Regional Climate Model Simulations Research Articles

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

ABSTRACT The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very good with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, delta change method seems to be with no biases for maximum temperature, but it increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C amount from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

The added value of simulated near-surface wind speed over the Alps from a km-scale multimodel ensemble

AbstractThe advancement of computational resources has allowed researchers to run convection-permitting regional climate model (CPRCM) simulations. A pioneering effort promoting a multimodel ensemble of such simulations is the CORDEX Flagship Pilot Studies (FPS) on “Convective Phenomena over Europe and the Mediterranean” over an extended Alps region. In this study, the Distribution Added Value metric is used to determine the improvement of the representation of all available FPS hindcast simulations for the daily mean near-surface wind speed. The analysis is performed on normalized empirical probability distributions and considers station observation data as the reference. The use of a normalized metric allows for spatial comparison among the different regions (coast and inland), altitudes and seasons. This approach permits a direct assessment of the added value between the CPRCM simulations against their global driving reanalysis (ERA-Interim) and respective coarser resolution regional model counterparts. In general, the results show that CPRCMs add value to their global driving reanalysis or forcing regional model, due to better-resolved topography or through better representation of ocean-land contrasts. However, the nature and magnitude of the improvement in the wind speed representation vary depending on the model, the season, the altitude, or the region. Among seasons, the improvement is usually larger in summer than winter. CPRCMs generally display gains at low and medium-range altitudes. In addition, despite some shortcomings in comparison to ERA-Interim, which can be attributed to the assimilation of wind observations on the coast, the CPRCMs outperform the coarser regional climate models, both along the coast and inland.

Evaluation of precipitation extremes in ERA5 reanalysis driven regional climate simulations over the CORDEX-Australasia domain

Reanalysis-driven regional climate simulations using the Weather Research and Forecasting (WRF) model in New South Wales (NSW) and Australian Regional Climate Modelling (NARCliM) Version 2.0 are assessed for capturing precipitation extreme indices. Seven configurations of the WRF model driven by ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) for Australia from 1979 to 2020 at 20 km resolution are evaluated. We assess the spatiotemporal patterns of six selected Expert Team on Sector-Specific Climate Indices (ET-SCI) precipitation extremes by comparing regional climate model (RCM) simulations against gridded observations. The RCMs evaluated have varying levels of accuracy in simulating precipitation extremes. While they capture climatology and coefficient of variation of precipitation extremes relatively well, temporal correlation and trend reproduction present challenges. Some RCMs perform more effectively for specific extreme indices, while others encounter challenges in accurately replicating them. No single RCM excels in all aspects, highlighting the need to consider specific strengths when selecting RCMs for global climate model (GCM) driven simulations.

Mid-century climate change impacts on tornado-producing tropical cyclones

Tornadoes are a co-occurring extreme that can be produced by landfalling tropical cyclones (TCs). These tornadoes can exacerbate the loss of life and property damage caused by the TC from which they were spawned. It is uncertain how the severe weather environments of landfalling TCs may change in a future climate and how this could impact tornado activity from TCs. In this study, we investigated four TCs that made landfall in the U.S. and produced large tornado outbreaks. We performed four-member ensembles of convective-allowing (4-km resolution) regional climate model simulations representing each TC in the historical climate and a mid-twenty-first century future climate. To identify potentially tornadic storms, or TC-tornado (TCT) surrogates, we used thresholds for three-hourly maximum updraft helicity and radar reflectivity, as tornadoes are not resolved in the model. We found that the ensemble-mean number of TCT-surrogates increased substantially (56–299%) in the future, supported by increases in most-unstable convective available potential energy, surface-to-700-hPa bulk wind shear, and 0–1-km storm-relative helicity in the tornado-producing region of the TCs. On the other hand, future changes in most-unstable convective inhibition had minimal influence on future TCT-surrogates. This provides robust evidence that tornado activity from TCs may increase in the future. Furthermore, TCT-surrogate frequency between 00Z and 09Z increased for three of the four cases, suggesting enhanced tornado activity at night, when people are asleep and more likely to miss warnings. All of these factors indicate that TC-tornadoes may become more frequent and a greater hazard in the future, compounding impacts from future increases in TC winds and precipitation.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Improving land surface feedbacks to the atmosphere in convection-permitting climate simulations for Europe

