Resolution Global Climate Model Research Articles

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

AbstractProjecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM‐LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM‐LIN method improved R2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM‐LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

Quantile-Regression-Ensemble: A Deep Learning Algorithm for Downscaling Extreme Precipitation

Global Climate Models (GCMs) simulate low resolution climate projections on a global scale. The native resolution of GCMs is generally too low for societal-level decision-making. To enhance the spatial resolution, downscaling is often applied to GCM output. Statistical downscaling techniques, in particular, are well-established as a cost-effective approach. They require significantly less computational time than physics-based dynamical downscaling. In recent years, deep learning has gained prominence in statistical downscaling, demonstrating significantly lower error rates compared to traditional statistical methods. However, a drawback of regression-based deep learning techniques is their tendency to overfit to the mean sample intensity. Extreme values as a result are often underestimated. Problematically, extreme events have the largest societal impact. We propose Quantile-Regression-Ensemble (QRE), an innovative deep learning algorithm inspired by boosting methods. Its primary objective is to avoid trade-offs between fitting to sample means and extreme values by training independent models on a partitioned dataset. Our QRE is robust to redundant models and not susceptible to explosive ensemble weights, ensuring a reliable training process. QRE achieves lower Mean Squared Error (MSE) compared to various baseline models. In particular, our algorithm has a lower error for high-intensity precipitation events over New Zealand, highlighting the ability to represent extreme events accurately.

An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)

Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.

Deep learning-based spatial downscaling and its application for tropical cyclone detection in the western North Pacific

Resolution of global climate models (GCMs) significantly influences their capacity to simulate extreme weather such as tropical cyclones (TCs). However, improving the GCM resolution is computationally expensive and time-consuming, making it challenging for many research organizations worldwide. Here, we develop a downscaling model, MSG-SE-GAN, based on the Generative Adversarial Networks (GAN) together with Multiscale Gradient (MSG) technique and a Squeeze-and-Excitation (SE) Net, to achieve 10-folded downscaling. GANs consist of a generator and a discriminator network that are trained adversarially, and are often used for generating new data that resembles a given dataset. MSG enables generation and discrimination of multi-scale images within a single model. Inclusion of an attention layer of SE captures better underlying spatial structure while preserving accuracy. The MSG-SE-GAN is stable and fast converging. It outperforms traditional bilinear interpolation and other deep-learning methods such as Super-Resolution Convolutional Neural Networks (SRCNN) and MSG-GAN in downscaling low-resolution meteorological data in assessment metrics and power spectral density. The MSG-SE-GAN has been used to downscale the TC-related variables in the western North Pacific in the low-resolution GCMs of HadGEM3-GC31 and EC-Earth3P, respectively. The downscaled data show highly similar TC activities to the direct outputs of the high-resolution HadGEM3-GC31 and EC-Earth3P, respectively. These results not only suggest the validity of the MSG-SE-GAN but also indicate its possible portability among low-resolution GCMs.

Using high resolution climate models to explore future changes in post-tropical cyclone precipitation

One of the most costly effects of climate change will be its impact on extreme weather events, including tropical cyclones (TCs). Understanding these changes is of growing importance, and high resolution global climate models are providing potential for such studies, specifically for TCs. Beyond the difficulties associated with TC behavior in a warming climate, the extratropical transition (ET) of TCs into post-tropical cyclones (PTCs) creates another challenge when understanding these events and any potential future changes. PTCs can produce excessive rainfall despite losing their original tropical characteristics. The present study examines the representation of PTCs and their precipitation in three high resolution (25–50 km) climate models: CNRM, MRI, and HadGEM. All three of these models agree on a simulated decrease in TC and PTC events in the future warming scenario, yet they lack consistency in simulated regional patterns of these changes, which is further evident in regional changes in PTC-related precipitation. The models also struggle with their represented intensity evolution of storms during and after the ET process. Despite these limitations in simulating intensity and regional characteristics, the models all simulate a shift toward more frequent rain rates above 10 mm h−1 in PTCs. These high rain rates become 4%–12% more likely in the warmer climate scenario, resulting in a 5%–12% increase in accumulated rainfall from these rates.

