Regional Impact Studies Research Articles

The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections

The Selection of Global Climate Models for Regional Impact Studies Should Consider Information from Historical Simulations and Future Projections

Anthropogenic influence on extreme temperature and precipitation in Central Asia

We investigate the contribution of anthropogenic forcing to the extreme temperature and precipitation events in Central Asia (CA) during the last 60 years. We bias-adjust and downscale two Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ensemble outputs, with natural (labelled as hist-nat, driven only by solar and volcanic forcing) and natural plus anthropogenic forcing (labelled as hist, driven by all-forcings), to 0.25^{circ } times 0.25 ^{circ } spatial resolution. Each ensemble contains six models from ISIMIP, based on the Coupled Model Inter-comparison Project phase 6 (CMIP6). The presented downscaling methodology is necessary to create a reliable climate state for regional climate impact studies. Our analysis shows a higher risk of extreme heat events (factor 4 in signal-to-noise ratio) over large parts of CA due to anthropogenic influence. Furthermore, a higher likelihood of extreme precipitation over CA, especially over Kyrgyzstan and Tajikistan, can be attributed to anthropogenic forcing (over 100% changes in intensity and 20% in frequency). Given that these regions show a high risk of rainfall-triggered landslides and floods during historical times, we report that human-induced climate warming can contribute to extreme precipitation events over vulnerable areas of CA. Our high-resolution data set can be used in impact studies focusing on the attribution of extreme events in CA and is freely available to the scientific community.

Gridded value-added of primary, secondary and tertiary industries in China under Shard Socioeconomic Pathways

Gridded distribution of future economy plays an important role in climate change impact assessment. The trend of the output values of different industries is crucial for a variety of planning and design processes. Under the Shared Socioeconomic Pathways (SSPs) global framework, the multidimensional model and Cobb-Douglas production model with localized population and economic parameters are used to develop the annual provincial population and value-added of primary, secondary and tertiary industries in China from 2020 to 2100. The most recently implemented fertility-promoting and industrial planning policies in China are considered in our projections. We build multiple models to evaluate the impact of different types of land use on the value-added of primary, secondary and tertiary industries and then gridded the projected value-added to a 5′ × 5′ resolution, based on recorded county-level economic statistics and gridded land use. The reliability of estimations is verified against 2011–2019 statistical data and multiple published datasets. The high-resolution economic dataset is expected to contribute greatly to national and regional climate change impact, adaptation, and vulnerability studies.

Bias‐adjustment of high‐resolution temperature CORDEX data over the Carpathian region: Expected changes including the number of summer and frost days

AbstractClimate model simulations' outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. One possible solution for reducing uncertainty and eliminating possible systematic errors within climate projections is the bias‐adjustment of the raw climate model data. We used the quantile mapping method for bias‐adjustment of a mini ensemble consisting of 8‐member high‐resolution (0.11°) regional climate model simulations provided by the CORDEX community. As the method requires a reliable observational dataset serving as reference data, we used the quality controlled and homogenized observational dataset: CARPATCLIM. Quantile mapping bias‐adjustment technique was applied on the following variables: daily mean, minimum and maximum temperature. We analysed changes in mean temperature characteristics and of climate indices for future periods of 2021–2050 and 2070–2099 with respect to the reference period 1976–2005 for the Carpathian Region. The selected climate indices are based on minimum (frost days, FD) and maximum daily temperature data (summer days, SU). Our bias‐adjusted RCM results suggest a similar degree of mean temperature change as the raw RCMs' data. Bias‐adjusted and raw RCM data project a remarkable annual temperature increase on average under the RCP8.5 scenario (1.4°C and 3.9°C by 2021–2050 and 2070–2099, respectively). The highest temperature increase is likely to occur in summer: it is 4.3°C on average by the end of the 21st century. More pronounced differences were found for the projected changes of the number of summer and frost days based on the bias‐adjusted and the raw RCM data. Our results draw attention to the fact that bias‐adjusted RCM data are crucial for the provision of regional climate change impact and adaptation studies for the Carpathian Region.

