Yes, we provide dowscaled data from the CMIP5 and CMIP6 historical and scenario runs forced by different emissions scenarios.
CMIP6 is the latest generation of climate models.

For CMIP6 we include SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP4-8.5. For CMIP5, RCP2.6 , RCP4.5 and RCP8.5.

The spatial resolution of our downscaled data is either 25 km or 3km, depending on the historical data employed as a reference to downscale the climate models.

The typical spatial resolution of Global Climate models varies between 100 and 200 km. In order to increase the resolution and at the same time correct biases in model projections, a statistical approach based on the quantile mapping technique is employed by Climate Scale. In this way model simulations are downscaled to 25km2 resolution using ERA5 as a representation of the observed climate for most of the essential climate variables, ensuring a globally consistent dataset for multiple variables simultaneously. For wind speed and solar irradiation, the resolution is increased to 3 km using Vortex and GSA data.

Climate Scale provides more than 20 climate variables or drivers of impacts linked to both chronic and acute hazards, that adhere to the TCFD recommendations and the EU Taxonomy guidelines. Temperature, water, wind, and solid mass related, together with technology-specific hazards are provided.
Contact us for a detailed description of our metrics.

We offer statistics for extreme events such extreme heat and cold, wildfire conditions, extreme precipitation, droughts, storminess, and tropical cyclones. Additionally, information about low/high anomalies for a variety of climate variables, such as wind drought periods, can be calculated from our time SERIES.

The REPORT includes information for the baseline period (2000-2019), and three projection periods: short (2020-2039), medium (2040-2059), and long (2080-2099) terms.
The daily time SERIES span from 2000 to 2099.

It depends on the availability of daily model data for each scenario and climate variable. It typically varies from 20 to 30 models for the essential climate variables.

We provide confidence levels for the projected changes in climate drivers and risk levels following the IPCC recommendations. The confidence level is high when at least 80% of the climate models project the same direction of change as the ensemble median, medium when between 60 and 80% of the models do so, and low otherwise.

By default we apply an outlier filter to remove climate models that are identified as outliers for certain climate models, variables and localizations.
We do not weight climate models according to their performance as a default option.
However, we have developed a methodology based on peer-reviewed approaches, that can be used to determine models’ weights dependent on model performance. These weights can be taken into account when calculating model ensemble statistics (ensemble medians, likely ranges, etc).
Contact us to find out more.

The scientific community has thoroughly evaluated the CMIP5 and CMIP6 climate models from a variety of angles and uses.
It should be noted that the historical and future time series for the CMIP models are not synchronized with recorded weather and climate variables as they are not driven by observations.
We have evaluated the wind speed and other variables for our downscaled CMIP data. For documentation and the most recent climate quality assessments, see our resource site.

Since we consider vulnerability information to be particularly sensitive to extremely localized conditions, and, confidential confidential input, it is not included in our hazard risk metrics by default.
However, we can add various layers of protection if required. Get in touch to discuss customizing our REPORTS to include vulnerability information.

We do not include financial information when calculating our climate-related physical hazards since, in most cases, doing so would necessitate making informed assumptions about the particulars of an asset/project.
We can, however, support you in the interpretation of physical risks and their conversion to financial impacts.

We cover the physical risks required by several sustainability frameworks at both, project and portfolio levels. These include the chronic and acute physical risks recommended by the TCFD, and the main climate hazards included in the EU Taxonomy, EU CSRD & ESRS.

Yes, we have API that can be used for third party integration and access of our data products. Get in touch with us to get the documentation and discuss technical details.

We provide all the information required to create  reports for your applications that address the climate physical hazard risks associated  to your application and are in line with guidelines and industry best practices.
For example, our product REPORT includes information on current values and projected changes for several climate drivers of impacts and associated physical risks.
We also offer assistance to users who want to incorporate our data into their analysis and reports.

Our data has been applied in different contexts including due diligence report for wind farms, companies responses to TCFD and EU Taxonomy reporting requirements, climate risk assessments for various wind and solar projects throughout the world, and more.
We collaborate with asset managers, developers, consultants, and corporate sustainability departments, and we continuously improve and customize our climate data solutions to better meet the demands of users with diverse requirements.

Our tool is available upon request. Please contact us to gain access, and one of our team members will promptly reach out to you. They will provide an introduction to our products and services and help set up a free trial tailored to your needs.

Yes, we provide free trials for businesses and organizations interested in exploring our data and services. In fact, we advise you to test the tool and climate products as a first step. Just get in touch with us, and we'll arrange a trial. All we ask in return is for your feedback on our data and services.

Access to our data is available on a pay-as-you-go basis, in packages, or by subscription.  Naturally, the discount increases the more data you request. Get in touch with us to determine which solutions are most suitable for your needs

Yes, all members of your organization will get full access for the same price. Each user can each have its own account and, if appropriate, access to all the products that the other team members have requested.

Your name, email address, and company. We advise you to use a business email so you may download all the sample data at once. We follow EU legislation regarding data privacy.

Requesting a comprehensive REPORT for your site or a 100 years daily time SERIES for a project only takes a few clicks.

We employ a modeling-on-demand methodology, so when a user requests a product, a number of modeling processes are launched in our computer cluster, starting with the downscaling of the crucial climate variables and ending with the postprocessing of all the metrics required to generate the outputs. Depending on the product, this can take anywhere from 8 to 48 hours.