Constraining an eddy energy dissipation rate due to relative wind stress for use in energy budget-based eddy parameterisations

Abstract. A geostrophic eddy energy dissipation rate due to the interaction of the large-scale wind field and mesoscale ocean currents, or relative wind stress, is derived here for use in eddy energy budget-based eddy parameterisations. We begin this work by analytically deriving a relative wind stress damping term and a baroclinic geostrophic eddy energy equation. The time evolution of this analytical eddy energy in response to relative wind stress damping is compared directly with a baroclinic eddy in a general circulation model for both anticyclones and cyclones. The dissipation of eddy energy is comparable between each model and eddy type, although the numerical model diverges from the analytical model at around day 150, likely due to the presence of non-linear baroclinic processes. A constrained dissipation rate due to relative wind stress is then proposed using terms from the analytical eddy energy budget. This dissipation rate depends on the potential energy of the eddy thermocline displacement, which also depends on eddy length scale. Using an array of ocean datasets, and computing two forms for the eddy length scale, a range of values for the dissipation rate are presented. The analytical dissipation rate is found to vary from 0.25 to 4 times that of a constant dissipation rate employed in previous studies. The dissipation rates are generally enhanced in the Southern Ocean but smaller in the western boundaries. This proposed dissipation rate offers a tool to parameterise the damping of total eddy energy in coarse resolution global climate models and may have implications for a wide range of climate processes.

Fewer, but More Intense, Future Tropical Storms Over the Ganges and Mekong Basins

AbstractUnderstanding climate change impacts on Tropical Storm (TS) activity is crucial for effective adaptation planning and risk assessment, particularly in densely populated low‐lying delta rivers basins like the Ganges and Mekong. The change to TS characteristics with warming is uncertain due to limitations in global climate model resolution and process‐representation and storm tracking algorithms (trackers). Here, we used 13 HighResMIP models and two trackers to estimate the uncertainty in projections of TS characteristics. We found different trackers producing qualitatively similar but quantitatively different results. Our results show a decline (median ∼52%) in the frequency of TS but increase in the strongest TS and Available Cyclone Energy (ACE) of TS over both basins. The higher‐resolution models extract TS with much higher intensity and ACE values compared to the lower‐resolution models. These results have implications for adaptation planning and risk assessment for TS and suggest the need for further high‐resolution modeling studies.

Past and future freshwater availability scenarios and their impact on the Indian Sundarbans ecosystem and fisheries

Abstract The Indian Sundarbans is considered one of the zones of highest vulnerability in the world in terms of climate change. About 4.43 million people living in the Indian Sundarbans face a lack of freshwater availability due to the erratic behaviour of monsoon rains, frequent cyclonic storms, intrusion of saline water, and other factors, all of which affect the fisheries and agriculture activities of this area. In this study, estimates of freshwater availability through past and predicted future rainfall and evapotranspiration change scenarios in the Sundarbans are presented. Due to the lack of high-quality in-situ data, various sources of gridded rainfall and evapotranspiration data were used. Between 1948 and 2010, half of the 19 administrative blocks showed a decreasing trend of monsoonal rainfall while the rest showed an increasing trend. Freshwater availability showed a decreasing trend during the monsoon season over different blocks of the Sundarbans, which is a matter of great concern for fisheries and agricultural activities. Statistical downscaling was used to generate future rainfall and evapotranspiration scenarios, using coarse resolution Global Climate Models from the Coupled Model Intercomparison Project phase five for a smaller area like the Sundarbans. Downscaled Global Climate Models project an increasing trend in future monsoon rainfall in both RCP 4.5 and RCP 8.5 emission scenarios. The increasing rainfall can trigger excessive run-off and flooding, which would in turn affect aquaculture infrastructure and damage lentic aquaculture productions across the Sundarbans. However, increased rainfall may expand the flood plain area and extend the feeding grounds of fish. Hence, the impact of rainfall change is quite unpredictable. Proper adaptation techniques may be required to harness the positive impacts while preventing negative effects.

