High-resolution Climate Change Projections Research Articles

Dynamical downscaling CMIP6 models over New Zealand: added value of climatology and extremes

AbstractDynamical downscaling provides physics-based high-resolution climate change projections across regional and local scales. This is particularly important for island nations characterized by complex terrain, where the coarse resolution of global climate model (GCM) output often prohibits direct use. One of the main motivations for dynamical downscaling is to reduce biases relative to the host GCM at the local scale, which can be quantified through assessing ‘added value’. However, added value from downscaling is not guaranteed; quantifying this can help users make informed decisions about how best to use available climate projection data. Here we describe the experiment design of the updated national climate projections for New Zealand based on dynamical downscaling. The global non-hydrostatic Conformal Cubic Atmospheric Model (CCAM) is primarily used for downscaling, with a global stretched grid targeting high resolution over New Zealand (12-km) and the wider South Pacific region (12–35-km). Focusing on the historical simulations, we assess added value for a range of metrics, climatological fields, extreme indices, and tropical cyclones. The main strengths of the downscaling include generally large improvements relative to the host GCM for temperature and orographic precipitation. Inter-annual variability in temperature is well captured across New Zealand, and several temperature and precipitation-based extreme indices show large improvements. The representation of tropical cyclones reaching at least category 2 intensity is generally improved relative to the large consistent under-representation in the host GCMs. The remaining biases are explored and discussed forming the basis for ongoing bias-correction work.

High resolution climate change observations and projections for the evaluation of heat-related extremes

The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme humid heat and drought conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product, the Climate Hazards InfraRed Precipitation with Stations precipitation product, and ERA5-derived relative humidity form the basis of the 1983–2016 historical record, from which daily Vapor Pressure Deficits (VPD) and maximum Wet Bulb Globe Temperatures (WBGTmax) were derived. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 projections of daily precipitation, temperature, relative humidity, and derived VPD and WBGTmax. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.

Biological sensitivities to high-resolution climate change projections in the California current marine ecosystem.

The California Current Marine Ecosystem is a highly productive system that exhibits strong natural variability and vulnerability to anthropogenic climate trends. Relating projections of ocean change to biological sensitivities requires detailed synthesis of experimental results. Here, we combine measured biological sensitivities with high‐resolution climate projections of key variables (temperature, oxygen, and pCO2) to identify the direction, magnitude, and spatial distribution of organism‐scale vulnerabilities to multiple axes of projected ocean change. Among 12 selected species of cultural and economic importance, we find that all are sensitive to projected changes in ocean conditions through responses that affect individual performance or population processes. Response indices were largest in the northern region and inner shelf. While performance traits generally increased with projected changes, fitness traits generally decreased, indicating that concurrent stresses can lead to fitness loss. For two species, combining sensitivities to temperature and oxygen changes through the Metabolic Index shows how aerobic habitat availability could be compressed under future conditions. Our results suggest substantial and specific ecological susceptibility in the next 80 years, including potential regional loss of canopy‐forming kelp, changes in nearshore food webs caused by declining rates of survival among red urchins, Dungeness crab, and razor clams, and loss of aerobic habitat for anchovy and pink shrimp. We also highlight fillable gaps in knowledge, including specific physiological responses to stressors, variation in responses across life stages, and responses to multistressor combinations. These findings strengthen the case for filling information gaps with experiments focused on fitness‐related responses and those that can be used to parameterize integrative physiological models, and suggest that the CCME is susceptible to substantial changes to ecosystem structure and function within this century.

Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections

Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 1/4 degrees (25 km) over one of the most climatically diversified countries, India. We showcase significant improvement gain against two popular state-of-the-art baselines with a better ability to predict statistics of extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.

