scholarly journals Production and use of regional climate model projections – A Swedish perspective on building climate services

2016 ◽  
Vol 2-3 ◽  
pp. 15-29 ◽  
Author(s):  
Erik Kjellström ◽  
Lars Bärring ◽  
Grigory Nikulin ◽  
Carin Nilsson ◽  
Gunn Persson ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Services

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>

scholarly journals Possible influence of the convection schemes in regional climate model RegCM4.6 for climate services over Central Africa

2021 ◽  
Vol 28 (2) ◽  
Author(s):  
A. J. Komkoua Mbienda ◽  
G. M. Guenang ◽  
R. S. Tanessong ◽  
S. V. Ashu Ngono ◽  
S. Zebaze ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Central Africa ◽  
Climate Services ◽  
Convection Schemes

Qinghai-Xizang (Tibetan) Plateau climate simulation using the regional climate model RegCM3

2013 ◽  
Vol 57 (3) ◽  
pp. 173-186 ◽  
Author(s):  
X Wang ◽  
M Yang ◽  
G Wan ◽  
X Chen ◽  
G Pang
Keyword(s):  
Tibetan Plateau ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Simulation

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

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