Ensemble Climate Research Articles

A framework for visual comparison of scalar fields with uncertainty

AbstractScientists working with uncertain data, such as climate simulations, medical images, or ensembles of physical simulations, regularly confront the problem of comparing observations, e.g., to identify similarities, differences, or patterns. Current approaches in comparative visualization of uncertain scalar fields mainly rely on juxtaposition of both data and uncertainties, where each is represented using, e.g., color mapping or volume rendering. While interpretation of uncertain scalar data from visual encodings is already cognitively challenging, comparison of uncertain fields without explicit visualization support adds a further layer of complexity. In this paper, we present a theoretical framework to devise and describe a class of techniques that directly visualize differences between two or more uncertain scalar fields in a single image. We model each such technique as a combination of one or more interpolation stages, with the application of distance measures on random variables to the resulting distributions, and an appropriate visual encoding. Our framework captures existing methods and lends itself well to formulating new comparative visualization techniques for uncertain data for different visualization scenarios. Furthermore, by modeling uncertain scalar field differences as random variables themselves, we enable additional opportunities for comparison. We demonstrate the usefulness of our framework and its properties by applying it to effective comparative visualization techniques for several synthetic and real-world data sets.

Actionable human–water system modelling under uncertainty

Abstract. This paper develops an actionable interdisciplinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used decision support system for water resource planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where water management policies meet or miss their objectives and to explore potential tipping points. The application of the methods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by < 0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 % or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adaptation strategies and related uncertainties for identifying robust decisions.

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

AbstractClimate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

Proper orthogonal decomposition reduced-order model of the global oceans

AbstractA reduced-order model (ROM) of the global oceans is developed by projecting the hydrostatic Boussinesq equations of motion onto a proper orthogonal decomposition (POD) basis. Three-dimensional POD modes are calculated from the ocean fields of an ensemble climate reanalysis dataset. The coefficients in the POD ROM are calculated using a regression approach. The performance of various POD ROM configurations are assessed. Each configuration is derived from an alternate sea-water equation of state, linking the density and temperature fields. POD ROM variants incorporating an equation of state in which density is a quadratic function of temperature, are able to reproduce the statistics of the large-scale structures at a fraction of the computational cost required to numerically simulate this flow. Due to the speed and efficiency of calculation, such reduced-order models of the global geophysical system will enable researchers and policy makers to assess the physical risk for a broader range of potential future climate scenarios.

Increasing extreme precipitation variability plays a key role in future record-shattering event probability

Climate events that break records by large margins are a threat to society and ecosystems. Climate change is expected to increase the probability of such events, but quantifying these probabilities is challenging due to natural variability and limited data availability, especially for observations and very rare extremes. Here we estimate the probability of precipitation events that shatter records by a margin of at least one pre-industrial standard deviation. Using large ensemble climate simulations and extreme value theory, we determine empirical and analytical record shattering probabilities and find they are in high agreement. We show that, particularly in high emission scenarios, models project much higher record-shattering precipitation probabilities in a changing relative to a stationary climate by the end of the century for almost all the global land, with the strongest increases in vulnerable regions in the tropics. We demonstrate that increasing variability is an essential driver of near-term increases in record-shattering precipitation probability, and present a framework that quantifies the influence of combined trends in mean and variability on record-shattering behaviour in extreme precipitation. Probability estimates of record-shattering precipitation events in a warming world are crucial to inform risk assessment and adaptation policies.

Why East Asian monsoon anomalies are more robust in post El Niño than in post La Niña summers

The East Asian summer monsoon (EASM) supplies vital rainfall for over one billion people. El Niño-Southern Oscillation (ENSO) markedly affects the EASM, but its impacts are more robust following El Niño than La Niña. Here, we show that this asymmetry arises from the asymmetry in ENSO evolution: though most El Niño events last for one year, La Niña events often persist for 2-3 years. In the summers between consecutive La Niña events, the concurrent La Niña opposes the delayed effect of the preceding winter La Niña on the EASM, causing a reduction in the magnitude and coherence of climate anomalies. Results from a large ensemble climate model experiment corroborate and strengthen the observational analysis with an order of magnitude increase in sample size. The apparent asymmetry in the impacts of the ENSO on the EASM can be reduced by considering the concurrent ENSO, in addition to the ENSO state in the preceding winter. This has important implications for seasonal climate forecasts.