Heat stress risk and vulnerability under climate change in Durban metropolitan, South Africa—identifying urban planning priorities for adaptation

There is an urgent need to map the geographic location of climate change risks and vulnerability, especially for cities in sub-Saharan Africa, which are experiencing the greatest urban development challenges and vulnerability to climate change impacts. The aim of this study is to investigate current and projected future heat risk, expressed as a heat stress exposure index using high-resolution climate change projections, and a social vulnerability index, to identify areas of potential future heat stress risk in the Durban (eThekwini) metropolitan area, South Africa. Additionally, this is the first study to use high-resolution downscaled climate change projections under Representative Concentration (RCP) 8.5, to construct the heat exposure index using apparent temperature and increases in minimum temperature and a social vulnerability index, using demographic and socio-economic census and land use data to, derived from principal component analysis (PCA) to spatially characterize heat stress within a South African city. Results show that while heat stress is not a current concern, it is projected to increase and become a future concern, mainly as a function of social vulnerability due to household demographic and infrastructural characteristics, and will be experienced in both the rural and inner-city areas of the metro. This study contributes a heat risk framework to identify locations for specific research and adaptation activities on heat stress risk and for urban planning in sub-Saharan African cities, which are characterized by both rural and urban contexts, to address climate change adaptation targeting and priority setting.

High resolution climate change projections for the Pyrenees region

Abstract. The Pyrenees, located in the transition zone of Atlantic and Mediterranean climates, constitute a paradigmatic example of mountains undergoing rapid changes in environmental conditions, with potential impact on the availability of water resources, mainly for downstream populations. High-resolution probabilistic climate change projections for precipitation and temperature are a crucial element for stakeholders to make well-informed decisions on adaptation to new climate conditions. In this line, we have generated high–resolution climate projections for 21st century by applying two statistical downscaling methods (regression for max and min temperatures, and analogue for precipitation) over the Pyrenees region in the frame of the CLIMPY project over a new high-resolution (5 km × 5 km) observational grid using 24 climate models from CMIP5. The application of statistical downscaling to such a high resolution observational grid instead of station data partially circumvent the problems associated to the non-uniform distribution of observational in situ data. This new high resolution projections database based on statistical algorithms complements the widely used EUROCORDEX data based on dynamical downscaling and allows to identify features that are dependent on the particular downscaling method. In our analysis, we not only focus on maximum and minimum temperatures and precipitation changes but also on changes in some relevant extreme indexes, being 1986–2005 the reference period. Although climate models predict a general increase in temperature extremes for the end of the 21st century, the exact spatial distribution of changes in temperature and much more in precipitation remains uncertain as they are strongly model dependent. Besides, for precipitation, the uncertainty associated to models can mask – depending on the zones- the signal of change. However, the large number of downscaled models and the high resolution of the used grid allow us to provide differential information at least at massif level. The impact of the RCP becomes significant for the second half of the 21st century, with changes – differentiated by massifs – of extreme temperatures and analysed associated extreme indexes for RCP8.5 at the end of the century.

The Ontario Climate Data Portal, a user-friendly portal of Ontario-specific climate projections

An easily accessible climate data portal, http://yorku.ca/ocdp, was developed and officially launched in 2018 to disseminate a super ensemble of high-resolution regional climate change projections for the province of Ontario, Canada. The spatial resolution is ~10 km × ~10 km and temporal resolution is one day, UTC. The data covers 120 years from 1981 to 2100. This user-friendly portal provides users with thousands of static and interactive maps, decadal variation trend lines, summary tables, reports and terabytes of bias-corrected downscaled data. The data portal was generated with an emphasis on interactive visualization of climate change information for researchers and the public to understand to what extent climate could change locally under different emission scenarios in the future. This paper presents an introduction to the portal structure and functions, the large extent of the datasets available and the data development methodology.