Dynamical Downscaling of Near‐Term (2026–2035) Climate Variability and Change for the Main Hawaiian Islands

AbstractNatural climate variability poses a limit to the confidence in regional climate projections, particularly for the near‐term. Therefore, the next decades are of importance for regional impact studies and the development of regional adaptation plans. This article presents results from an ensemble of regional climate model simulations (years 1996–2005 and 2026–2035) that are used to examine the effects of both anthropogenic forcing and natural variability associated with the Pacific Decadal Oscillation (PDO) on near‐term climate projections for the Hawaiian Islands. The Community Earth System Model Large Ensemble (CESM‐LE) is used in conjunction with the Weather Research and Forecasting (WRF) model for downscaling. The climate responses to the PDO and anthropogenic forcing are isolated and analyzed separately. In response to anthropogenic forcing, significant increases in surface air temperature, of ∼0.8 K, are projected at low elevations. Stronger warming, of up to 1.3 K, is seen at higher elevation areas. Future climate simulations show significant increases in wet season rainfall, of ∼10%–20%, along the windward slopes of Big Island and Maui. Rainfall patterns during the positive PDO phase are projected to reverse in sign, leading to drier conditions, by ∼10%–30%, at many locations. Future climate simulations show daily rainfall extremes will increase, by up to ∼10%–15%, at many locations. Daily temperature extremes are also projected to increase significantly, by up to 1.4 K. Overall, results indicate that natural variability will continue to contribute to uncertainty in near‐term rainfall projections for the Hawaiian Islands, masking the forced signal.

One simulation, different conclusions—the baseline period makes the difference!

The choice of the baseline period, intentionally chosen or not, as a reference for assessing future changes of any projected variable can play an important role for the resulting statement. In regional climate impact studies, well-established or arbitrarily chosen baselines are often used without being questioned. Here we investigated the effects of different baseline periods on the interpretation of discharge simulations from eight river basins in the period 1960–2099. The simulations were forced by four bias-adjusted and downscaled Global Climate Modelsunder two radiative forcing scenarios (RCP 2.6 and RCP 8.5). To systematically evaluate how far the choice of different baselines impacts the simulation results, we developed a similarity index that compares two time series of projected changes. The results show that 25% of the analyzed simulations are sensitive to the choice of the baseline period under RCP 2.6 and 32% under RCP 8.5. In extreme cases, change signals of two time series show opposite trends. This has serious consequences for key messages drawn from a basin-scale climate impact study. To address this problem, an algorithm was developed to identify flexible baseline periods for each simulation individually, which better represent the statistical properties of a given historical period.

Assessing Climate Change Impacts and Adaptation Options for Farm Performance Using Bio-Economic Models in Southwestern France

Regional impact studies are needed to explore possible adaptation options to climate change. We estimated impacts and adaptation options for future scenarios that feature different assumptions regarding climate, cropping pattern and access to irrigation with two bio-economic farm models. Farm profit, soil organic matter balance and labor input are used as indicators of farm performance. The difference between the baseline and the alternative configurations computed by models is referred as adaptation potential, indicative of the adaptation options including the corresponding changes in cropping patterns. Our results show that as long as there is sufficient access to irrigation water, there is little incentive to change current practices, as farming is at the economic optimum, has a positive soil organic matter balance and labor requirements can be met. Conversely, if irrigation is no longer possible, drastic impacts occur, causing a need to sustainably adjust on-going farm practices. Adaptation through changed crop selection reduced losses to some extent. We conclude that the use of bio-economic models can assist in evaluating the qualitative findings of participatory studies by quantitatively assessing possible climate change impacts and adaptation measures. Strong impacts of climate change, however, cannot be offset by changes in cropping patterns and need further adaptation measures.

Complete spatial model calibration

Computer simulation models are central to environmental science. These mathematical models are used to understand complex weather and climate patterns and to predict the climate’s response to different forcings. Climate models are of course not perfect reflections of reality, and so comparison with observed data is needed to quantify and to correct for biases and other deficiencies. We propose a new method to calibrate model output using observed data. Our approach not only matches the marginal distributions of the model output and gridded observed data, but it simultaneously postprocesses the model output to have the same spatial correlation as the observed data. This comprehensive calibration method permits realistic spatial simulations for regional impact studies. We apply the proposed method to global climate model output in North America and show that it successfully calibrates the model output for temperature and precipitation.

