A regime perspective on jet and blocking dynamics in CMIP6

2021 ◽  
Author(s):  
Joshua Dorrington
Keyword(s):  
Potential Vorticity ◽  
Climate Models ◽  
Rossby Waves ◽  
Climate Model ◽  
Atmospheric Dynamics ◽  
Interdecadal Variability ◽  
Model Ensemble ◽  
Atlantic Region ◽  
Non Linear ◽  
Jet Structure

<p>Weather over the Euro-Atlantic region during winter is highly variable, with rich and chaotic internal atmospheric dynamics. In particular, the non-linear breaking of Rossby waves irreversibly mixes potential vorticity contours and so triggers shifts in the latitude of the eddy driven jet and establishes persistent anticyclonic blocking events. The concept of atmospheric regimes captures the tendency for blocks – and for the jet – to persist in a small number of preferred locations. Regimes then provide a non-linear basis through which model deficiencies, interdecadal variability and forced trends in the Euro-Atlantic circulation can be studied.</p><p>A drawback of past regime approaches is that they were unable to easily capture both the dynamics of the jet and of blocking anticyclones simultaneously. In this work we apply a recently developed regime framework, which is able to capture both these important aspects while reducing sampling variability, to the CMIP6 climate model ensemble. We analyse both the historical variability and biases of blocking and jet structure in this latest generation of climate models, and make new estimates of the anthropogenic forced trend over the coming century.</p><p> </p>

scholarly journals Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Joseph Leedale ◽  
Adrian M. Tompkins ◽  
Cyril Caminade ◽  
Anne E. Jones ◽  
Grigory Nikulin ◽  
...  
Keyword(s):  
Climate Change ◽  
Malaria Transmission ◽  
Climate Models ◽  
Climate Model ◽  
Eastern Africa ◽  
Future Climate Change ◽  
Climate Projections ◽  
Model Ensemble ◽  
East African Community ◽  
Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

Spatiotemporal Changes in Headwater Flow Contributions to Major Rivers of Germany under Changing Climate

2020 ◽  
Author(s):  
Akash Koppa ◽  
Thomas Remke ◽  
Stephan Thober ◽  
Oldrich Rakovec ◽  
Sebastian Müller ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Soil Moisture ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Model Ensemble ◽  
Hydrologic Models ◽  
River Temperature

<p>Headwater systems are a major source of water, sediments, and nutrients (including nitrogen and carbon di-oxide) for downstream aquatic, riparian, and inland ecosystems. As precipitation changes are expected to exhibit considerable spatial variability in the future, we hypothesize that headwater contribution to major rivers will also change significantly. Quantifying these changes is essential for developing effective adaptation and mitigation strategies against climate change. However, the lack of hydrologic projections at high resolutions over large domains have hindered attempts to quantify climate change impacts on headwater systems.</p><p>Here, we overcome this challenge by developing an ensemble of hydrologic projections at an unprecedented resolution (1km) for Germany. These high resolution projections are developed within the framework of the Helmholtz Climate Initiative (https://www.helmholtz.de/en/current-topics/the-initiative/climate-research/). Our modeling chain consists of the following four components:</p><p><strong>Climate Modeling:</strong> We statistically downscale and bias-adjust climate change scenarios from three representative concentration pathway (RCP) scenarios derived from the EURO-CORDEX ensemble - 2.6, 4.5, and 8.5 to a horizontal resolution of 1km over Germany (i.e, a total of 75 ensemble members). The EURO-CORDEX ensemble is generated by dynamically downscaling CMIP-5 general circulation models (GCM) using regional climate models (RCMs). <strong>Hydrologic Modeling:</strong> To account for model structure uncertainty, the climate model projections are used as forcings for three spatially distributed hydrologic models - a) the mesocale Hydrologic model (mHM), b) Noah-MP, and c) HTESSEL. The outputs that will be generated in the study are soil moisture, evapotranspiration, snow water equivalent, and runoff. <strong>Streamflow Routing:</strong> To minimize uncertainty from river routing schemes, we use the multiscale routing model (mRM v1.0) to route runoff from all the three models. <strong>River Temperature Modeling:</strong> A novel river temperature model is used to quantify the changes in river temperature due to anthropogenic warming.</p><p>The 225-member ensemble streamflow outputs (75 climate model members and 3 hydrologic models) are used to quantify the changes in the contribution of headwater watersheds to all the major rivers in Germany. Finally, we analyze changes in soil moisture, snow melt, and river temperature and their implications for headwater contributions. Previously, a high-resolution (5km) multi-model ensemble for climate change projections has been created within the EDgE project<strong><sup>1,2,3,4</sup></strong>. The newly created projections in this project will be compared against those created in the EDgE project.  The ensemble used in this project will profit from the higher resolution of the regional climate models that provide a more detailed land orography.</p><p><strong>References</strong></p><p><strong>[1] </strong>Marx,<em> A. et al. (2018). Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 C. Hydrology and Earth System Sciences, 22(2), 1017-1032.</em></p><p><strong>[2]</strong><em> Samaniego, L. et al. (2019). Hydrological forecasts and projections for improved decision-making in the water sector in Europe. Bulletin of the American Meteorological Society.</em></p><p><strong>[3]</strong> Samaniego,<em> L. and Thober, S., et al. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421.</em></p><p><strong>[4]</strong> Thober,<em> S. et al. (2018). Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003.</em></p><p> </p><p> </p><p> </p>