PROJETA platform: accessing high resolution climate change projections over Central and South America using the Eta model

The search for data on climate change by researchers, government agencies or private companies is a recurrent demand. However, it is hampered by the means of access to this type of information, mainly due to the complexity of extracting, reformatting, and making this data available, which can exceed terabytes in size. The PROJETA platform aims to automate the process of extracting and making available the dataset of global climate change projections downscaling to 20 km over South America generated by the model Eta at CPTEC/INPE. The data request, processing, and conversion process, which used to be done manually and in a oneto-one data delivery basis. The objective of this work is to describe the methodology used to create the platform PROJETA and the information made available. It is a service that allows access to a broad set of different climatic variables. This dataset is available to different users via the Web or API, in a flexible way in terms of data format and data volume. In addition, it integrates technologies that allow the access to the database in an efficient and easy way for use in studies of impact, vulnerability, and adaptation to climate change in various socio-economic sectors.

Sensitivity of the Amazon biome to high resolution climate change projections

ABSTRACT: Despite the reduction in deforestation rate in recent years, the impact of global warming by itself can cause changes in vegetation cover. The objective of this work was to investigate the possible changes on the major Brazilian biome, the Amazon Rainforest, under different climate change scenarios. The dynamic vegetation models may simulate changes in vegetation distribution and the biogeochemical processes due to climate change. Initially, the Inland dynamic vegetation model was forced with initial and boundary conditions provided by CFSR and the Eta regional climate model driven by the historical simulation of HadGEM2-ES. These simulations were validated using the Santarém tower data. In the second part, we assess the impact of a future climate change on the Amazon biome by applying the Inland model forced with regional climate change projections. The projections show that some areas of rainforest in the Amazon region are replaced by deciduous forest type and grassland in RCP4.5 scenario and only by grassland in RCP8.5 scenario at the end of this century. The model indicates a reduction of approximately 9% in the area of tropical forest in RCP4.5 scenario and a further reduction in the RCP8.5 scenario of about 50% in the eastern region of Amazon. Although the increase of CO2 atmospheric concentration may favour the growth of trees, the projections of Eta-HadGEM2-ES show increase of temperature and reduction of rainfall in the Amazon region, which caused the forest degradation in these simulations.

A regional ocean–atmosphere coupled model developed for CORDEX East Asia: assessment of Asian summer monsoon simulation

In this study, a developed regional ocean–atmosphere coupled model FROALS was applied to the CORDEX East Asia domain. The performance of FROALS in the simulation of Asian summer monsoon during 1989–2010 was assessed using the metrics developed by the CLIVAR Asian–Australian Monsoon Panel Diagnostics Task Team. The results indicated that FROALS exhibited good performance in simulating Asian summer monsoon climatology. The simulated JJA mean SST biases were weaker than those of the CMIP5 multi-model ensemble mean (MMEM). The skill of FROALS approached that of CMIP5 MMEM in terms of the annual cycle of Asian summer monsoon. The simulated monsoon duration matched the observed counterpart well (with a spatial pattern correlation coefficient of 0.59). Some biases of CMIP5 MMEM were also found in FROALS, highlighting the importance of local forcing and model physics within the Asian monsoon domain. Corresponding to a strong East Asian summer monsoon, an anomalous anticyclone was found over western North Pacific in both observation and simulation. However, the simulated strength was weaker than the observed due to the responses to incorrect sea surface anomalies over the key regions. The model also accurately captured the spatial pattern of the intraseasonal variability variance and the extreme climate indices of Asian summer monsoons, although with larger amplitude. The results suggest that FROALS could be used as a dynamical downscaling tool nested within the global climate model with coarse resolution to develop high-resolution regional climate change projections over the CORDEX East Asia domain.