Improving the Reliability and Added Value of Dynamical Downscaling via Correction of Large‐Scale Errors: A Norwegian Perspective

AbstractHeavy precipitation and associated flooding are major concerns for western Norway under both present climate conditions and projected future scenarios. In winter, these events are mostly caused by North Atlantic low‐pressure systems. However, the storm track in this region is systematically biased in most global climate models. Further, these models are unable to capture the interactions between synoptic systems and the complex topography due to their coarse resolution, which contributes to poor representation of precipitation. In this study we employed a correction technique to remove the mean biases from multiple variables in a global Earth system model (Norwegian Earth System Model), and we subsequently dynamically downscaled a 38‐year period. The effect of the bias correction was a more realistic storm track and an improved precipitation distribution in southern Norway. Our results indicate that it is possible to enhance the reliability of regional climate simulations with positive implications for regional climate projections and climate change impact studies.

Climate change projections from Coupled Model Intercomparison Project phase 5 multi‐model weighted ensembles for Mexico, the North American monsoon, and the mid‐summer drought region

Two versions of the “reliability ensemble averaging” (REA and REA Xu) method and an unweighted mean were used to generate multi‐model ensembles of 14 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for a baseline (1971–2000) period and future scenarios (RCP4.5 and RCP8.5) for the 21st century. To test the consistency of the REA ensembles at different scales, one ensemble was area‐averaged at regional scale and two were obtained at 50‐km gridpoints. Climatic metrics of temperature and precipitation during the baseline period served to evaluate the performance of the GCMs and the ensembles in the North American monsoon (NAM) and the mid‐summer drought (MSD) regions. The metrics of the three REA ensembles are very similar among them in each region and show a better performance than the unweighted multi‐model ensemble (U‐MME). The majority of the GCMs overestimate winter precipitation in the NAM and fail to retract the end of the monsoon in autumn; REA ensembles reduce this overestimation. All ensembles capture well the MSD's double peak of rainfall, but underestimate summer precipitation. According to all ensembles, temperature increases of 1.5–2 °C in the two regions may be reached between 2035 and 2055 relative to the baseline, and by 2070–2099 temperature (precipitation) in Mexico may increase (decrease) between 2° C (5%) and 5.8 °C (10%) in the RCP4.5 and RCP8.5 scenarios, respectively. Annual changes of precipitation show a north (positive) and south of 35°N (negative) pattern. The largest impacts are expected during summer with a possible decrease of ~13% (up to −1.5 mm/day), especially in southern Mexico, Central America, and the Caribbean, while autumn precipitation may slightly increase. Future projections from the REA Xu (by gridpoint) ensemble show spatial patterns similar to the U‐MME, but with more regional detail, which could be an added value for regional climate impact studies.

Climate change impacts on the energy system: a review of trends and gaps

Major transformation of the global energy system is required for climate change mitigation. However, energy demand patterns and supply systems are themselves subject to climate change impacts. These impacts will variously help and hinder mitigation and adaptation efforts, so it is vital they are well understood and incorporated into models used to study energy system decarbonisation pathways. To assess the current state of understanding of this topic and identify research priorities, this paper critically reviews the literature on the impacts of climate change on the energy supply system, summarising the regional coverage of studies, trends in their results and sources of disagreement. We then examine the ways in which these impacts have been represented in integrated assessment models of the electricity or energy system.Studies tend to agree broadly on impacts for wind, solar and thermal power stations. Projections for impacts on hydropower and bioenergy resources are more varied. Key uncertainties and gaps remain due to the variation between climate projections, modelling limitations and the regional bias of research interests. Priorities for future research include the following: further regional impact studies for developing countries; studies examining impacts of the changing variability of renewable resources, extreme weather events and combined hazards; inclusion of multiple climate feedback mechanisms in IAMs, accounting for adaptation options and climate model uncertainty.