AbstractWe investigated positive temperature (warm) and negative precipitation (dry) biases in convection-permitting model (CPM) simulations for Europe (2.2 km grid spacing) that were considerably larger than in equivalent regional climate model (RCM) simulations (12 km grid spacing). We found that improvements in dry biases could be made by (1) using a more complex runoff scheme which takes into account topography and groundwater, (2) delaying the onset of water stress in vegetation to enhance transpiration, (3) changing the microphysics scheme to CASIM (Cloud AeroSol Interacting Microphysics) which also decreases heavy rainfall and increases light rainfall. Increasing soil moisture to the critical point can remove dry precipitation biases in southern Europe but not in northern areas, indicating that soil moisture limitation is a key contributor to precipitation biases in the south only. Instead, in the north, changing the cloud scheme of the model has more impact on precipitation biases. We found that the more intense and intermittent nature of rainfall in the CPM, which is more realistic leads to different canopy interception compared to the RCM. This can impact canopy evaporation, evapotranspiration and feed back on precipitation. Increasing rainfall storage in the canopy only leads to small improvements in warm biases, since it still fills rapidly with intense CPM rainfall, suggesting the need for an additional moisture store via improved groundwater modelling or surface pooling. Overall, this work highlights the challenge of correctly capturing land surface feedbacks in CPMs, which play an important role in future climate projections in some regions.

Inter‐Comparison of Precipitation Simulation and Future Projections Over China From an Ensemble of Multi‐GCM Driven RCM Simulations

AbstractAn analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing.

Seasonal pumped hydropower storage role in responding to climate change impacts on the Brazilian electrical sector

Since Brazil's major energy resources are renewable and directly related to climate factors, it is among the countries most likely of being affected by climate change. Given Brazil's high hydropower storage capacity and the strong seasonal patterns of its renewable resources, introducing Seasonal Pumped Hydropower Storage (SPHS) can help mitigate these challenges. To this end, a methodology is proposed that links the dynamic system-optimization model – MESSAGEix - to regional climate model simulations, called the Brazilian Electricity System MESSAGEix Model (BESMM). This model, with its detailed hydropower representation, is capable of integrating data from three climate change scenarios with the country's energy system. Climate change introduces a new dimension to this approach, as there is evidence of increasing the seasonal imbalance of variable renewable resources in Brazil. BESMM results suggest that SPHS can play a fundamental role in achieving a 100 % renewable matrix by 2100 in RCP 2.6 scenario, as well as enhancing the renewable energy endowment in scenarios RCP 4.5 and RCP 8.5. A reduction of up to 68 % of CO2 emissions is predicted in scenarios incorporating SPHS, compared to scenarios without SPHS.

Assessment of the hydrological impact of an hourly precipitation distribution correction method in the SASER modeling system

The SASER (SAfran-Surfex-Eudyssée-Rapid) hydrological modeling system is used for simulating the continental hydrological cycle. The validation of the simulated streamflow showed that the results were generally accurate but with a tendency towards underestimating peak values. This underestimation has several causes, but in this study, we addressed the known fact that the precipitation forcing data produced by SAFRAN is good at the daily time scale, as it ingests daily precipitation data, but its performance in interpolating daily to hourly precipitation data using relative humidity as a weight is poor. Our objective was to improve the SAFRAN hourly precipitation distribution and evaluate its hydrological impact on SURFEX LSM simulations. We used a correction method to adjust the hourly precipitation distribution of SAFRAN based on a more realistic distribution obtained from a regional climate model simulation forced by ERA-Interim (CNRM-ALADIN). At the grid point scale, the correction preserves SAFRAN’s accumulated precipitation on a time window of a few days, while reflecting the RCM’s hourly variability of precipitation. We evaluated the correction method across different time windows and found that the 7-day correction showed the highest performance. Our results show that hourly precipitation patterns have a strong influence on surface runoff at the event and grid-point scales. However, at the long-term and catchment scale, the sensitivity to the use of the new precipitation forcing dataset was limited.

Future flood envelope curves for the estimation of design flood magnitudes for highway bridges at river crossings

Creager flood envelope curves, which serve as the upper bound/limit of observed extreme flows, are commonly used by practitioners to estimate design flood magnitudes, which in the case of most river-crossing highway bridges is 75-year flood magnitude in Canada. This study proposes a novel framework for climate change adaption of Creager curves for estimating future design floods. These curves, for the current period, are assessed considering 417 observation stations, located in seven major Canadian river basins (i.e., Fraser, Nelson, Mackenzie, Yukon, Churchill, St Lawrence and St John). The Creager coefficient C, which defines flood envelope curves, varies between 1 and 45 across the studied river basins. To adapt Creager curves for future changes in streamflow, a correction factor, RC, which is the ratio of future to current period C values, is proposed. These factors are obtained for observation sites, using streamflow data from an ensemble of Regional Climate Model (RCM) simulations for current and future periods, through two Regional Frequency Analysis approaches. The first approach, considering only the RCM cells where the stations are located, suggests RC in the 0.3–1.6 range, with southeasterly basins showing values < 1. The second approach, considering all RCM cells for a given region, yields a wider range for RC and adds useful information in that RC values can also be established at ungauged locations. From a practical viewpoint, the proposed framework for estimating future design floods is robust and transferrable to other basins, but can benefit using streamflow projections from other models for better uncertainty quantification.