EURO-CORDEX: new high-resolution climate change projections for European impact research

A new high-resolution regional climate change ensemble has been established for Europe within the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) initiative. The first set of simulations with a horizontal resolution of 12.5 km was completed for the new emission scenarios RCP4.5 and RCP8.5 with more simulations expected to follow. The aim of this paper is to present this data set to the different communities active in regional climate modelling, impact assessment and adaptation. The EURO-CORDEX ensemble results have been compared to the SRES A1B simulation results achieved within the ENSEMBLES project. The large-scale patterns of changes in mean temperature and precipitation are similar in all three scenarios, but they differ in regional details, which can partly be related to the higher resolution in EURO-CORDEX. The results strengthen those obtained in ENSEMBLES, but need further investigations. The analysis of impact indices shows that for RCP8.5, there is a substantially larger change projected for temperature-based indices than for RCP4.5. The difference is less pronounced for precipitation-based indices. Two effects of the increased resolution can be regarded as an added value of regional climate simulations. Regional climate model simulations provide higher daily precipitation intensities, which are completely missing in the global climate model simulations, and they provide a significantly different climate change of daily precipitation intensities resulting in a smoother shift from weak to moderate and high intensities.

Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal

A statistical bias correction technique is applied to twelve high‐resolution climate change simulations of temperature and precipitation over Europe, under the SRES A1B scenario, produced for the EU project ENSEMBLES. The bias correction technique is based on a transfer function, estimated on current climate, which affects the whole Probability Distribution Function (PDF) of variables, and which is assumed constant between the current and future climate. The impact of bias correction on 21st Century projections, their inter‐model variability, and the climate change signal is investigated, with focus being on discrepancies between the original and the bias‐corrected results. As assessing the impact of climate change is significantly dependent on the frequency of extreme events, we also analyze the evolution of the shape of the PDFs, and extreme events indices. Results show that the ensemble mean climate change signal and its inter‐model variability are generally conserved. However, the impact of the bias correction varies amongst regions, seasons and models, and differences up to 0.5°C for the summer temperature climate change signal are found in Southern Europe. Finally the bias correction is found to influence the probability of extreme events like extremely hot or frost days, which also impacts the climate change signal.

Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate

[1] A statistical bias correction technique is applied to a set of high-resolution climate change simulations for Europe from 11 state-of-the-art regional climate models (RCMs) from the project ENSEMBLES. Modeled and observed daily values of mean, minimum and maximum temperature and total precipitation are used to construct transfer functions for the period 1961–1990, which are then applied to the decade 1991–2000, where the results are evaluated. By using a large ensembles of model runs and a long construction period, we take into account both intermodel variability and longer (e.g., decadal) natural climate variability. Results show that the technique performs successfully for all variables over large part of the European continent, for all seasons. In particular, the probability distribution functions (PDFs) of both temperature and precipitation are greatly improved, especially in the tails, i.e., increasing the capability of reproducing extreme events. When the statistics of bias-corrected results are ensemble averaged, the result is very close to the observed ones. The bias correction technique is also able to improve statistics that depend strongly on the temporal sequence of the original field, such as the number of consecutive dry days and the total amount of precipitation in consecutive heavy precipitation episodes, which are quantities that may have a large influence on, e.g., hydrological or crop impact models. Bias-corrected projections of RCMs are hence found to be potentially useful for the assessment of impacts of climate change over Europe.

Risk assessment of dengue virus amplification in Europe based on spatio-temporal high resolution climate change projections

During the last decades dengue incidences are emerging significantly around the globe. Currently, about one fifth of the human population lives in dengue risk zones, which are mainly located in (sub-) tropical regions of Southeast Asia and the Western Pacific. Dengue infections in European population mainly referred to returning travellers from tropical endemic regions. Nevertheless, vector establishment in Europe already took place and therefore the risk increases. Currendy, autochthonous cases of dengue fever have been reported in Europe. Studies estimating the risk of dengue epidemics regard ing changing climatic conditions in Europe are missing. Therefore, we close this gap by using the temperature constraints for virus amplification within the vector Aedes aegypti from two laboratory experiments. We transfer these findings to the changing European climate based on data provided from a regional climate model (COSMO-CLM; A1B and B1 scenario). Daily mean temperature were averaged for the time-steps 2011—2040, 2041—2070 and 2071-2100 in order to reduce natural variability but rather point out climatic trends for risk assessments. For both scenarios the strongest increase of temperature is projected after mid-century. Results indicate a growing threat of virus amplification in Europe especially towards the end of this century. Larger parts of the Mediterranean will be at risk. The southwest of the Iberian Peninsular appears to be especially threatened. Even in some parts of Central Europe, such as Southwest Germany, dengue virus amplification can no longer be excluded at the end of the century. However, it is unlikely that Aedes aegypti will serve as an efficient vector in Europe. In fact, it is Aedes albopictus that is an invasive species in Europe and potential differences in extrinsic incubation period between Ae. aegypti and Ae. albopictus have to be identified. Policy and public health authorities have to consider these emerging biorisks in order to establish surveillance systems and develop counteraction strategies. Hence, we strongly empha size the need for a growing European awareness in the face of biological hazards that are responding to climatic changes.

