David Huard

Climate services seek to inform decisions about long-term climate changes. Since the 1990s, the vast majority of climate projections have covered the period up to 2100, a horizon that is not so distant anymore. This leaves decision makers tasked with planning coastal development or building long-lived infrastructure with relatively sparse information to assess climate risks, evaluate the costs and benefits of adaptation options, and implement carbon emission mitigation actions. In a bid to better understand how climate services could extend further into the future, this paper reviews the literature to understand how and why post-2100 climate studies are conducted. It identifies three broad themes: understanding climate processes with great inertia, assessing climate risks and informing adaptation actions, and evaluating emission mitigation scenarios. Multiple papers reveal the complex interplay between climate feedbacks, and show how climate change post-2100 is not merely an extension of 21st century trends. The overwhelming focus on the current century downplays the long-term damages of current emissions and fails to convey the possibility that future climate conditions could exceed adaptation capacity. We reiterate numerous calls made in the literature to extend mainstream climate emission scenarios and climate model projections farther into the future, and provide the building blocks for robust climate products. Plain Language Summary Most climate products and services available today end in 2100. This is partly due to the fact that very few climate model projections extend past that date, so there is little data to work with to build robust, credible products. Such information is highly valuable to decision makers tasked with planning coastal development, large infrastructure projects or nature conservation that all have multi-century implications. This article looks at how and why researchers study climate change after 2100. Most papers investigate processes with great inertia, such as the Antarctic and Greenland ice sheets or ocean heat storage, along with their impacts, for example on sea-level rise or the ocean biosphere. Authors are motivated by a better understanding of risks, informing climate adaptation, and evaluating scenarios for carbon emissions reduction. Although the impacts of climate change are already felt worldwide, the bulk of costs and damages will occur after 2100. By focusing modeling efforts on 21st century climate change, we might very well underestimate the challenges ahead and fail to prepare adequately.

Future projections of climate change are uncertain, in part because of uncertainty in future greenhouse gas emissions. However, current widely used emission scenarios and related physical climate projections have no probabilities associated with them, presenting a challenge for risk‐informed climate adaptation decision making. Motivated to close this gap, we demonstrate an ensemble of full‐complexity Earth System Model‐based climate projections that include explicit estimates of emissions uncertainty. This approach avoids the need to condition future Earth System Model projections on storyline‐based climate scenarios and provides information that can be probabilistically interpreted directly for arbitrary climate metrics including regional climate impact drivers. This addresses a key need of climate risk assessment for climate adaptation. Our demonstration is intended to motivate further engagement on development and delivery of probabilistic climate information, that complements information from scenario‐based climate projections.

Decision-making regarding adaptation to climate hazards frequently requires a quantitative assessment of the likelihood of future hazards. This is typically done using climate model projections conditional on future greenhouse gas (GHG) and aerosol concentrations, as well as land-use changes. This leaves decision makers with the responsibility to select the concentration pathway corresponding to their risk tolerance, with little guidance from the scientific community. Here we propose a climate service estimating the occurrence of future climate hazards based on a weighted mixture distribution calibrated on an ensemble of hundreds of simulations from different global climate models and GHG concentration pathways. Each simulation is weighted according to its performance over the historical period, the equilibrium climate sensitivity of climate models, and the likelihood of future GHG concentrations. A prototype implementation for Canada was evaluated by groups of professionals from engineering firms and governments to collect criticism and suggestions. This feedback underlines the interest for climate products that integrate seamlessly with established risk-based frameworks familiar to decision makers.

Xclim is a Python library that enables computation of climate indicators over large, heterogeneous data sets. It is built using xarray objects and operations, can seamlessly benefit from the parallelization handling provided by dask, and relies on community conventions for data formatting and metadata attributes. xclim is meant as a tool to facilitate both climate science research and the delivery of operational climate services and products. In addition to climate indicator calculations, xclim also includes utilities for bias correction and statistical adjustment, ensemble analytics, model diagnostics, data quality assurance, and metadata standards compliance.

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO₂) concentration scenarios used in key climate change modeling experiments. It computes the CO₂ emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO₂ emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO₂ concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5‐8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO₂ concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) has adopted the FAIR Guiding Principles. We present the Atlas chapter of Working Group I (WGI) as a test case. We describe the application of the FAIR principles in the Atlas, the challenges faced during its implementation, and those that remain for the future. We introduce the open source repository resulting from this process, including coding (e.g., annotated Jupyter notebooks), data provenance, and some aggregated datasets used in some figures in the Atlas chapter and its interactive companion (the Interactive Atlas), open to scrutiny by the scientific community and the general public. We describe the informal pilot review conducted on this repository to gather recommendations that led to significant improvements. Finally, a working example illustrates the re-use of the repository resources to produce customized regional information, extending the Interactive Atlas products and running the code interactively in a web browser using Jupyter notebooks.

