Gaps

Overview

This page identifies gaps in climate information, datasets, and methods relevant for the use of climate data in the electricity sector.

Gaps associated with climate data and climate information

1. Mismatch between decision scale and climate data scale

The most obvious gap.

Insufficient temporal resolution for operational decisions

Utilities often need hourly or sub-hourly information, but climate datasets remain monthly or daily.

Insufficient spatial resolution for operational decisions

Energy applications need local information, models provide the average conditions over a large area.

2. Sparse, discontinuous, or non-representative observational records

Common problems include short records, changes in instruments or siting, missing data, weak coverage in mountains, coasts, northern service territories, forested corridors, and near reservoirs or hydro basins, plus poor representation of variables utilities need directly.

3. Design-relevant extremes are often inadequately characterized

Utilities do not just need mean temperature or annual precipitation. They need the tails, but many datasets resolve these poorly, especially when events are short-lived or localized.

4. Lack of information on compound and correlated hazards

Failures are frequently caused by combinations of hazards, but scientific literature (and data) of compound events is relatively limited.

5. Future climate uncertainty is difficult to translate into operational decisions

Climate information is often delivered in forms that are scientifically valid but operationally awkward: emissions scenarios, ensemble spreads, percentile changes, or annual mean anomalies. Also, no probability assigned to future CMIP climate scenario.

6. Data uncertainty is often difficult to translate into operational decisions

Uncertainty of datasets like reanalysis is difficult to evaluate, let alone use.

7. Hydrology and land-surface process gaps for water-dependent utilities

Very few products provide information on hydrology and land-surface processes in Canada.

9. Limited subseasonal-to-decadal (S2D) actionable information

Many utility actions sit between weather and climate timescales, leaving a gap between short-range meteorology and long-range climate planning

Gaps associated with the organizations and utilities themselves

1. Limited internal climate literacy

A common organizational gap is not absence of data, but lack of internal capacity to interpret what different datasets are for and how to use them, interpret ensembles, downscaling methods, scenario structure, bias correction, etc.

2. Siloes between departments that need the same information

Different teams often use different datasets, assumptions, and hazard definitions. This creates conflicting baselines and duplicated work.

3. Internal data management/governance is often very limited

Utilities often lack version control, dataset approval processes, reproducible workflows, and clear ownership of climate assumptions. They will tend to use the dataset that their colleague is using because it is available, without making sure that it is fit for purpose.

4. Consultant dependence without informed oversight

Utilities outsource climate analysis but lack the internal expertise to specify requirements, evaluate methodological quality, or challenge inappropriate products.

5. Existing standards are based on historical values

Different practices (e.g. design codes, return-period assumptions) are often built around historical climatology and regulatory conventions. Even when people recognize climate change risk, the approved methods and standards may not allow them to incorporate it.

6. Inadequate handling of uncertainty

Utilities typically respond to (certain type of) uncertainty by ignoring it.

7. Climate information exists but is not embedded in core models and workflows

Information exists, but it is not operationalized (e.g. information sits in reports).

8. Organization may privilege historical experience over forward-looking evidence

Operations and engineering teams may trust observed past events more than modeled futures.

9. Business cases for climate information are hard to quantify

Some utilities struggle to demonstrate the economic value of climate information or climate data internally.