scholarly journals Indian Ocean Dipolelike Variability in the CSIRO Mark 3 Coupled Climate Model

2005 ◽  
Vol 18 (10) ◽  
pp. 1449-1468 ◽  
Author(s):  
Wenju Cai ◽  
Harry H. Hendon ◽  
Gary Meyers
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
Climate Models ◽  
Rossby Waves ◽  
Climate Model ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface

Abstract Coupled ocean–atmosphere variability in the tropical Indian Ocean is explored with a multicentury integration of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 3 climate model, which runs without flux adjustment. Despite the presence of some common deficiencies in this type of coupled model, zonal dipolelike variability is produced. During July through November, the dominant mode of variability of sea surface temperature resembles the observed zonal dipole and has out-of-phase rainfall variations across the Indian Ocean basin, which are as large as those associated with the model El Niño–Southern Oscillation (ENSO). In the positive dipole phase, cold SST anomaly and suppressed rainfall south of the equator on the Sumatra–Java coast drives an anticyclonic circulation anomaly that is consistent with the steady response (Gill model) to a heat sink displaced south of the equator. The northwest–southeast tilting Sumatra–Java coast results in cold sea surface temperature (SST) centered south of the equator, which forces anticylonic winds that are southeasterly along the coast, which thus produces local upwelling, cool SSTs, and promotes more anticylonic winds; on the equator, the easterlies raise the thermocline to the east via upwelling Kelvin waves and deepen the off-equatorial thermocline to the west via off-equatorial downwelling Rossby waves. The model dipole mode exhibits little contemporaneous relationship with the model ENSO; however, this does not imply that it is independent of ENSO. The model dipole often (but not always) develops in the year following El Niño. It is triggered by an unrealistic transmission of the model’s ENSO discharge phase through the Indonesian passages. In the model, the ENSO discharge Rossby waves arrive at the Sumatra–Java coast some 6 to 9 months after an El Niño peaks, causing the majority of model dipole events to peak in the year after an ENSO warm event. In the observed ENSO discharge, Rossby waves arrive at the Australian northwest coast. Thus the model Indian Ocean dipolelike variability is triggered by an unrealistic mechanism. The result highlights the importance of properly representing the transmission of Pacific Rossby waves and Indonesian throughflow in the complex topography of the Indonesian region in coupled climate models.

scholarly journals Potential vorticity dynamics of life cycles of blocked weather regimes in the Euro-Atlantic region: A diagnostic framework

