Overview of Climate Data

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Types of Climate Datasets

Climate data are grouped into three main categories:

Observational data are direct measurements from weather stations. These provide the most accurate and high-resolution climate records, but their coverage is limited to specific locations, leading to gaps in remote areas. Instrument or human failure may cause gaps in climate station records. Gridded observational datasets address the spatial limitation of station data by interpolating their data across a defined grid, providing more comprehensive spatial coverage. These datasets, offer a balance between accuracy and spatial representation, although uncertainties arise in data-sparse regions and temporal data gaps will persist in a gridded data product.

Ranalysis datasets combine historical observations with atmospheric process models to create consistent, long-term reconstructions of the atmosphere. They offer global coverage and high temporal resolution, making them valuable for analyzing past climate conditions and trends. However, reanalysis products rely on model-based data assimilation, which may introduce biases, especially in areas with limited observations.

Global climate models (GCMs) simulate atmospheric process of the planet. They are operated for known historical boundary conditions of the earth and under different future greenhouse gas scenarios. The resulting simulated climate data are essential for understanding past and future climate patterns and assessing potential changes and impacts. Uncertainties and biases of climate models are addressed by employing climate model simulation ensembles and applying bias adjustment procedures to raw model outputs.

Together, these datasets enable a comprehensive view of both historical and future climate conditions, supporting a wide range of applications in the electricity sector.

Climate Data

This section was taken from the Annex of the CSA Standard on Vulnerability Assessment for Dams

Generally, climate data can be sourced from historical observation of climate using instrumentation or from climate model simulations. In the context of electricity system planning and design, future climatic conditions should be of primary interest, but observed historical climate data and products derived thereof may also be employed or used in the development of adequate future climate information.

Observed data is provided as point information at station locations. To provide continuous fields of climate information, station data can be interpolated spatially to produce gridded historical data. They provide a portrait of climate beyond the stations point locations but are impacted by potential interpolation errors, particularly in data sparce regions, or in regions of complex topography (e.g. mountainous areas). The approach that attempts to overcome these drawbacks is the reproduction of historical climate through the combination of a maximum of available climate information with a physical climate simulation approach known as climate reanalysis. Reanalysis products have substantially improved over recent years and are often used as surrogates for observational data in climate science and studies.

Observational data and reanalysis data provide a representation of the actual evolution and sequence of events as they occurred and provide robust estimates of climate when averaged over climatic periods (usually 30 years). Note, however, that due to natural climate variability, climate estimates would shift with slight temporal shifts of the period, or the data being used.

Simulated data from climate models used for electricity system planning and design should be sourced from internationally coordinated ensembles of climate model simulations or products derived thereof. These simulations typically cover multiple decades and are available for historical and future periods. They are fully consistent in time and space and are provided on grids. The grid resolution depends on the model or the data product.

Like observational data, bias-adjusted historical climate simulations may be used to derive climate estimates of historical climate. These estimates will not be identical to climate estimates from observed data yet will generally fall into the range of natural variability of the observed climate. Since climate model simulations extend into the future, estimates for future climate may also be derived. It is important to note that the sequence of events produced by a historical climate simulation is distinct from the sequence of historically observed events, although their respective climate estimate is the robust characteristic they have in common.

Climate data

from the demand section, to be edited

Climate data is essential for the electricity demand impact assessments under climate change. These data are primarily sourced from Global Climate Models (GCMs), which simulate the Earth’s climate system based on physical laws and boundary conditions. GCMs provide long-term projections of atmospheric variables such as temperature and precipitation, which are critical inputs to electricity demand models, especially in studies focusing on temperatrue-sensitive loads like heating and cooling.

Many studies assessing future electricity demand have employed GCM outputs from the Coupled Model Intercomparison Project phases, particularly CMIP5 and CMIP6. For example, Fonseca et al. (2019) usedtemperature projections from 20 CMIP5 GCMs to simulate intra-day electricity demand shifts. Similarly, Chidiac et al. (2022) relied on GCMs from the North American CORDEX, which has 23 CMIP6 to assess building heating and cooling demand in Ontario, while Hu et al. (2024) drew on CMIP5 from the EURO-CORDEX to construct the temperature response function. Amonkar et al. (2023) used ERA5 reanalysis data to analyze changes in peak and average thermal demand across the contiguous US. To explore a range of potential future emissions trajectories, studies commonly apply Representative Concentration Pathways (RCPs). These scenarios (i.e., RCP2.6, RCP4.5, RCP6.0, and RCP8.5) represent different greenhouse gas concentration trajectories based on varying degrees of pathway scenarios. The selection of RCPs provides a structured way to assess electricity demand sensitivity under different warming levels. For instance, Hu et al. (2024) employed three major RCPs (2.6, 4.5, and 8.5) to examine how demand profiles shift across Europe. Emodi et al. (2018) and Reyna and Chester (2017) similarly applied four RCPs including RCP6.0 to assess electricity demand responses under climate change, respectively. While Trotter et al. (2016) and Auffhammer et al. (2017) applied two RCPs (4.5 and 8.5), Lipson et al. (2019) only used the worst pathway, RCP8.5, to investigate climate change impact on energy demand through building-urban-atmosphere simulations. On the other hand, Chidiac et al. (2022) evaluated changes of HDDs and CDDs for buildings’ heating and cooling demand under the intermediate scenario, RCP4.5.

