Climadjust: easing the Bias Adjustment process through a user-friendly web service

Climate projections obtained from global and regional climate models usually exhibit biases: systematic deviations from observations. Adjusting these biases is typically the first step towards obtaining actionable climate information to be used in impact studies. However, this bias adjustment process is highly technical and demands a lot of resources, both infrastructures (e.g. access to high performance and cloud computing) &#8212;particularly for continental wide applications&#8212; and human (e.g. personnel specialised in climate data post-processing).Climadjust (accessible through https://climadjust.com/) is a web service developed with the support of the Copernicus Climate Change Service&#160; implementing user-friendly bias adjustment for climate projections from the C3S catalogue using customized methods and reference datasets. The service was developed by Predictia &#8212;a company with a strong focus on climate services development and climate modelling&#8212; in collaboration with the Spanish Research Council (CSIC).&#160;Climadjust provides scalable cloud resources to compute bias-adjusted climate projections from the ensembles of CMIP and CORDEX datasets or customized areas of interest. In this process, the users are able to (i) upload their own dataset of observations to adjust the climate projections, or choose among reference datasets such as ERA5-Land or WFDE-5, (ii) choose among six state-of-the-art Bias Adjustment techniques implemented using the open source Climate4R package, and (iii) validate the results through the standard framework developed in the European VALUE COST Action. The output is a validated netCDF file, ready to be used by the climate modellers working in climate studies.This climate service is targeted at the end tail of the downstream market of climate services, namely climate modellers working in sectoral climate adaptation in the agriculture, hydrology, biodiversity, insurance and forestry management fields, among others. Currently, the service counts with over 100 registered users.To promote the user uptake of the service, the project faced several barriers, such as a lack of understanding on the need of adjusting biases by the end-users, and communication barriers between the climate science community and the end-user community. The session will present the lessons learnt during the user uptake campaigns, the user needs gathered through the user engagement activities performed within it, as well as relevant use-cases of the service, developed hand in hand with the end users.

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The VIADUC project: innovation in climate adaptation through service design

Advances in Science and Research ◽

10.5194/asr-12-199-2015 ◽

2015 ◽

Vol 12 (1) ◽

pp. 199-205

Author(s):

L. Corre ◽

P. Dandin ◽

D. L'Hôte ◽

F. Besson

Keyword(s):

Rural Areas ◽

Climate Adaptation ◽

End Users ◽

Lessons Learned ◽

Easy Access ◽

Climate Projections ◽

Adaptation To Climate Change ◽

Economic Sectors ◽

Major Step ◽

Climate Services

Abstract. From the French National Adaptation to Climate Change Plan, the "Drias, les futurs du climat" service has been developed to provide easy access to French regional climate projections. This is a major step for the implementation of French Climate Services. The usefulness of this service for the end-users and decision makers involved with adaptation planning at a local scale is investigated. As such, the VIADUC project is: to evaluate and enhance Drias, as well as to imagine future development in support of adaptation. Climate scientists work together with end-users and a service designer. The designer's role is to propose an innovative approach based on the interaction between scientists and citizens. The chosen end-users are three Natural Regional Parks located in the South West of France. The latter parks are administrative entities which gather municipalities having a common natural and cultural heritage. They are also rural areas in which specific economic activities take place, and therefore are concerned and involved in both protecting their environment and setting-up sustainable economic development. The first year of the project has been dedicated to investigation including the questioning of relevant representatives. Three key local economic sectors have been selected: i.e. forestry, pastoral farming and building activities. Working groups were composed of technicians, administrative and maintenance staff, policy makers and climate researchers. The sectors' needs for climate information have been assessed. The lessons learned led to actions which are presented hereinafter.

