCMs performance is better than GCMs in simulating extreme temperatures. However, the selected RCMs and GCMs are found to either over estimate or under estimate the rainfall despite bias correction and also overestimated the rainfall extremes for selected Indian locations. Based on the overall performance of four models for the six locations, it was found that the GFDL_ESM2M and NORESM1-M RCMs performed comparatively better than CSIRO and IPSL models. After bias correction, the RCMs could represent the observed climatology better than the GCMs. And these RCMs viz., GFDL_ESM2M and NORESM1-M can be usedindividually after bias correction in the climate change assessment studies for the selected regions.

Download Full-text