2021 ◽  
Author(s):  
Seraphine Hauser ◽  
Christian M. Grams ◽  
Michael Riemer ◽  
Peter Knippertz ◽  
Franziska Teubler
Keyword(s):  
Potential Vorticity ◽  
Large Scale ◽  
Climate Models ◽  
Numerical Models ◽  
Weather Prediction ◽  
Life Cycles ◽  
Forecast Errors ◽  
Atlantic Region ◽  
Weather Regimes ◽  
Medium Range

<p>Quasi-stationary, persistent, and recurrent states of the large-scale extratropical circulation, so-called weather regimes, characterize the atmospheric variability on sub-seasonal timescales of several days to a few weeks. Weather regimes featuring a blocking anticyclone are of particular interest due to their long lifetime and potential for high-impact weather. However, state-of-the-art numerical weather prediction and climate models struggle to correctly represent blocking life cycles, which results in large forecast errors at the medium-range to sub-seasonal timescale. Despite progress in recent years, we are still lacking a process-based conceptual understanding of blocked regime dynamics, which hinders a better representation of blocks in numerical models. In particular the relative contributions of dry and moist processes in the onset and maintenance of a block remain unclear.</p><p>Here we aim to revisit the dynamics of blocking in the Euro-Atlantic region. To this end we investigate the life cycles of blocked weather regimes from a potential vorticity (PV) perspective in ERA5 reanalysis data (from 1979 to present) from the European Centre for Medium-Range Weather Forecasts. We develop a diagnostic PV framework that allows the tracking of negative PV anomalies associated with blocked weather regimes. Complemented by piecewise PV-tendencies - separated into advective and diabatic PV tendencies - we are able to disentangle different physical processes affecting the amplitude evolution of negative PV anomalies associated with blocked regimes. Most importantly, this approach newly enables us to distinguish between the roles of dry and moist dynamics in the initiation and maintenance of blocked weather regimes in a common framework. A first application demonstrates the functionality of the developed PV framework and corroborates the importance of moist-diabatic processes in the initiation and maintenance of a block in a regime life cycle. </p>

scholarly journals Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics

2017 ◽  
Vol 8 (2) ◽  
pp. 387-403 ◽  
Author(s):  
Sebastian Sippel ◽  
Jakob Zscheischler ◽  
Miguel D. Mahecha ◽  
Rene Orth ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Global Climate Model ◽  
Large Fraction ◽  
Cmip5 Models ◽  
Temperature Extremes ◽  
Model Ensemble ◽  
Model Simulations

Abstract. The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter. This coupled behaviour causes various land–atmosphere feedbacks, and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in midlatitude regions has been identified empirically for high-impact heat waves, but individual climate models differ widely in their respective representation of land–atmosphere coupling. Here, we compile an ensemble of 54 combinations of observations-based temperature (T) and evapotranspiration (ET) benchmarking datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land–atmosphere interactions during periods of anomalously warm temperatures. First, we demonstrate that a large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in midlatitude regions during the warm season and in several tropical regions year-round. These coincidences (high T, low ET) are closely related to the representation of temperature variability and extremes across the multi-model ensemble. Second, we derive a land-coupling constraint based on the spread of the T–ET datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these benchmark estimates. The constrained multi-model simulations exhibit more realistic temperature extremes of reduced magnitude in present climate in regions where models show substantial spread in T–ET coupling, i.e. biases in the model ensemble are consistently reduced. Also the multi-model simulations for the coming decades display decreased absolute temperature extremes in the constrained ensemble. On the other hand, the differences between projected and present-day climate extremes are affected to a lesser extent by the applied constraint, i.e. projected changes are reduced locally by around 0.5 to 1 °C – but this remains a local effect in regions that are highly sensitive to land–atmosphere coupling. In summary, our approach offers a physically consistent, diagnostic-based avenue to evaluate multi-model ensembles and subsequently reduce model biases in simulated and projected extreme temperatures.