Climate change adaptation under resource constraints and future climate uncertainties would benefit from fully probabilistic climate risks assessments. Conducting such risk analyses requires assigning probabilities to the future greenhouse gases (GHG) and land-use scenarios used by global climate models. This paper proposes an approach to estimate the relative likelihood of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. The approach relies on the comparison of CO2 emissions from probabilistic simulations of Integrated Assessment Models (IAM) with compatible CO2 emissions diagnosed by global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6). The approach is demonstrated with five emission simulations from four IAMs, leading to independent estimates of the relative likelihood of CMIP5 Representation Concentration Pathways and CMIP6’ Shared Socioeconomic Pathways (SSP) up to 2100. Results suggest that SSP5-8.5 is an unlikely scenario for the second half of the century, but there is no clear consensus on the most likely scenario. Scenario likelihood is affected by a number of potential errors, including sampling errors, differences in emission sources simulated by the IAMs, and the lack of a common experimental framework for IAM simulations. These errors, along with the small IAM ensemble size, limit the applicability of the results. The delivery of fully probabilistic climate risk assessments would benefit from a coordinated probabilistic IAM experiment jointly designed with a coordinated climate modeling experiment where Earth System Model are driven by representative emission pathways.

The Intergovernmental Panel on Climate Change (IPCC) currently prepares its Sixth Assessment Report (AR6). Its authors assess peer-reviewed scientific literature and recent climate datasets to info...

The Intergovernmental Panel on Climate Change (IPCC) currently prepares its Sixth Assessment Report (AR6). Its authors assess peer-reviewed scientific literature and recent climate datasets to inform policy-makers about the current state of the science regarding climate change and its impacts, as well as adaptation and mitigation options. For AR6, efforts are underway to make its main results FAIR and preserve them in the TRUSTworthy repositories of the IPCC Data Distribution Centre (DDC), jointly managed by CEDA, DKRZ, and CIESIN. The AR6 FAIR initiative was kickstarted by the IPCC DDC and Working Group I (WGI) [Stockhause et al., 2019], then adopted by IPCC TG-Data (Task Group on Data Support for Climate Change Assessments) shortly after its creation. All three WGs have adopted the FAIR data guidelines. IPCC assessments are large and diverse in in terms of scientists involved as well as included scientific objects. Challenges for digital data curation are related to the scale and diversity of papers, reports, datasets, the variety of software, and the different familiarity of the scientists with these technical aspects. The following priority areas for improved data stewardship were selected based on the aims to enhance the traceability of AR6 key findings and their reusability: preserve figure datasets in the DDC; - preserve analysis software; . preserve main input datasets in the DDC; . assemble datasets and provenance information on the figure creation from IPCC authors; and . interlink datasets to the IPCC report. Datasets are transferred to the DDC at the end of AR6. The DDC partners are responsible to preserve the data for future reuse by different stakeholders and under a variety of current and future scientific and policy-related questions. As the role for the DDC expands within the IPCC, new partners are sought. The TRUST principles provide a framework for the communication of DDC tasks to different stakeholders, e.g. to countries interested to host a DDC. The presentation will give an overview over the IPCC AR6 approaches towards FAIR data maintained in TRUSTworthy repositories, their challenges, their approach to meet these challenges and open questions, e.g. the integration of digital data into the IPCC Error Protocol, targeted within TG-Data.

Annual inflow forecasts are often based on historical time series, where every year is considered equally likely to reoccur. This process ignores the persistence of dry/wet conditions often observed in time series, behavior that is of utmost importance for hydroelectric energy producers. However, the modeling of persistence properties is challenging when only short time series are available for calibration. Here, we use Gaussian hidden Markov models to describe the regime‐switching behavior, where the next year's inflow depends on the current estimated regime. For four large hydropower reservoirs on the Québec‐Labrador Peninsula, a Gaussian hidden Markov model is calibrated on both a 30‐year observational record and a 190‐year paleoclimatic inflow reconstruction. Each reconstruction is a composite of three reconstruction methods drawing on five different tree‐ring proxies (ring widths, minimal wood density, maximal wood density,δ¹³C, andδ¹⁸O). The calibration on the reconstructed series finds two hydrological regimes, while the calibration on the observed data has only one regime for three out of four watersheds. Yearly hindcasts with the two calibrated Gaussian hidden Markov models suggest that for all four watersheds, extending the time series with reconstructions improves the model's predictive accuracy. This approach does not explicitly account for the differing accuracy of the observational and reconstructed time series or compare hidden Markov models to other models of persistence.