Modeling the Production of Heinrich Layers With a Sediment‐Enabled Iceberg Model

AbstractIn the North Atlantic, relatively coarse grained sediments can be found periodically throughout sediment cores spanning the Last Glacial Period. These sediments were rafted by icebergs released from the Laurentide Ice Sheet (LIS) in so‐called Heinrich Events. These “Heinrich Layers” coincide with records of global climate change, suggesting that the impact of these events was propagated beyond the North Atlantic. In order to best understand the climate context and significance of Heinrich Events, it is important to constrain the mechanism for their release from the LIS and the nature of the ice sheet itself. One approach for investigating the source of Heinrich Events is to understand the sediment load of icebergs involved, information that would inform interpretations of how those icebergs were produced. By simulating Heinrich Events in a high resolution global climate model (20–40 times the resolution of previous studies), this work investigates the processes involved in the deposition of Heinrich Layers in the North Atlantic. In these simulations, the same volume of sediment is distributed differently through the same volume of icebergs, producing profoundly different sediment records. Due to the high resolution of the model, these simulated sedimentary layers can be inspected in great detail, revealing nuances of the deposit. Only when sediment is distributed throughout the entire iceberg does the model produce a sediment pattern in agreement with observations, yet icebergs with this sediment distribution are not observed in the modern‐day.

Evaluation of global climate models for the simulation of precipitation and maximum and minimum temperatures at coarser and finer resolutions based on temporal and spatial assessment metrics in mainland of China

Abstract Many studies have evaluated the performance of multiple global climate models (GCMs) from a temporal or spatial perspective at finer resolution, but no study has evaluated the performance of individual GCMs at different resolutions and before and after bias correction from both temporal and spatial perspectives. The goal of this study is to evaluate the performance of 21 Coupled Model Inter-comparison Project 6 (CMIP6) GCMs at the raw (coarser) and downscaled (finer) resolutions and after bias correction in relation to their skills in the simulation of daily precipitation and maximum and minimum temperatures over China for the period 1961–2014 using state-of-the-art temporal and spatial metrics, Kolmogorov–Smirnov statistic and SPAtial EFficiency. The results indicated some differences in the ranks of GCMs between temporal and spatial metrics at different resolutions. The overall ranking shows that the simulations at the raw resolution of GCMs are more similar to the observations than the simulations after inverse distance weighted interpolation in SPAtial EFficiency. Three variables from bias-corrected GCMs ranked from 1 to 21 show similar good performance in spatial patterns but the poorest trend in empirical Cumulative Distribution Functions (ECDFs) except daily precipitation.

Past and future freshwater availability scenarios and their impact on the Indian Sundarbans ecosystem and fisheries

Abstract The Indian Sundarbans are considered one of the zones of highest vulnerability in the world in terms of climate change. About 4.43 million people living in the Indian Sundarbans face a lack of freshwater availability due to the erratic behaviour of monsoon rains, frequent cyclonic storms, intrusion of saline water, and other factors, all of which affect the fisheries and agriculture activities of this area. In this study, estimates of freshwater availability through past and predicted future rainfall and evapotranspiration change scenarios in the Sundarbans are presented. Due to the lack of high-quality in situ data, various sources of gridded rainfall and evapotranspiration data were used. Between 1948 and 2010, half of the 19 administrative blocks showed a decreasing trend of monsoonal rainfall while the rest showed an increasing trend. Freshwater availability showed a decreasing trend during the monsoon season over different blocks of the Sundarbans, which is a matter of great concern for fisheries and agricultural activities. Statistical downscaling was used to generate future rainfall and evapotranspiration scenarios, using coarse resolution Global Climate Models from the Coupled Model Inter-comparison Project, Phase Five for a smaller area like the Sundarbans. Downscaled global climate models project an increasing trend in future monsoon rainfall in both RCP 4.5 and RCP 8.5 emission scenarios. The increasing rainfall can trigger excessive run-off and flooding, which would in turn affect aquaculture infrastructure and damage lentic aquaculture productions across the Sundarbans. However, increased rainfall may expand the flood plain area and extend the feeding grounds of fish. Hence, the impact of rainfall change is quite unpredictable. Proper adaptation techniques may be required to harness the positive impacts while preventing negative effects.