User-tailored sub-selection of climate model ensemble members for impact studies

Climate model ensembles serve as an input to all impact studies that use sector-specific models (e.g., hydrological, ecological, crop models, fire hazard) at regional or local scales. These models require regionally scaled climate information to simulate the potential environmental and socio-economic sectoral impacts of climate change. Such simulations are based on comprehensive multi-member climate ensembles derived from the bias-adjusted and downscaled global and regional climate models. Due to limited computational resources, users of climate scenarios often can only include a small number of the ensemble members in their calculations, and therefore they often select them at random. A pre-selection of meaningful, consistent and case-specific members is therefore desired by the climate data users. In this work, we aim to fill this gap and present a novel user-tailored procedure for sub-selecting ensemble members for a variety of applications. Our method is based on the ranking of the climate change signal (CCS) calculated for a set of climate indices (e.g., mean temperature or number of hot days). Based on the CCS strength, three ensemble members representing the strongest, weakest, and median CCS are selected for each application. We also demonstrate the robustness of our approach in a specific hydrological impact model framework. Providing a systematic procedure not only assists impact modelers in selecting appropriate members, but also improves the consistency and comparability of different impact studies.

Future climate change and marine heatwaves - Projected impact on key habitats for herring reproduction

This study explores the impact of global climate targets on sea surface temperatures and marine heatwaves (MHWs) in the Baltic Sea. We further evaluate potential adverse climate effects on the reproductive success of the western Baltic Sea (WBS) herring stock, which underwent a dramatic decline during the past two decades. For this, we use refined ensemble climate projections from the Coupled Model Intercomparison Project. For the WBS herring spawning ground, the number of MHW days nearly triples from 34 days/year in the historical period, to 102 days/year already under the optimistic 1.5 °C target of global climate warming (Paris, 2015) and further increases at a rate of 36 to 48 [days yr−1]/0.5 °C beyond the 1.5 °C target. The average MHW surface extent more than doubles in the 1.5 °C target from ~8 % to 21 % in this area.This study finds the phenological winter climate considerably altered in response to future global warming and more frequent MHW days in the WBS. The winter duration reduces by ~25 % already in the 2.0 °C target but by ~60 % in the 4.0 °C target compared to the historical climate. Winter inceptions/terminations occur successively later/earlier and the share of missed winters, i.e. winters unsuitable to support herring reproductive success, increases by up to ~70 %. Days with heat stress on the cardiac function of herring larvae will likewise increase and occur earlier in the year. Consequently, the early life cycle of herring will face more often winter conditions that were unprecedented during the historical past, and the risk for future reproductive failure will increase. However, our results reveal that abiotic disturbances for the marine ecosystem can be partly mitigated if global warming remains compliant with the 1.5 °C target.

Changing dynamics of Western European summertime cut‐off lows: A case study of the July 2021 flood event

AbstractIn July 2021, a cut‐off low‐pressure system brought extreme precipitation to Western Europe. Record daily rainfall totals led to flooding that caused loss of life and substantial damage to infrastructure. Climate change can amplify rainfall extremes via thermodynamic processes, but the role of dynamical changes is uncertain. We assess how the dynamics involved in this particular event are changing using flow analogues. Using past and present periods in reanalyses and large ensemble climate model data of the present‐day climate and 2°C warmer climate, we find that the best flow analogues become more similar to the cut‐off low‐pressure system observed over Western Europe in 2021. This may imply that extreme rain events will occur more frequently in the future. Moreover, the magnitude of the analogue lows has deepened, and the associated air masses contain more precipitable water. Simulations of future climate show similar events of the future could lead to intense rainfall further east than in the current climate, due to a shift of the pattern. Such unprecedented events can have large consequences for society, we need to mitigate and adapt to reduce future impacts.

Projected rapid response of stratospheric temperature to stringent climate mitigation

Deep, rapid and sustained reductions in greenhouse gas emissions are required to meet the 2015 Paris Agreement climate target. If the world strengthens efforts toward near-term decarbonisation and undertakes major societal transformation, this will be met with requests from policymakers and the public for evidence that our actions are working and there are demonstrable effects on the climate system. Global surface temperature exhibits large internal variability on interannual to decadal timescales, meaning a reduction in the magnitude of surface warming would not be robustly attributable to climate mitigation for some time. In contrast, global stratospheric temperature trends have much higher signal-to-noise ratios and could offer an early indication of the effects of climate mitigation. Here we examine projected near-term global temperature trends at the surface and in the stratosphere using large ensemble climate models following three future emission scenarios. Under rapid, deep emission cuts following SSP1–1.9, modelled middle and upper stratospheric cooling trends show a detectable weakening within 5 years compared to a scenario approximately representing current climate commitments (SSP2–4.5). Therefore, stratospheric temperature trends could serve as an early indicator to policymakers and the public that climate mitigation is taking effect.