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Realised added value in dynamical downscaling of Australian climate change

10.5194/egusphere-egu21-3855 ◽

2021 ◽

Author(s):

Giovanni Di Virgilio ◽

Jason P. Evans ◽

Alejandro Di Luca ◽

Michael R. Grose ◽

Vanessa Round ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Adaptation ◽

Regional Climate ◽

Future Climate ◽

Added Value ◽

Climate Projections ◽

Eastern Australia ◽

Australian Alps ◽

Future Climate Projections

Coarse resolution global climate models (GCM) cannot resolve fine-scale drivers of regional climate, which is the scale where climate adaptation decisions are made. Regional climate models (RCMs) generate high-resolution projections by dynamically downscaling GCM outputs. However, evidence of where and when downscaling provides new information about both the current climate (added value, AV) and projected climate change signals, relative to driving data, is lacking. Seasons and locations where CORDEX-Australasia ERA-Interim and GCM-driven RCMs show AV for mean and extreme precipitation and temperature are identified. A new concept is introduced, &#8216;realised added value&#8217;, that identifies where and when RCMs simultaneously add value in the present climate and project a different climate change signal, thus suggesting plausible improvements in future climate projections by RCMs. ERA-Interim-driven RCMs add value to the simulation of summer-time mean precipitation, especially over northern and eastern Australia. GCM-driven RCMs show AV for precipitation over complex orography in south-eastern Australia during winter and widespread AV for mean and extreme minimum temperature during both seasons, especially over coastal and high-altitude areas. RCM projections of decreased winter rainfall over the Australian Alps and decreased summer rainfall over northern Australia are collocated with notable realised added value. Realised added value averaged across models, variables, seasons and statistics is evident across the majority of Australia and shows where plausible improvements in future climate projections are conferred by RCMs. This assessment of varying RCM capabilities to provide realised added value to GCM projections can be applied globally to inform climate adaptation and model development.

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What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽

10.3390/cli8010015 ◽

2020 ◽

Vol 8 (1) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Ge Peng ◽

Jessica L. Matthews ◽

Muyin Wang ◽

Russell Vose ◽

Liqiang Sun

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Adaptation ◽

Global Climate ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Climate Projections ◽

Emission Scenarios ◽

Ice Coverage ◽

Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

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A high-resolution downscaled CMIP6 projections dataset of essential surface climate variables over the globe coherent with the ERA5-Land reanalysis for climate change impact assessments

10.31223/x5s610 ◽

2021 ◽

Author(s):

Thomas Noël ◽

Harilaos Loukos ◽

Dimitri Defrance

Keyword(s):

Climate Change ◽

High Resolution ◽

Climate Models ◽

Mapping Method ◽

Cumulative Distribution ◽

Climate Projections ◽

Climate Modelling ◽

Temperature And Precipitation ◽

Surface Climate ◽

Emissions Scenarios

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

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Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea

10.5194/egusphere-egu21-13557 ◽

2021 ◽

Author(s):

Andrea Lira Loarca ◽

Giovanni Besio

Keyword(s):

Bias Correction ◽

Temporal Variability ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Wave Climate ◽

Mapping Method ◽

Climate Projections ◽

Bias Adjustment ◽

Distribution Mapping

Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability.&#160;Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.&#160; While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker & Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.&#160;References:<ul><li>Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.</li> <li>Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.</li> <li>Parker, K. & Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65</li> </ul>

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COSMO-CLM Regional Climate Simulations in the CORDEX framework: a review

10.5194/gmd-2020-443 ◽

2021 ◽

Author(s):

Silje Lund Sørland ◽

Roman Brogli ◽

Praveen Kumar Pothapakula ◽

Emmanuele Russo ◽

Jonas Van de Walle ◽

...

Keyword(s):

Climate Models ◽

Regional Climate ◽

Parameter Tuning ◽

Horizontal Resolution ◽

Global Climate Models ◽

Limited Area ◽

Climate Projections ◽

Climate Modelling ◽

Near Surface ◽

Climate Simulations

Abstract. In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Re- gional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44° (∼50 km), 0.22° (∼25 km) and 0.11° (∼12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Fed- eration (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain.