Large-scale heavy precipitation over the Czech Republic and its link to atmospheric circulation in CORDEX regional climate models

AbstractThe study evaluates ability of regional climate models (RCMs) to reproduce relationships between large-scale heavy precipitation events (LHPEs) over the Czech Republic and atmospheric circulation. We use an ensemble of 32 RCM simulations with the 0.11° resolution from the Euro-CORDEX project, and compare the historical simulations (1951–2005) against observations from the E-OBS data set. A novel selection criterion for LHPEs is proposed, defining these as days with at least 70% of all grid boxes over a given area with precipitation amounts exceeding the 90th grid-specific percentile of the seasonal distribution of daily amounts. The association with atmospheric circulation is investigated through circulation types derived from sea level pressure using airflow indices (direction, strength and vorticity). The majority of the RCMs capture that the frequency of days with LHPEs is higher in winter than summer, but almost all underestimate the occurrence of LHPEs in both seasons. In winter, the observed LHPEs are connected mainly with cyclonic types and westerly supertype; the role of nonwesterly and cyclonic-nonwesterly supertypes is significant only in the eastern part, where the Atlantic influence is weaker. In summer, the importance of cyclonic and nonwesterly types in producing LHPEs increases compared to winter. The RCMs reasonably well reproduce these links, including differences between seasons and regions, if their ensemble mean is evaluated, but large variations occur among individual simulations mainly in summer. The importance of cyclonic vorticity is overestimated in the RCMs, while westerly advection of moist air plays a smaller role in models than in observations.

Impact of groundwater representation on heat events in regional climate simulations over Europe

Abstract. The representation of groundwater is simplified in most regional climate models (RCMs), potentially leading to biases in the simulations. This study introduces a unique dataset from the regional Terrestrial Systems Modelling Platform (TSMP) driven by the Max Planck Institute Earth System Model at Low Resolution (MPI-ESM-LR) boundary conditions in the context of dynamical downscaling of global climate models (GCMs) for climate change studies. TSMP explicitly simulates full 3D soil and groundwater dynamics together with overland flow, including complete water and energy cycles from the bedrock to the top of the atmosphere. By comparing the statistics of heat events, i.e., a series of consecutive days with a near-surface temperature exceeding the 90th percentile of the reference period, from TSMP and those from GCM–RCM simulations with simplified groundwater dynamics from the COordinated Regional Climate Downscaling EXperiment (CORDEX) for the European domain, we aim to improve the understanding of how groundwater representation affects heat events in Europe. The analysis was carried out using RCM outputs for the summer seasons of 1976–2005 relative to the reference period of 1961–1990. While our results show that TSMP simulates heat events consistently with the CORDEX ensemble, there are some systematic differences that we attribute to the more realistic representation of groundwater in TSMP. Compared to the CORDEX ensemble, TSMP simulates fewer hot days (i.e., days with a near-surface temperature exceeding the 90th percentile of the reference period) and lower interannual variability and decadal change in the number of hot days on average over Europe. TSMP systematically simulates fewer heat waves (i.e., heat events lasting 6 d or more) compared to the CORDEX ensemble; moreover, they are shorter and less intense. The Iberian Peninsula is particularly sensitive with respect to groundwater. Therefore, incorporating an explicit 3D groundwater representation in RCMs may be a key in reducing biases in simulated duration, intensity, and frequency of heat waves in Europe. The results highlight the importance of hydrological processes for the long-term regional climate simulations and provide indications of possible potential implications for climate change projections.

A Standardized Benchmarking Framework to Assess Downscaled Precipitation Simulations