Despite the advances in climate data usage, the literature reveals several challenges and limitations. First, the choice of climate models and RCPs is not always clearly justified or consistent across studies, making comparative assessment difficult. Second, biases in raw climate model outputs can distort demand projections if not corrected or validated using observations. Third, the integration of climate data with energy system models often lacks explicit treatment of uncertainty. Fonseca et al. (2019) noted that climate variables alone explain a limited portion of electricity demand variability as socioeconomic, behavioral, and technological factors may play considerable roles. Additionally, while many studies have applied a single or limited number of climate models, ensemble approaches or various combinations of GCMs and RCMs, as used by Reyna and Chester (2017), Auffhammer et al. (2017), Lipson et al. (2019), Fonseca et al. (2019), Chidiac et al. (2022), Lee and Dessler (2022), and Bonkaney et al. (2023), are therefore essential to improve the robustness of results.

References

Amonkar, Y., Doss-Gollin, J., Farnham, D.J., Modi, V., Lall, U., 2023. Differential effects of climate change on average and peak demand for heating and cooling across the contiguous USA. Communications Earth and Environment 4, 1–9. https://doi.org/10.1038/s43247-023-01048-1
Auffhammer, M., Baylis, P., Hausman, C.H., 2017. Climate change is projected to have severe impacts on the frequency and intensity of peak electricity demand across the United States. Proceedings of the National Academy of Sciences of the United States of America 114, 1886–1891. https://doi.org/10.1073/pnas.1613193114
Bonkaney, A.L., Abiodun, B.J., Sanda, I.S., Balogun, A.A., 2023. Potential impact of global warming on electricity demand in Niger. Climatic Change 176. https://doi.org/10.1007/s10584-023-03513-4
Chidiac, S.E., Yao, L., Liu, P., 2022. Climate Change Effects on Heating and Cooling Demands of Buildings in Canada. CivilEng 3, 277–295. https://doi.org/10.3390/civileng3020017
Emodi, N.V., Chaiechi, T., Alam Beg, A.B.M.R., 2018. The impact of climate change on electricity demand in Australia. Energy and Environment 29, 1263–1297. https://doi.org/10.1177/0958305X18776538
Fonseca, F.R., Jaramillo, P., Bergés, M., Severnini, E., 2019. Seasonal effects of climate change on intra-day electricity demand patterns. Climatic Change 154, 435–451. https://doi.org/10.1007/s10584-019-02413-w
Hu, W., Scholz, Y., Yeligeti, M., Deng, Y., Jochem, P., 2024. Future electricity demand for Europe: Unraveling the dynamics of the Temperature Response Function. Applied Energy 368, 123387. https://doi.org/10.1016/j.apenergy.2024.123387
Lee, J., Dessler, A.E., 2022. The Impact of Neglecting Climate Change and Variability on ERCOT’s Forecasts of Electricity Demand in Texas. Weather, Climate, and Society 14, 499–505. https://doi.org/10.1175/WCAS-D-21-0140.1
Lipson, M.J., Thatcher, M., Hart, M.A., Pitman, A., 2019. Climate change impact on energy demand in building-urban-atmosphere simulations through the 21st century. Environmental Research Letters 14. https://doi.org/10.1088/1748-9326/ab5aa5
Reyna, J.L., Chester, M.V., 2017. Energy efficiency to reduce residential electricity and natural gas use under climate change. Nature Communications 8. https://doi.org/10.1038/ncomms14916
Trotter, I.M., Bolkesjø, T.F., Féres, J.G., Hollanda, L., 2016. Climate change and electricity demand in Brazil: A stochastic approach. Energy 102, 596–604. https://doi.org/10.1016/j.energy.2016.02.120