Precipitation frequency in Med-CORDEX and EURO-CORDEX ensembles from 0.44° to convection-permitting resolution: impact of model resolution and convection representation

Recent studies using convection-permitting (CP) climate simulations have demonstrated a step-change in the representation of heavy rainfall and rainfall characteristics (frequency-intensity) compared to coarser resolution Global and Regional climate models. The goal of this study is to better understand what explains the weaker frequency of precipitation in the CP ensemble by assessing the triggering process of precipitation in the different ensembles of regional climate simulations available over Europe. We focus on the statistical relationship between tropospheric temperature, humidity and precipitation to understand how the frequency of precipitation over Europe and the Mediterranean is impacted by model resolution and the representation of convection (parameterized vs. explicit). We employ a multi-model data-set with three different resolutions (0.44°, 0.11° and 0.0275°) produced in the context of the MED-CORDEX, EURO-CORDEX and the CORDEX Flagship Pilot Study "Convective Phenomena over Europe and the Mediterranean" (FPSCONV). The multi-variate approach is applied to all model ensembles, and to several surface stations where the integrated water vapor (IWV) is derived from Global Positioning System (GPS) measurements. The results show that all model ensembles capture the temperature dependence of the critical value of IWV (IWVcv), above which an increase in precipitation frequency occurs, but the differences between the models in terms of the value of IWVcv, and the probability of its being exceeded, can be large at higher temperatures. The lower frequency of precipitation in convection-permitting simulations is not only explained by higher temperatures but also by a higher IWVcv necessary to trigger precipitation at similar temperatures, and a lower probability to exceed this critical value. The spread between models in simulating IWVcv and the probability of exceeding IWVcv is reduced over land in the ensemble of models with explicit convection, especially at high temperatures, when the convective fraction of total precipitation becomes more important and the influence of the representation of entrainment in models thus becomes more important. Over lowlands, both model resolution and convection representation affect precipitation triggering while over mountainous areas, resolution has the highest impact due to orography-induced triggering processes. Over the sea, since lifting is produced by large-scale convergence, the probability to exceed IWVcv does not depend on temperature, and the model resolution does not have a clear impact on the results.

Dependence of Convective Cloud Properties and Their Transport on Cloud Fraction and GCM Resolution Diagnosed from a Cloud-Resolving Model Simulation

The scale-aware convective parameterization for high resolution global climate models must satisfy the requirement that the parameterized subgrid convective transport diminishes as the model resolution increases to convection-resolving resolutions. A major assumption in current scale-aware convection schemes is that the differences between convective cloud properties and their environmental counterparts are independent of cloud fraction. This study examines convective cloud vertical velocity, moist static energy (MSE), moisture, and the vertical eddy transport of MSE and moisture for different averaging subdomain sizes and fractional convective cloudiness using a cloud resolving model simulation of a midlatitude mesoscale convective system. Results show that convective cloud fraction, mass flux, and vertical transport of MSE and moisture increase with decreasing subdomain size. The differences between convective cloud properties in both updrafts and downdrafts and their environment depend on both cloud fraction and the averaging subdomain size. For a given subdomain size, the differences increase with cloud fraction, in contrast to the assumption used in current scale-aware convection parameterization schemes. A consequence of this is that the parameterized convective eddy transport reaches maximum at a higher cloud fraction than believed in previous studies. This has implications on how fast the subgrid convective transport should diminish as GCM resolution increases.

Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050

Context. Sites for next-generation telescopes are chosen decades before the first light of a telescope. Site selection is usually based on recent measurements over a period that is too short to account for long-term changes in observing conditions such as those arising from anthropogenic climate change. For astronomical facilities with a typical lifetime of 30 yr, it is therefore essential to be aware of climate evolution to optimise observing time. Aims. In this study, we analyse trends in astronomical observing conditions for eight sites. Most sites either already host telescopes that provide in situ measurements of weather parameters or are candidates for hosting next-generation telescopes. For a fine representation of orography, we use the highest resolution global climate model (GCM) ensemble available provided by the high-resolution model intercomparison project and developed as part of the European Union Horizon 2020 PRIMAVERA project. Methods. We evaluate atmosphere-only and coupled PRIMAVERA GCM historical simulations against in situ measurements and the fifth generation atmospheric reanalysis (ERA5) of the European centre for medium-range weather forecasts for the period 1979–2014. The projections of changes in current site conditions are then analysed for the period 2015–2050 using PRIMAVERA future climate simulations. Results. Over most sites, we find that PRIMAVERA GCMs show a good agreement in temperature, specific humidity, and precipitable water vapour compared to in situ observations and ERA5. The ability of PRIMAVERA to simulate those variables increases confidence in their projections. For those variables, the model ensemble projects an increasing trend for all sites, which will result in progressively poorer astronomical observing conditions compared to current conditions. On the other hand, no significant trends are projected for relative humidity, cloud cover, or astronomical seeing and PRIMAVERA does not simulate these variables well compared to observations and reanalyses. Therefore, there is little confidence in these projections. Conclusions. Our results show that climate change will negatively impact the quality of astronomical observations and is likely to increase time lost due to bad site conditions. We stress that it is essential for astronomers to include long-term climate projections in their process for site selection and monitoring. We show that high-resolution GCMs can be used to analyse the effect of climate change on site characteristics of next-generation telescopes.

Unexpected Changes of Aerosol Burdens With Decreased Convection in the Context of Scale‐Aware Convection Schemes

Atmospheric aerosols in global climate models (GCMs) can be removed by both large-scale and convective precipitation. As the horizontal resolution of GCMs increases, it is expected that large-scale precipitation increases and convective precipitation decreases if scale-aware convection schemes are well developed. To explore its impact on aerosol burdens, this study develops a novel method and applies it to the National Center for Atmospheric Research Community Atmosphere Model version 5.3 to mimic the behavior of scale-aware convective schemes. Surprisingly, it is found that aerosol burden increases globally as convective precipitation decreases and large-scale precipitation increases, which can be attributed to the reduced light rain. In the light-rain intensity category, the decreased frequency of convective precipitation is not offset by the increased frequency of large-scale precipitation. Therefore, for aerosol simulations in high-resolution GCMs using scale-aware convection schemes, subgrid convection and large-scale condensation should be coordinated to assure light rain stays the same.

Orographic resolution driving the improvements associated with horizontal resolution increase in the Northern Hemisphere winter mid-latitudes

Abstract. In recent years much attention has been devoted to the investigation of the impact of increasing the horizontal resolution of global climate models. In the present work, a set of atmosphere-only idealized sensitivity simulations with EC-Earth3 has been designed to disentangle the relative roles of increasing the resolution of the resolved orography and of the atmospheric grid. Focusing on the Northern Hemisphere winter, it is shown that if the grid is refined while keeping the resolved orography unchanged, model biases are reduced only on some specific occasions. Conversely, increasing the resolved (or mean) orography is found to clearly reduce several important systematic model errors, including synoptic transient eddies, the North Atlantic jet stream variability, and atmospheric blocking frequency and duration. From an analysis of the radiation budget it is concluded that the large changes in radiative fluxes caused by the resolution increase – something commonly observed in climate models – have a relevant impact on the atmospheric circulation, partially offsetting the benefits obtained from the increase in orographic resolution. These findings point to the necessity of always tuning climate models to fully exploit the benefits of high horizontal resolution.