scholarly journals Sensitivity of age of air trends to the derivation method for non-linear increasing inert SF<sub>6</sub>

2020 ◽  
Vol 20 (14) ◽  
pp. 8709-8725 ◽  
Author(s):  
Frauke Fritsch ◽  
Hella Garny ◽  
Andreas Engel ◽  
Harald Bönisch ◽  
Roland Eichinger
Keyword(s):  
Time Series ◽  
Atmospheric Chemistry ◽  
Climate Models ◽  
Climate Model ◽  
Linear Increase ◽  
Parameter Selection ◽  
Test Bed ◽  
Uncertainty Range ◽  
Non Linear ◽  
Derivation Method

Abstract. Mean age of air (AoA) is a diagnostic of transport along the stratospheric Brewer–Dobson circulation. While models consistently show negative trends, long-term time series (1975–2016) of AoA derived from observations show non-significant positive trends in mean AoA in the Northern Hemisphere. This discrepancy between observed and modelled mean AoA trends is still not resolved. There are uncertainties and assumptions required when deriving AoA from trace gas observations. At the same time, AoA from climate models is subject to uncertainties, too. In this paper, we focus on the uncertainties due to the parameter selection in the method that is used to derive mean AoA from SF6 measurements in Engel et al. (2009, 2017). To correct for the non-linear increase in SF6 concentrations, a quadratic fit to the time series at the reference location, i.e. the tropical surface, is used. For this derivation, the width of the AoA distribution (age spectrum) has to be assumed. In addition, to choose the number of years the quadratic fit is performed for, the fraction of the age spectrum to be considered has to be assumed. Even though the uncertainty range due to all different aspects has already been taken into account for the total errors in the AoA values, the systematic influence of the parameter selection on AoA trends is described for the first time in the present study. For this, we use the EMAC (ECHAM MESSy Atmospheric Chemistry) climate model as a test bed, where AoA derived from a linear tracer is available as a reference and modelled age spectra exist to diagnose the actual spatial age spectra widths. The comparison of mean AoA from the linear tracer with mean AoA from a SF6 tracer shows systematic deviations specifically in the trends due to the selection of the parameters. However, for an appropriate parameter selection, good agreement for both mean AoA and its trend can be found, with deviations of about 1 % in mean AoA and 12 % in AoA trend. In addition, a method to derive mean AoA is evaluated that applies a convolution to the reference time series. The resulting mean AoA and its trend only depend on an assumption about the ratio of moments. Also in that case, it is found that the larger the ratio of moments, the more the AoA trend gravitates towards the negative. The linear tracer and SF6 AoA are found to agree within 0.3 % in the mean and 6 % in the trend. The different methods and parameter selections were then applied to the balloon-borne SF6 and CO2 observations. We found the same systematic changes in mean AoA trend dependent on the specific selection. When applying a parameter choice that is suggested by the model results, the AoA trend is reduced from 0.15 to 0.07 years per decade. It illustrates that correctly constraining those parameters is crucial for correct mean AoA and trend estimates and still remains a challenge in the real atmosphere.

scholarly journals Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies

2021 ◽  
Author(s):  
Eva Sebok ◽  
Hans Jørgen Henriksen ◽  
Ernesto Pastén-Zapata ◽  
Peter Berg ◽  
Guillume Thirel ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Model Performance ◽  
Climate Impact ◽  
Expert Elicitation ◽  
Model Structure ◽  
Hydrological Models ◽  
Model Ensemble ◽  
Impact Studies ◽  
Model Weighting

Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.

scholarly journals Refining multi-model projections of temperature extremes by evaluation against land–atmosphere coupling diagnostics

2016 ◽  
Author(s):  
Sebastian Sippel ◽  
Jakob Zscheischler ◽  
Miguel D. Mahecha ◽  
Rene Orth ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Heat Waves ◽  
Global Climate ◽  
Global Climate Model ◽  
Large Fraction ◽  
Cmip5 Models ◽  
Temperature Extremes ◽  
Model Ensemble