Climate change, regulatory policies and regional cooperation in South Asia

Purpose With climate change and environmental degradation being major issues in the world today, it is imperative for governments within a regional setting to collaborate on initiatives, harmonize their policies and develop strategies to counter threats. In South Asia, several attempts have been made to create a common framework for action in implementing synchronized policies. However, both political and technical deterrents have thwarted moves to accommodate priorities and interests of collaborating states. The purpose of this paper is to assess these issues and existing policies/strategies in selected South Asian countries and evaluate integrated plans of action based on collaborative partnerships. Design/methodology/approach Using a broad exploratory and interpretive approach, this paper evaluates how harmonization of environmental principles and synergies among countries can help reduce the effect of climate change and environmental hazards. Based on a review of ideas and concepts as well as both primary and secondary sources, including official records, legislation, inter-state and regional agreements, evaluation reports, impact studies (social, economic and ecological), and commentaries, it highlights several initiatives and processes geared to creating environmental protection standards and practices for the South Asian region. Findings Climate change has resulted in devastating impacts on people. It contributed to the proliferation of climate refugees and high incidence of poverty in South Asia. The region faces both political and technical obstacles in developing a sustainable approach to combat climate change. This is exacerbated by non-availability of information as well as reluctance to acknowledge the problem by key actors. The best strategy will be to integrate policies and regulations in the various countries of the region to develop strategic plans. The approach of prevention and protection should replace the existing emphasis on relief and rehabilitation. Originality/value The paper provides a critical overview of the climatic and environmental problems encountered in the South Asian region and provides pointers to resolving shared problems through the use of policy instruments for regulating the problems within the gamut of regional environmental governance. It attempts to identify solutions to offset regulatory and institutional barriers in achieving preferred results by emphasizing the need for redesigning regulatory structures and policy approaches for ecological well-being.

A new scheme for multivariate, multisite weather generator with inter-variable, inter-site dependence and inter-annual variability based on empirical copula approach

Weather generators (WGs) are often used to develop an ensemble of plausible climate scenarios across multiple spatial and temporal scales for vulnerability assessment and impact studies. Most of the conventional WGs are single-site models, which neglect the spatial dependence in the simulated meteorological field, are improper for regional impact studies. In recent several decades, efforts have been devoted to develop multivariate, multisite weather generators (MMWGs), which are able to reproduce the inter-variable and inter-site dependencies as well as the temporal structures observed in the historical records. Though several improvements have been achieved, the existed MMWGs are either conceptually complex or computationally expensive, and have difficulty in preserving the entire desired attributes. This study proposes a new two-stage scheme for constructing a MMWG. At the first stage, the meteorological series are simulated from a single-site multivariate weather generator (SMWG), which preserves the marginal distributional attributes for each meteorological variable. For the second stage, the inter-variable and inter-site dependencies as well as the temporal structures are reproduced using the Empirical Copula (EC) approach. An application of the proposed MMWG is presented for the Upper Thames River Catchment, Canada, and the performance is evaluated and compared with another two-stage MMWG based on the Iman shuffle approach. Results show that the proposed MMWG not only preserves the marginal distributional attributes, but also restores the inter-variable and inter-site dependencies, the temporal persistence, and the inter-annual variability almost perfectly. The performance of the proposed MMWG outperforms the one based on the Iman shuffle approach in terms of reconstruction of temporal persistence and inter-annual variability, as well as the reproduction of multivariate dependencies. Being conceptually simple and computationally inexpensive, the proposed scheme based on the EC approach is well suited for developing ensemble-based climate scenarios for vulnerability assessment and impact studies.

Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis–Kuo–Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

Abstract. Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970–1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q20 and Q80. On the other hand, bias-corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and characteristics and the effect of bias correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.