Seamless Projections of Global Storm Surge and Ocean Waves Under a Warming Climate

AbstractFor coastal adaptation purposes, it is important to estimate the climate related changes in extreme sea levels due to storm surges and ocean waves, in addition to mean sea level rise. This study provides the first consistent and continuous estimation of projected changes in global storm surges and ocean waves from the past to the warmer future, based on an extremely high resolution global climate model. The spatial pattern in the trend of annual maximum sea surface heights and wave heights is predominantly driven by changes in tropical cyclone (TC) frequency. In the western North Pacific, future TC frequencies are projected to decrease, and the annual maximum sea surface heights and wave heights show decreasing trends (−20 cm/century and −200 cm/century). Although highly intense tropical cyclones are enhanced in the warmer climate, highly extreme storm surges and wave heights do not necessarily increase due to the large natural variability.

Investigating the response of the Botswana High to El Niño Southern Oscillation using a variable resolution global climate model

The Botswana High is an important component of the regional atmospheric circulation during austral spring, summer and autumn. While the high tends to be stronger during El Niño and weaker during La Niña, its direct response to El Niño Southern Oscillation (ENSO) remains unknown. To that end, a variable resolution global climate model (Model Prediction Across Scales version 7, hereafter MPAS) is applied with relatively high resolution (48-km grid spacing) over Southern Africa and a coarser resolution (240-km grid spacing) over the rest of the globe for the study period 1980–2010. The first model experiment uses observed sea surface temperatures (SSTs) everywhere during the study period, while the second experiment uses observed SSTs everywhere except over the Pacific Ocean, where monthly climatological SSTs are imposed. The model results were validated against satellite data (Global Precipitation Climatology Project, GPCP) and reanalysis datasets (Climate Forecast System Reanalysis, CFSR; European Centre for Medium-Range Weather Forecasts version 5, ERA5). The results of the study show that the MPAS model gives a credible simulation of the temporal variability of the Botswana High, the seasonal rainfall and 500-hPa geopotential heights over Southern Africa. In the absence of ENSO forcing, the amplitude of the Botswana High variability reduces but the signal of the variability remains. Hence, this study shows that ENSO enhances the strength of the Botswana High but does not aid in the formation of the high.

On the dependency of GCM-based regional surface climate change projections on model biases, resolution and climate sensitivity

We investigate the dependency of projected regional changes in surface air temperature (SAT) and precipitation on the model biases, resolution and global temperature sensitivity in two global climate model (GCM) ensembles. End of twenty-first century changes under high end scenarios normalized in units of per degree of global warming (PDGW) are examined for CMIP5 (RCP8.5) and CMIP6 (SSP5-8.5) ensembles of comparable size over 26 sub-continental scale regions, for December–January–February (DJF) and June–July–August (JJA). A brief analysis is also carried out for the scenario SSP3-7.0, which shows results essentially in line with the SSP5-8.5 ones. We find that the average regional change patterns are very similar between the CMIP5 and CMIP6 ensembles, both for SAT and precipitation, with spatial correlations exceeding 0.84. Also similar are the regional bias patterns over most regions analyzed, suggesting that these two generations of models still share some common systematic errors. A statistically significant relationship between projected regional changes and biases is found in 27% of regional cases for both SAT and precipitation; between regional changes and model resolution in 2% of cases for SAT and 12% of cases for precipitation; and between regional changes and global temperature sensitivity in 19% of cases for SAT and 14% of cases for precipitation. Therefore, we assess that the GCM resolution does not appear to be a significant factor in affecting the sub-continental scale projected changes, at least for the resolution range in the CMIP5 and CMIP6 models, while global temperature sensitivity and especially model biases play a more important role. These dependencies are not always consistent between the CMIP5 and CMIP6 ensembles. Overall, in our assessment the CMIP6 ensemble does not appear to provide substantially different, and presumably improved, regional surface climate change information compared to CMIP5 despite the use of more comprehensive models and somewhat higher resolution.

Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.