Drivers of future extratropical sea surface temperature variability changes in the North Pacific

Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability (and correspondingly marine heatwave intensity) on monthly timescales and longer is spatially heterogeneous. We examined these projected changes (between 1960–2000 and 2060–2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change.

Future Changes of Extreme Precipitation and Related Atmospheric Conditions in East Asia under Global Warming Projected in Large Ensemble Climate Prediction Data

Abstract Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes of extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the TRMM and ERA5 datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relatively humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future, because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.

Fill‐spill‐merge terrain analysis reveals topographical controls on Canadian river runoff

AbstractDigital Elevation Models (DEMs) are a crucial tool for watershed analysis, offering valuable insights into landscape‐scale hydrology. Traditional watershed delineations are derived by filling a DEM to force flow paths through topographic depressions, thus creating a continuous drainage network throughout the domain. However, this approach is challenged in landscapes with abundant real‐world depression storage, intermittently flowing stream channels, and internally drained lake basin (endorheic basin) such as the Canadian Shield (CS). The CS landscape is characterized by “fill‐and‐spill” surface water hydrology, with runoff flow paths controlled by bedrock sills and rocky cascades that overtop when water levels are high but cease flowing when water levels are low. To better represent these intermittent drainage networks, we apply a non‐traditional, less‐aggressive DEM filling model (Fill‐Spill‐Merge or FSM) to a continental‐scale DEM (MERIT) all of Canada. To ensure adequate filling of DEM noise while also preserving real‐world topographic depressions, we propose a climatic method to initialize a key FSM parameter (“runoff depth”) that calibrates observed discharges from 1690 Environment and Climate Change Canada (ECCC) river gauges with climate model P‐ET (precipitation minus evapotranspiration) data. Our application of FSM to all 1690 gauged watersheds identifies 916 significant topographical control points controlling >20% and/or 1000 km2 of their respective areas. The Geikie, Snare, Kazan, Tazin, and Seal rivers may be particularly affected, with impacted watershed areas ranging from 12% to 64%. Extending this approach to ungauged parts of the CS reveals an additional 635 significant topographical control points. Ensemble climate model projections suggest that around 10% of these control points are currently dry but will become active by 2100. This research explicitly determines how CS watersheds are affected by fill‐and‐spill hydrology, and demonstrates the importance of accurate terrain modelling for delineating surface water flow paths in depressional landscapes.

A new scenario free procedure to determine flood peak changes in the Harz Mountains in response to climate change projections

Study areaSix catchments of different hydrological characteristics in the Harz Mountains, Germany. Study focusA new scenario free method for determining changes of flood peaks considering climate change is developed. Compared to existing methods, it accounts for heavy rainfall changes by a newly developed factor with two seasons and establishes a numerical relationship to a greater number of relevant climate predictors. For easier application, it is based on frequently available daily measurements. A functional test of the factor for heavy rainfall changes and its adjustment algorithm for the precipitation time series is performed. Using a regional AR5 ensemble, for the first time the error and the uncertainty of a new scenario free method are estimated and compared with an existing method. The new method is applied in the Harz Mountains, where the sensitivity of the region to different climate predictors is investigated. New hydrological insights for the regionThe adjusting algorithm for precipitation is able to adjust the time series while maintaining mass balance. The new method has a lower error than the reference method, with better matches of changes in the median of the climate ensemble as well as the most ensemble members. In general, the uncertainty of the seasonal results is below the climate uncertainty of the AR5 ensemble. Regarding future flood peaks, the regional catchments are most sensitive to mean precipitation changes, followed by heavy rainfall changes especially in the winter season. Mean temperature changes are of minor significance, but the catchments characteristics are important. The new method can be recommended for assessments of climate change impacts on floods in low to average mountain regions in Germany and Europe.

Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts

Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM10) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-model ensemble (MME). We identified potential climate predictors that potentially affect the wintertime air quality variability in South Korea in the global domain. From these potential climate predictors, those that can be forecasted skillfully by APCC MME were utilized to establish a multiple-linear regression model to predict the winter PM10 concentration in South Korea. As a result of evaluating the forecast skill through retrospective forecasts for the past 25 winters (1995/96-2019/20), this model showed statistically significant forecast skill at a lead time of a month to a season. The skill of PM10 forecast from the MME was overall better than that from a single model. We also found that it is possible to improve forecast skills through optimal MME combinations.