The role of the simulation setup in a long-term high-resolution climate change projection for the southern African region

High-resolution regional climate change simulations have proven to offer an added value compared to available global climate model simulations. However, over many regions of the globe, long-term high-resolution climate change projections are rather sparse. We present a transient high-resolution climate change projection with the regional climate model with the regional climate model REMO over the southern African region, following the SRES A1B emission scenario. The simulation was conducted at 18 km grid spacing for the period from 1960 to 2100, making it to the longest available climate change projection at such a high resolution for the region. In the first part of the study, we focus on the impact of the model setup on the simulated rainfall over the southern African region. In the standard setup, we used the output of the global climate model ECHAM5/MPIOM directly to force REMO. This setup led to a very strong wet bias over the region. Changing it to the double-nesting setup significantly reduced this bias, but a substantial wet bias still persists. The remaining bias could partly be attributed to a warm bias in the SST forcing over the southern Atlantic Ocean. Thus, we applied an SST correction based on the anomaly approach to the data, which led to a further improvement of the rainfall simulation. As the SST bias in the southern Atlantic is a common feature of all global climate models assessed by the IPCC, we recommend the chosen model setup, including the SST correction, as general procedure for dynamical downscaling studies over the southern African region. In the second part, we present the projected spatial and temporal changes of temperature and precipitation, including several rainfall characteristics, over the southern African region. Herby we compare the projections of the high-resolution REMO simulation to those of the forcing regional and global models. We generally find that for temperature the magnitude of the projected changes of the regional model only slightly differs from the GCM projection; however, the spatial patterns are much better resolved in the RCM projections. For precipitation, REMO shows a more intense drying toward the end of the twenty-first century than it is simulated by the global model. This can have a major influence when investigating the impacts of future climate change on a regional or even local scale. In combination with the improved spatial patterns, the application of high-resolution climate change information could therefore improve the results of such applications.

Physical and economic consequences of climate change in Europe

Quantitative estimates of the economic damages of climate change usually are based on aggregate relationships linking average temperature change to loss in gross domestic product (GDP). However, there is a clear need for further detail in the regional and sectoral dimensions of impact assessments to design and prioritize adaptation strategies. New developments in regional climate modeling and physical-impact modeling in Europe allow a better exploration of those dimensions. This article quantifies the potential consequences of climate change in Europe in four market impact categories (agriculture, river floods, coastal areas, and tourism) and one nonmarket impact (human health). The methodology integrates a set of coherent, high-resolution climate change projections and physical models into an economic modeling framework. We find that if the climate of the 2080s were to occur today, the annual loss in household welfare in the European Union (EU) resulting from the four market impacts would range between 0.2-1%. If the welfare loss is assumed to be constant over time, climate change may halve the EU's annual welfare growth. Scenarios with warmer temperatures and a higher rise in sea level result in more severe economic damage. However, the results show that there are large variations across European regions. Southern Europe, the British Isles, and Central Europe North appear most sensitive to climate change. Northern Europe, on the other hand, is the only region with net economic benefits, driven mainly by the positive effects on agriculture. Coastal systems, agriculture, and river flooding are the most important of the four market impacts assessed.

Protecting health from climate change

Protecting health from climate change