Abstract. The Earth's land surface and the atmosphere are strongly interlinked through the exchange of energy and matter (e.g. water and carbon). This coupled behaviour causes various land–atmosphere feedbacks and an insufficient understanding of these feedbacks contributes to uncertain global climate model projections. For example, a crucial role of the land surface in exacerbating summer heat waves in mid-latitude regions has been identified empirically for high-impact heatwaves, but individual climate models differ widely in their respective representation of land-atmosphere coupling. Here, we combine an ensemble of observations-based and simulated temperature (T) and evapotranspiration (ET) datasets and investigate coincidences of T anomalies with ET anomalies as a proxy for land-atmosphere interactions during periods of anomalously warm temperatures. We demonstrate that a relatively large fraction of state-of-the-art climate models from the Coupled Model Intercomparison Project (CMIP5) archive produces systematically too frequent coincidences of high T anomalies with negative ET anomalies in mid-latitude regions during the warm season and in several tropical regions year-round. Further, we show that these coincidences (high T, low ET), as diagnosed by the land-coupling coincidence metrics, are closely related to the variability and extremes of simulated temperatures across a multi-model ensemble. Thus, our approach offers a physically consistent, diagnostic-based avenue to evaluate these ensembles, and subsequently reduce model biases in simulated and predicted extreme temperatures. Following this idea, we derive a land-coupling constraint based on the spread of 54 combinations of T-ET benchmarking datasets and consequently retain only a subset of CMIP5 models that produce a land-coupling behaviour that is compatible with these observations-based benchmark estimates. The constrained multi-model projections exhibit lower temperature extremes in regions where models show substantial spread in T-ET coupling, and in addition, biases in the climate model ensemble are consistently reduced.

A brief discussion of the history, strengths and limitations of conceptual climate models for pre-Quaternary time

1993 ◽  
Vol 341 (1297) ◽  
pp. 263-266 ◽  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Early Stage ◽  
Numerical Models ◽  
Continental Drift ◽  
Structural Data ◽  
Atmospheric Dynamics ◽  
Climatic Data ◽  
Late Mesozoic ◽  
Zonal Model

Although it has been recognized at least since the time of Darwin and Agassiz that climate has varied significantly over geologic time, the study of global palaeoclimate did not come into its own until the theory of continental drift became ascendant. Initial studies in the early 1960s used climate models to test the reconstructions of continental positions. These studies, many collected in a pair of symposium volumes edited by A. E. M. Nairn, used a zonal model of climate or simple modifications thereof to predict how certain palaeoclimatic indicators - principally evaporites, coals, carbonates, red beds, and eolian sandstones - should be distributed on the continents through time if the continental reconstructions were correct. Even at that early stage in the development of continental reconstructions, past patterns of sedimentation were more clearly explained than had previously been the case. Continental reconstructions eventually began to stabilize, at least with respect to the major plates, in the late 1970s. Most of the information for positioning the continents came from paleomagnetic and structural data, but some elements of continental reconstructions relied heavily on climatic data - and the zonal climate model - for positioning. Nevertheless, it was at this time that studies of global palaeoclimate, independent of the concerns about the positions of the continents, could begin in earnest. A primary need was independence of the continental reconstructions from palaeoclimatic data, an ideal even now fully realized only for the late Mesozoic and Cenozoic. The term ‘conceptual climate model’ was coined by J . E. Kutzbach in reference to models published in the early 1980s. Like numerical models, conceptual climate models are based on the fundamentals of atmospheric circulation as determined from studies of the modern climate system, without explicitly treating atmospheric dynamics. They are reproducible and useful for developing an understanding of major changes in climate patterns driven by the changing positions of the continents. Despite their simplicity and non-explicit treatment of atmospheric dynamics, conceptual climate models have proved to be surprisingly robust in that the patterns predicted by explicitly dynamical models are similar for any given geologic period.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

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