Future Changes in Regional Tropical Cyclone Wind, Precipitation, and Flooding Using Event‐Based Downscaling

AbstractUnderstanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

Delivering an Improved Framework for the New Generation of CMIP6-Driven EURO-CORDEX Regional Climate Simulations

Abstract The Coordinated Regional Downscaling Experiment (CORDEX) is a coordinated international activity that has produced ensembles of regional climate simulations with domains that cover all land areas of the world. These ensembles are used by a wide range of practitioners that include the scientific community, policymakers, and stakeholders from the public and private sectors. They also provide the scientific basis for the Intergovernmental Panel on Climate Change-Assessment Reports. As its next phase now launches, the CMIP6-CORDEX datasets are expected to populate community repositories over the next couple of years, with updated state-of-the-art regional climate data that will further support national and regional communities and inform their climate adaptation and mitigation strategies. The protocol presented here focuses on the European domain (EURO-CORDEX). It takes the international CORDEX protocol covering all 14 global domains as its template. However, it expands on the international protocol in specific areas; incorporates historical and projected aerosol trends into the regional models in a consistent way with CMIP6 global climate models, to allow for a better comparison of global versus regional trends; produces more climate variables to better support sectorial climate impact assessments; and takes into account the recent scientific developments addressed in the CORDEX Flagship Pilot Studies, enabling a better assessment of processes and phenomena relevant to regional climate (e.g., land-use change, aerosol, convection, and urban environment). Here, we summarize the scientific analysis which led to the new simulation protocol and highlight the improvements we expect in the new generation regional climate ensemble.

Internal Versus Forced Variability Metrics for General Circulation Models Using Information Theory

AbstractOcean model simulations show variability due to intrinsic chaos and external forcing (air‐sea fluxes, river input, etc.). It is important to estimate their contributions to total variability for attribution. Using variance to estimate variability might be unreliable due to non‐Gaussian higher statistical moments. We show the use of non‐parametric information theory metrics, Shannon entropy and mutual information, for measuring internal and forced variability in ocean models. These metrics are applied to spatially and temporally averaged data. The metrics delineate relative intrinsic to total variability in a wider range of circumstances than previous approaches based on variance ratios. The metrics are applied to (a) a synthetic ensemble of random vectors, (b) ocean component of a global climate (GFDL‐ESM2M) large ensemble, (c) ensemble of a realistic coastal ocean model. The information theory metric qualitatively agrees with the variance‐based metric and possibly identifies regions of nonlinear correlations. In application (2)–the climate ensemble–the information theory metric detects higher temperature intrinsic variability in the Arctic region compared to the variance metric illustrating that the former is robust in a skewed probability distribution (Arctic sea surface temperature) resulting from sharply nonlinear behavior (freezing point). In application (3)–coastal ensemble–variability is dominated by external forcing. Using different selective forcing ensembles, we quantify the sensitivity of the coastal model to different types of external forcing: variations in the river runoff and changes in wind product do not add information (i.e., variability) during summer. Information theory enables ranking how much each forcing type contributes across multiple variables.

Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California

Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.

A revised interpretation of signal-to-noise ratio paradox and its application to constrain regional climate projections

The signal-to-noise ratio paradox is interpreted as the climate model’s ability to predict observations better than the model itself. This view is counterintuitive, given that climate models are simplified numerical representations of complex earth system dynamics. A revised interpretation is provided here: the signal-to-noise ratio paradox represents excessive noise in climate predictions and projections. Noise is potentially reducible, providing a scientific basis for improving the signal in regional climate projections. The signal-to-noise ratio paradox was assessed in long-term climate projections using single-model and multi-model large ensemble climate data. A null hypothesis was constructed by performing bootstrap resampling of climate model ensembles to test its ability to predict the 20th-century temperature and precipitation trends locally and compare it with the observations. The rejection of the null hypothesis indicates the existence of a paradox. The multi-model large ensemble does not reject the null hypothesis in most places globally. The rejection rate in the single-model large ensemble is related to the model’s fidelity to simulate internal climate variability rather than its ensemble size. For regions where the null hypothesis is rejected in the multi-model large ensemble, for example, India, the paradox is caused by a smaller signal strength in the climate model’s ensemble. The signal strength was improved by 100% through ensemble selection and based on past performance, which reduced uncertainty in India’s 30-year temperature projections by 25%. Consistent with previous studies, precipitation projections are noisier, leading to a paradox metric value 2–3 times higher than that of the temperature projections. The application of ensemble selection methodology significantly decreased uncertainty in precipitation projections for the United Kingdom, Western Australia, and Northeastern America by 47%, 36%, and 20%, respectively. Overall, this study makes a unique contribution by reducing uncertainty at the temporal scale, specifically in estimating trends using the signal-to-noise ratio paradox metric.