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Design of a regional climate modelling projection ensemble experiment – NARCliM

Geoscientific Model Development ◽

10.5194/gmd-7-621-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 621-629 ◽

Cited By ~ 112

Author(s):

J. P. Evans ◽

F. Ji ◽

C. Lee ◽

P. Smith ◽

D. Argüeso ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Selection Process ◽

Regional Climate ◽

Global Climate Models ◽

Regional Climate Modelling ◽

Future Research ◽

Climate Projections ◽

Climate Modelling ◽

Future Change

Abstract. Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

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Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany

Atmosphere ◽

10.3390/atmos11111245 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1245

Author(s):

Frank Kreienkamp ◽

Philip Lorenz ◽

Tobias Geiger

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Regional Scale ◽

Coupled Model ◽

Global Climate Models ◽

Climate Projections ◽

Climate Modelling ◽

Climate Signal ◽

Temperature Signal

Climate modelling output that was provided under the latest Coupled Model Intercomparison Project (CMIP6) shows significant changes in model-specific Equilibrium Climate Sensitivity (ECS) as compared to CMIP5. The newer versions of many Global Climate Models (GCMs) report higher ECS values that result in stronger global warming than previously estimated. At the same time, the multi-GCM spread of ECS is significantly larger than under CMIP5. Here, we analyse how the differences between CMIP5 and CMIP6 affect climate projections for Germany. We use the statistical-empirical downscaling method EPISODES in order to downscale GCM data for the scenario pairs RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. We use data sets of the GCMs CanESM, EC-Earth, MPI-ESM, and NorESM. The results show that the GCM-specific changes in the ECS also have an impact at the regional scale. While the temperature signal under regional climate change remains comparable for both CMIP generations in the MPI-ESM chain, the temperature signal increases by up to 3 °C for the RCP8.5/SSP5-8.5 scenario pair in the EC-Earth chain. Changes in precipitation are less pronounced and they only show notable differences at the seasonal scale. The reported changes in the climate signal will have direct consequences for society. Climate change impacts previously projected for the high-emission RCP8.5 scenario might occur equally under the new SSP2-4.5 scenario.

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Supporting users to implement uncertainty of climate change information in adaptation studies

10.5194/egusphere-egu2020-18769 ◽

2020 ◽

Author(s):

Gabriella Zsebeházi ◽

Beatrix Bán

Keyword(s):

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Projections ◽

Web Portals ◽

Climate Data ◽

Adaptation Studies ◽

Climate Services ◽

Model Simulations

There is a growing need to develop climate services both at national and international level, to bridge the gap between the providers and the end-users of climate information. Several national climate services are aiming to serve the local users&#8217; needs by creating web portals. Thanks to this trend, the number of available climate data (both measured and modelled) is rapidly growing and often there is not any personal contact between the users and the climate scientists via the web portals. Therefore, it is important to make this service usable and informative and train the potential users about the nature, strengths and limits of climate data.Within the framework of a national funded project (KlimAdat), the regional climate model projections of the Hungarian Meteorological Service are extended and a representative climate database is developed. Regular workshops are organised, where we get hands-on information about the requirements and give training about climate modelling in exchange. One of the most discussed issue during the workshops is tackling with uncertainty information of climate projections in climate change adaptation studies. The future changes are quantified in probabilistic form, applying ensemble technique, i.e. several climate model simulations prepared with different global and regional climate models and anthropogenic scenarios are evaluated simultaneously.In order to help the users orienting through the mushrooming climate projections, a user guide is prepared. Topics are e.g. how to select model simulations, how to take into account model validation results and what is the difference between signal and noise. The guideline is based on 24 simulations of the 12-km resolution Euro-CORDEX regional climate models, driven by the RCP4.5 and RCP8.5 scenarios. Two target groups are distinguished based on the required level of post-processing climate data: 1) climate impact modellers, who need large amount of raw or bias corrected data to drive their own impact model; 2) decision makers and planners, who need heavily processed but lightweight data. The purpose of our guideline is to provide insight into the customized methodologies used at the Hungarian Meteorological Service for fulfilling users&#8217; needs.

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