区域生态效应研究中人类活动强度定量化评价

区域生态效应研究中人类活动强度定量化评价

Storylines of Atmospheric Circulation Change for European Regional Climate Impact Assessment

There is increasing interest in understanding the regional impacts of different global warming targets. However, several regional climate impacts depend on the atmospheric circulation, whose response to climate change remains substantially uncertain and not interpretable in a probabilistic sense in multimodel ensemble projections. To account for these uncertainties, a novel approach where regional climate change is analyzed as a function of carbon emissions conditional on plausible storylines of atmospheric circulation change is here presented and applied to the CMIP5 models’ future projections. The different storylines are determined based on the response in three remote drivers of regional circulation: the tropical and polar amplification of global warming and changes in stratospheric vortex strength. As an illustration of this approach, it is shown that the severity of the projected wintertime Mediterranean precipitation decline and central European windiness increase strongly depends on the storyline of circulation change. For a given magnitude of global warming, the highest impact storyline for these aspects of European climate is found for a high tropical amplification and a strengthening of the vortex. The difference in the precipitation and wind responses between the storylines is substantial and equivalent to the contribution from several degrees of global warming. Improving the understanding of the remote driver responses is thus needed to better bound the projected regional impacts in the European sector. The value of these storylines to represent the uncertainty in regional climate projections and to inform the selection of CMIP5 models in regional climate impact studies is discussed.

Evaluation of an ensemble of regional hydrological models in 12 large-scale river basins worldwide

In regional climate impact studies, good performance of regional models under present/historical climate conditions is a prerequisite for reliable future projections. This study aims to investigate the overall performance of 9 hydrological models for 12 large-scale river basins worldwide driven by the reanalysis climate data from the Water and Global Change (WATCH) project. The results serve as the basis of the application of regional hydrological models for climate impact assessment within the second phase of the Inter-Sectoral Impact Model Intercomparison project (ISI-MIP2). The simulated discharges by each individual hydrological model, as well as the ensemble mean and median series were compared against the observed discharges for the period 1971–2001. In addition to a visual comparison, 12 statistical criteria were selected to assess the fidelity of model simulations for monthly hydrograph, seasonal dynamics, flow duration curves, extreme floods and low flows. The results show that most regional hydrological models reproduce monthly discharge and seasonal dynamics successfully in all basins except the Darling in Australia. The moderate flow and high flows (0.02–0.1 flow exceedance probabilities) are also captured satisfactory in many cases according to the performance ratings defined in this study. In contrast, the simulation of low flow is problematic for most basins. Overall, the ensemble discharge statistics exhibited good agreement with the observed ones except for extremes in particular basins that need further scrutiny to improve representation of hydrological processes. The performances of both the conceptual and process-based models are comparable in all basins.

Added value of high-resolution regional climate simulations for regional impact studies

We present a comparison of results from the regional climate model COSMO-CLM at a horizontal resolution of 2.8 km with observations and assess the added value of such higher resolution compared to a coarser resolution of 7 km. Such an added value is expected to result from a better resolution of orography and land use as well as from direct simulation of deep convection.The simulations are driven by ERA40 reanalyses for the years 1971 to 2000 and cover southwestern Germany and parts of eastern France. We show that 2.8 km horizontal resolution simulations yield in many, but not all, cases a better agreement of temperature, precipitation, humidity, and global radiation with observation data than simulations with 7 km resolution, especially during the summer half year. At 2.8 km resolution, the model also is well able to capture the mechanisms generating small-scale features, e.g. wind systems. However, the added value is highly dependent on region and altitude. In general, we conclude that high-resolution climate modeling allows studying the impact of climatological parameters on regional scales. It produces encouraging results and has a high potential for applications and direct use in regional and local impact models and impact studies.

Water supply sensitivity to climate change: An R package for implementing reservoir storage analysis in global and regional impact studies

Whilst there are numerous global and regional studies of climate impacts on water resources, relatively few authors have incorporated reservoir storage into their earth system models. Consequently, such studies are unlikely to provide coherent estimates of how changes in climate might affect water supplies globally. This short communication describes an R package, named reservoir, which has been designed for rapid and easy routing of runoff data through storage. The package comprises tools for capacity design, release policy optimisation and performance analysis—allowing users to specify realistic reservoirs and then assess performance in terms of meeting water delivery targets. We demonstrate some of the capabilities of reservoir using 271 runoff records from the Global Runoff Data Centre. The package is freely available through the Comprehensive R Archive Network (CRAN).