Abstract Presently, there is no standardized framework or metrics identified to assess regional climate model precipitation output. Because of this, it can be difficult to make a one-to-one comparison of their performance between regions or studies, or against coarser-resolution global climate models. To address this, we introduce the first steps toward establishing a dynamic, yet standardized, benchmarking framework that can be used to assess model skill in simulating various characteristics of rainfall. Benchmarking differs from typical model evaluation in that it requires that performance expectations are set a priori. This framework has innumerable applications to underpin scientific studies that assess model performance, inform model development priorities, and aid stakeholder decision-making by providing a structured methodology to identify fit-for-purpose model simulations for climate risk assessments and adaptation strategies. While this framework can be applied to regional climate model simulations at any spatial domain, we demonstrate its effectiveness over Australia using high-resolution, 0.5° × 0.5° simulations from the CORDEX-Australasia ensemble. We provide recommendations for selecting metrics and pragmatic benchmarking thresholds depending on the application of the framework. This includes a top tier of minimum standard metrics to establish a minimum benchmarking standard for ongoing climate model assessment. We present multiple applications of the framework using feedback received from potential user communities and encourage the scientific and user community to build on this framework by tailoring benchmarks and incorporating additional metrics specific to their application. Significance Statement We introduce a standardized benchmarking framework for assessing the skill of regional climate models in simulating precipitation. This framework addresses the lack of a uniform approach in the scientific community and has diverse applications in scientific research, model development, and societal decision-making. We define a set of minimum standard metrics to underpin ongoing climate model assessments that quantify model skill in simulating fundamental characteristics of rainfall. We provide guidance for selecting metrics and defining benchmarking thresholds, demonstrated using multiple case studies over Australia. This framework has broad applications for numerous user communities and provides a structured methodology for the assessment of model performance.

Towards an intelligent malaria outbreak warning model based intelligent malaria outbreak warning in the northern part of Benin, West Africa

BackgroundMalaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.MethodsClimate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4).A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).ResultsTwo ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin.The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.ConclusionThese results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.

Assessing of Domain Change Sensitivity for Regional Climate Model simulation (Reg-Cm4.3) at Blue Nile Basin

Assessing of Domain Change Sensitivity for Regional Climate Model simulation (Reg-Cm4.3) at Blue Nile Basin

High-resolution projections of ambient heat for major European cities using different heat metrics

Abstract. Heat stress in cities is projected to strongly increase due to climate change. The associated health risks will be exacerbated by the high population density in cities and the urban heat island effect. However, impacts are still uncertain, which is among other factors due to the existence of multiple metrics for quantifying ambient heat and the typically rather coarse spatial resolution of climate models. Here we investigate projections of ambient heat for 36 major European cities based on a recently produced ensemble of regional climate model simulations for Europe (EURO-CORDEX) at 0.11° spatial resolution (∼ 12.5 km). The 0.11° EURO-CORDEX ensemble provides the best spatial resolution currently available from an ensemble of climate model projections for the whole of Europe and makes it possible to analyse the risk of temperature extremes and heat waves at the city level. We focus on three temperature-based heat metrics – yearly maximum temperature, number of days with temperatures exceeding 30 °C, and Heat Wave Magnitude Index daily (HWMId) – to analyse projections of ambient heat at 3 °C warming in Europe compared to 1981–2010 based on climate data from the EURO-CORDEX ensemble. The results show that southern European cities will be particularly affected by high levels of ambient heat, but depending on the considered metric, cities in central, eastern, and northern Europe may also experience substantial increases in ambient heat. In several cities, projections of ambient heat vary considerably across the three heat metrics, indicating that estimates based on a single metric might underestimate the potential for adverse health effects due to heat stress. Nighttime ambient heat, quantified based on daily minimum temperatures, shows similar spatial patterns to daytime conditions, albeit with substantially higher HWMId values. The identified spatial patterns of ambient heat are generally consistent with results from global Earth system models, though with substantial differences for individual cities. Our results emphasise the value of high-resolution climate model simulations for analysing climate extremes at the city level. At the same time, they highlight that improving the predominantly rather simple representations of urban areas in climate models would make their simulations even more valuable for planning adaptation measures in cities. Further, our results stress that using complementary metrics for projections of ambient heat gives important insights into the risk of future heat stress that might otherwise be missed.

Cloud- and ice-albedo feedbacks drive greater Greenland Ice Sheet sensitivity to warming in CMIP6 than in CMIP5

Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.

Changes in Atmospheric Dynamics Over Dansgaard‐Oeschger Climate Oscillations Around 40 ka and Their Impact on Europe

AbstractDansgaard‐Oeschger (D‐O) climate variability during the last glaciation was first evidenced in ice cores and marine sediments, and is also recorded in various terrestrial paleoclimate archives in Europe. The relative synchronicity across Greenland, the North Atlantic and Europe implies a tight and fast coupling between those regions, most probably effectuated by an atmospheric transmission mechanism. In this study, we investigated the atmospheric changes during Greenland interstadial (GI) and stadial (GS) phases based on regional climate model simulations using two specific periods, GI‐10 and GS‐9 both around 40 ka, as boundary conditions. Our simulations accurately capture the changes in temperature and precipitation as reconstructed by the available proxy data. Moreover, the simulations depict an intensified and southward shifted eddy‐driven jet during the stadial period. Ultimately, this affects the near‐surface circulation toward more southwesterly and cyclonic flow in western Europe during the stadial period, explaining much of the seasonal climate variability recorded by the proxy data, including oxygen isotopes, at the considered proxy sites.