Climate Projection Datasets

Definition

Climate projection datasets are collections of climate model outputs that simulate future climate conditions based on various greenhouse gas emission scenarios. These datasets are used to assess potential impacts of climate change on various sectors, including agriculture, water resources, and ecosystems.

Climate projection datasets

Bias correction / downscaling / data pre processing

Key Strengths of bias-adjusted datasets

Strength	Description
High spatial resolution	Resolves effects of regional topography and local climate patterns better than GCMs.
Regional relevance	Provides climate projections tailored to regional or provincial domains.
Supports impact studies	Designed to be used for vulnerability, impact, and adaptation (VIA) studies.
Adjustement of biases	Most of the GCM’s biases have been adjusted.

Key Limitations of bias-adjusted datasets

Limitation	Description
Reference bias	Biases present in the reference dataset will also appear in bias-adjusted datasets.
Climate change signal	Climate change signal comes from the GCM, even if its climate sensitivity is too high. These models can be excluded. Is this a limitation?
Biases remain	Bias-adjustment cannot completely remove all biases. Different methods will have different foci.
No-physics at the small scale	The fine resolution information only come from the reference and not a physical simulation of the future.
Bias stationnarity	Bias-adjustment assumes that the bias are constant over time.

(from Demand)

Downscaling methods

Given that GCMs operate at relatively coarse spatial resolutions (typically 100-250 km), their outputs are often inadequate for regional scale modeling. Because electricity planning is typically sensitive to finer-scale temperature variations, it is essential that climate inputs local meteorological conditions with sufficient detail. To address this, two primary categories of downscaling techniques are widely applied in the literature: statistical downscaling and dynamical downscaling.

Statistical downscaling establishes empirical relationships between large-scale GCM variables and local climate observations, using historical data as a training base. These methods are computationally efficient and have been widely used where computational resources or high-resolution models are limited. For example, Berardi and Jafarpur (2020) employed CCWorldWeatherGen and WeatherShift™ tools to statistically downscale GCMs and generated site-specific future weather data in Canada. Similarly, Fonseca et al. (2019) and Auffhammer et al. (2017) applied the Multivariate Adaptive Constructed Analogs (MACA) method to statistically downscale CMIP5 GCM projections for United States. The resulting high-resolution weather data were used to simulate changes in electricity demand profiles under a warming climate. Trotter et al. (2016) also used trend-preserving statistical downscaling method to estimate climate-induced changes across Brazil.

In contrast, dynamical downscaling embeds higher-resolution Regional Climate Models (RCMs) within GCMs to explicitly simulate regional climate processes. This approach is particularly beneficial for capturing terrain-driven weather dynamics and localized extremes. Within the North American COPRDEX (NA-CORDEX), Chidiac et al. (2022) applied multiple RCMs including CRCM5, RCA4, RegCM4, WRF, CanRCM4, and HIRHAM5 driven by GCMs like HadGEM2-ES, CanESM2, MPI-ESM-LR, MPI-ESM-MR. EC-EARTH, and GFDL-ESM2M, to project changes in building-level heating and cooling demands in Ontario. Similarly, Hu et al. (2024) used the ERMO2009 RCM under the EURO-CORDEX project to model electricity demand changes across European countries, providing spatially disaggregated insights into future temperature-response dynamics. These dynamical approaches offer more physically consistent outputs than statistical methods while a higher computational cost is required.

Nevertheless, dynamical downscaling is not without limitations. Bias correction is typically necessary because RCM outputs may deviate from observed climatology. Moreover, the selection of GCM-RCM combinations can introduce additional variability into the projections, as a concern emphasized by Chilkoti et al. (2017). To address this, ensemble approaches using multiple model combinations have become more common to capture the spread of plausible futures and quantify projection uncertainty, although this adds additional computational costs.

References

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

Berardi, U., Jafarpur, P., 2020. Assessing the impact of climate change on building heating and cooling energy demand in Canada. Renewable and Sustainable Energy Reviews 121, 109681. https://doi.org/10.1016/j.rser.2019.109681

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

Chilkoti, V., Bolisetti, T., Balachandar, R., 2017. Climate change impact assessment on hydropower generation using multi-model climate ensemble. Renewable Energy 109, 510–517. https://doi.org/10.1016/j.renene.2017.02.041

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

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