The tool is designed to be user-friendly and easy to use. Contact us to schedule a meeting and discuss the features, products, and applications of the data we provide.

Currently, we do not provide transitional risk assessments as they are outside our scope. However, if you offer transitional risk services and wish to integrate our data into your dashboard or application, you can access our API for seamless integration across platforms. Please contact us for more details.

Yes, sure. We offer to co-design specific metric and analysis with the users as part of our advisory service in order to tailor our data to their requirements.
For example, we can create and compute climate impacts metrics that best represent to your project, asset, or technology physical risks expousure.
Another example, we can assist with the integration of our data into your apps and design a custom dashboard to visualize the physical risks associated with climate change.

Climate models are numerical models that simulate the different components of the climate system (atmosphere, oceans, land and ice-covered regions of the planet) and their interactions. See for instance: https://www.carbonbrief.org/qa-how-do-climate-models-work/, and IPCC AR6 [sections 1.5.3 and 1.6]

The main inputs are the amount of the sun’s energy that is absorbed by the Earth, and how much is trapped by the atmosphere. This depends on the concentration of greenhouse gases (CO2, methane, etc), and aerosols (emitted when burning fossil fuels, forest fires and volcanic eruptions). These external factors are called forcings.

Climate models can differ substantially in their degree of complexity, i.e., how realistically they can simulate the earth system. This covers not only which parts of the climate system are represented in a model, but also the degree to which the different components are coupled. On the low end of the spectrum we typically find relatively simple atmosphere-ocean GCMs (general circulation models). These models can simulate atmosphere-ocean processes and interactions. Often the ocean component takes sea ice into account. On the high end, so-called earth system models can reach a degree of complexity where atmospheric and ocean processes are coupled to sophisticated land surface schemes that include land use,  glaciers, ice caps, interactive vegetation, soil models  and more. The coupling of these components allows for interactive feedback between the different parts of the climate system.
It is important to note that models not only differ  in the number of components but also in the complexity of each component, for example, how complex is the representation of cloud processes in the atmosphere component.

Even though the physical and chemical processes in the climate system follow known scientific laws, the complexity of the system implies that many simplifications and approximations have to be made when modelling the system. The choice of approximations creates a variety of physical climate models [IPCC 2021].

There are different sources of uncertainties in climate model projections. Climate forcing or scenario uncertainty is introduced by the fact that to simulate future climate, the models are run using different scenarios of anthropogenic forcings that represent plausible but inherently unknowable future socio-economic development. Climate model and climate variability uncertainties are due to our incomplete knowledge of the climate system, the limitations of computer models to simulate it, and the system’s nonlinearity. The relative and absolute importance of these different sources of uncertainty depends on the spatial scale, the lead-time of the projection and the variable of interest . At shorter time scales, in many cases, the current natural variability of the climate system and other non-climatic drivers of risks will have a higher impact than the climatic changes driven by changes in atmospheric concentrations of greenhouse gases.

The selection of models happens on various levels. At the most fundamental, the decision to include a model is simply made by the availability of data for a specific variable. Aspects like time frequency, spatial resolution, available scenarios, play a role here. Second, model output is also checked for technical issues, such as, physically impossible values or large transients between historical and future scenarios.
After this first screening, models could be evaluated with respect to their ability to match key aspects of observed climate. IPCC AR6 states that ‘no universal, robust method for weighting a multi-model projection ensemble is available, and expert judgement must be included in the assessment of the projections’
There are therefore many approaches to this task and no general recipe exists. On a basic level for instance, model biases and temporal variability of the variable in question (e.g., surface wind) can be evaluated against observations. Whether model quality depends on the location can be assessed by examining maps of the evaluation results.
For process based analysis other relevant variables can be included in the evaluation. If necessary, this can be extended to an evaluation of spatial patterns or indices of large-scale drivers of the process of interest. These drivers of variability can be different depending on the location of interest; while NAO can influence the climate in Europe, it is less relevant for North America for instance. It is then definitely necessary to adapt the evaluation to the regional conditions.
How the results of such an evaluation are further used for selecting models depends very much on the specific case.
Weighting individual models by their performance and independence (results of the evaluation) can be an alternative to a binary model selection. In the most recent IPCC report, for instance, projections of global surface air temperature are based on weighting models according to their ability to match past warming, their equilibrium climate sensitivity and transient climate response.

According to this commentary by Hausfather and Peters, 2020,⁶, IEA modelling projections suggest that following current energy-policies the global emissions will follow a pathway that overshoots the SSP2-4.5 scenario in the next decade or so, but remains below the SSP3-7.0 scenario. However, if stated pledges were implemented, the global emissions would remain under the SSP2-4.5 scenario. According to these authors then, more plausible scenarios in terms of emissions pathways are SSP2-4.5, SSP4-6.0 and SSP3-7.0, while SSP5-8.5/RCP8.5 should be labelled as an unlikely worst case.

There is no universal agreement in this matter, though. Schwalm et al , 2020⁷, argue that RCP8.5 will continue to serve as a useful tool for quantifying physical climate risk, especially over near to mid term time horizons. This is because not only historical cumulative emissions are consistent with RCP8.5, but RCP8.5 is also a good match for projected cumulative emissions out to midcentury under current and stated policies. However these authors use different assumptions about land use emissions than those employed in the IEA scenarios.