Climate Change Impact Studies Research Articles

The impact of lateral boundary forcing in the CORDEX-Africa ensemble over southern Africa

Abstract. The region of southern Africa (SAF) is among the most exposed climate change hotspots and is projected to experience severe impacts across multiple economical and societal sectors. For this reason, producing reliable projections of the expected impacts of climate change is key for local communities. In this work we use an ensemble of 19 regional climate model (RCM) simulations performed in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX) – Africa and a set of 10 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) that were used as the driving GCMs in the RCM simulations. We are concerned about the degree to which RCM simulations are influenced by their driving GCMs, with regards to monthly precipitation climatologies, precipitation biases and precipitation change signal, according to the Representative Concentration Pathway (RCP) 8.5 for the end of the 21st century. We investigate the degree to which RCMs and GCMs are able to reproduce specific climatic features over SAF and over three sub-regions, namely the greater Angola region, the greater Mozambique region, and the greater South Africa region. We identify that during the beginning of the rainy season, when regional processes are largely dependent on the coupling between the surface and the atmosphere, the impact of the driving GCMs on the RCMs is smaller compared to the core of the rainy season, when precipitation is mainly controlled by the large-scale circulation. In addition, we show that RCMs are able to counteract the bias received by their driving GCMs; hence, we claim that the cascade of uncertainty over SAF is not additive, but indeed the RCMs do provide improved precipitation climatologies. The fact that certain bias patterns during the historical period (1985–2005) identified in GCMs are resolved in RCMs provides evidence that RCMs are reliable tools for climate change impact studies over SAF.

Performance Evaluation of an ANN based Bias Correction algorithm in Monthly and Daily Precipitation Time Series of La Farge Station, USA

Understanding the change of future precipitation over long run is highly necessary in climate change impact studies. Mostly, simulated future precipitation series are found to be biased more with the historically observed precipitation series which need to be corrected before use for any impact studies. Many conventional and data-driven methods are available to correct this bias. In this study, to bias correct the monthly and daily precipitation series, Artificial Neural Network based method is applied and compared with the conventional methods. The normalized root mean squared errors obtained for monthly and daily series are 0.786 and 2.55 respectively. It is found that the performance of ANN-based method is poor in daily series and good only in monthly series. The reason for poor performance in daily series is analysed. In addition, the superiority of ANN based method over conventional method is established in monthly precipitation time series.

Climate Change in the Mediterranean Basin (Part II): A Review of Challenges and Uncertainties in Climate Change Modeling and Impact Analyses

The Mediterranean basin is particularly prone to climate change and vulnerable to its impacts. One of the most relevant consequences of climate change, especially for the southern Mediterranean regions, is certainly water scarcity as result of a reduction of surface runoff and groundwater levels. Despite the progress achieved in recent years in the field of climate change and its impact on water resources, results and outcomes should be treated with due caution since any future climate projection and derived implications are inevitably affected by a certain degree of uncertainty arising from each different stage of the entire modeling chain. This work offers a comprehensive overview of recent works on climate change in the Mediterranean basin, mainly focusing on the last ten years of research. Past and future trends on different components of the hydrological balance are discussed in a companion paper (Noto et al. 2022), while the present paper focuses on the problem of water availability and water scarcity. In addition, the work aims to discuss the most relevant sources of uncertainty related to climate change with the aim to gain awareness of climate change impact studies interpretation and reliability.

A comparison of multiple statistically downscaled climate change datasets for the conterminous USA

Climate change projections provided by global climate models (GCM) are generally too coarse for local and regional applications. Local and regional climate change impact studies therefore use downscaled datasets. While there are studies that evaluate downscaling methodologies, there is no study comparing the downscaled datasets that are actually distributed and used in climate change impact studies, and there is no guidance for selecting a published downscaled dataset. We compare five widely used statistically downscaled climate change projection datasets that cover the conterminous USA (CONUS): ClimateNA, LOCA, MACAv2-LIVNEH, MACAv2-METDATA, and NEX-DCP30. All of the datasets are derived from CMIP5 GCMs and are publicly distributed. The five datasets generally have good agreement across CONUS for Representative Concentration Pathways (RCP) 4.5 and 8.5, although the agreement among the datasets vary greatly depending on the GCM, and there are many localized areas of sharp disagreements. Areas of higher dataset disagreement emerge over time, and their importance relative to differences among GCMs is comparable between RCP4.5 and RCP8.5. Dataset disagreement displays distinct regional patterns, with greater disagreement in △Tmax and △Tmin in the interior West and in the North, and disagreement in △P in California and the Southeast. LOCA and ClimateNA are often the outlier dataset, while the seasonal timing of ClimateNA is somewhat shifted from the others. To easily identify regional study areas with high disagreement, we generated maps of dataset disagreement aggregated to states, ecoregions, watersheds, and forests. Climate change assessment studies can use the maps to evaluate and select one or more downscaled datasets for their study area.

Methods of Groundwater Recharge Estimation under Climate Change: A Review

Groundwater resources have deteriorated in many regions as a result of excessive use to satisfy increasing water demands. Furthermore, climate change has an influence on groundwater in terms of quality and quantity. An investigation of climate change impacts on quality and quantity of groundwater is vital for effective planning and sustainable management of groundwater resources. Despite of the importance of climate change impact studies on groundwater resources, climate change impact studies related to surface water resources have attracted more attention from the research community, leading to limited understanding of the groundwater and climate change relationship. In this paper, a systematic review of the latest literature related to the impact of climate change on groundwater recharge was carried out to provide guidance for future studies.

Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1

Abstract. With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited with respect to the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related to a strong human control on freshwater. While many LSMs consider natural processes only, the development of human-related processes, e.g. crop phenology and irrigation in LSMs, is key. In this study, we present the implementation of a new crop phenology and irrigation scheme in the ISBA (interactions between soil–biosphere–atmosphere) LSM. This highly flexible scheme is designed to account for various configurations and can be applied at different spatial scales. For each vegetation type within a model grid cell, three irrigation systems can be used at the same time. A limited number of parameters are used to control (1) the amount of water used for irrigation, (2) irrigation triggering (based on the soil moisture stress), and (3) crop seasonality (emergence and harvesting). A case study is presented over Nebraska (USA). This region is chosen for its high irrigation density and because independent observations of irrigation practices can be used to verify the simulated irrigation amounts. The ISBA simulations with and without the new crop phenology and irrigation scheme are compared to different satellite-based observations. The comparison shows that the irrigation scheme improves the simulated vegetation variables such as leaf area index, gross primary productivity, and land surface temperature. In addition to a better representation of land surface processes, the results point to potential applications of this new version of the ISBA model for water resource monitoring and climate change impact studies.

Impacts of accelerating deployment of offshore windfarms on near-surface climate

The European Union has set the ambitious goal of becoming climate neutral by 2050, which has stimulated renewable energy production and accelerated the deployment of offshore wind energy in the North Sea. Here, a high-resolution regional climate model was used to investigate the impact on the sea surface climate of large-scale offshore wind farms that are proposed for the North Sea. The results show a significant reduction in the air-sea heat fluxes and a local, annual mean net cooling of the lower atmosphere in the wind farm areas down to more than 2.0 Wm−2, due to a decrease in 10 m wind speed and turbulent kinetic energy and an increase in low-level clouds. Mean surface winds decreased by approximately 1 ms−1 downstream of wind farms. Furthermore, an increase of approximately 5% in mean precipitation was found over the wind farm areas. At a seasonal timescale, these differences are higher during winter and autumn than in other seasons. Although the offshore wind farms reduce the heat transport from the ocean to the atmosphere in the region of large wind farms, the atmospheric layers below the hub height show an increase in temperature, which is on the order of up to 10% of the climate change signal at the end of the century, but it is much smaller than the interannual climate variability. In contrast, wind speed changes are larger than projected mean wind speed changes due to climate change. Our results suggest that the impacts of large clustered offshore wind farms should be considered in climate change impact studies. Moreover, the identified offshore windfarm impacts on the sea surface climate and the introduced spatial pattern in atmospheric conditions, in particular the modeled wind speed changes, suggest potential impacts on local ocean dynamics and the structure of the marine ecosystem. This should be considered in future scenarios for the North Sea marine environment and taken into account as a structuring influence in the offshore environment.

Climate change impacts on flood peaks in Britain for a range of global mean surface temperature changes

AbstractAn increase in extreme weather events is leading to increased flood occurrence and risk in many areas. Although climate mitigation strategies are being implemented, it is widely accepted that societies must adapt to climate variability and climate change. Traditional climate change impact studies have used projections for future time‐slices, often for a range of possible emissions scenarios. Recently however, there has been a move to instead consider climate change impacts relative to global mean surface temperature (GMST) change, to try to encourage action to avoid the more severe impacts from higher GMST changes. To support adaptation planning, more localised information on impacts is required. Here, data on the potential range of changes in flood peaks is generated by combining flood response surfaces and the new UK Climate Projections 2018, for every river cell on a 1 km grid across Britain, for GMST changes of 1–4.5°C. The results show significant spatial variation, with impacts typically higher in the west than the east, and generally increasing with GMST change. Some southern regions show flood peak changes accelerating with GMST change. The changes in flood peaks can be translated into changes in flood inundation and associated flood risk under alternative adaptation assumptions.

Study of climate change impact on hydro-climatic extremes in the Hanjiang River basin, China, using CORDEX-EAS data

An integrated assessment modelling framework is introduced to comprehensively evaluate the climate change impact on hydro-climatic extremes over the Hanjiang River basin, China. The framework consists of climate model scenarios, bias correction method, hydrological model, and extreme climate and streamflow indices. The outputs from five CORDEX-EAS regional climate models (RCMs) under RCP 2.6 and 8.5 were firstly bias-corrected for two future windows, i.e. near future (2021-2050) and far future (2051-2080), and then used to drive a hydrological model to estimate the changes of extreme climate and streamflow, which were described by 19 standard hydro-climatic indices. The hydrological model was calibrated using a new software developed based on generalized likelihood uncertainty estimation (GLUE) algorithm under parallel computing environment. From the ensemble median of RCMs, it was found that the study basin is expected to be slightly wetter and significantly hotter in the future, with more intense and frequent precipitation and higher temperature extremes. Particularly, the extreme annual maximum 1-day streamflow index would have an overall increase across the basin, with the most significant increase in the south-western part of the basin. The extreme annual minimum 7-day streamflow index is projected to have a significant increase in the northern, south-western and south-eastern parts of the basin in the near future, while it would show a basin-wide decrease in the far future under RCP 8.5. Our findings helped gain an in-depth insight into the responses of hydro-climatic extremes to climate changes for a large-scale watershed and offer valuable guidance to decision makers who are responsible in designing adaptation strategies to mitigate the risks from extreme water-related disasters in the future.

Variation and Influencing Factors of Cloud Characteristics over Qinghai Lake from 2006 to 2019

Clouds are an indispensable part of climate change, and the occurrence and development of clouds in the Qinghai Lake area (QHL) have great significance for the regional energy budget and precipitation system. To a certain extent, clouds will affect the water resources, agriculture, animal husbandry, and photovoltaic power industry in this region. In this study, we used CloudSat satellite data, combined with meteorological elements and atmospheric circulation, to analyze the cloud occurrence frequency and cloud water content in QHL. The results demonstrate that the frequency of cloud occurrences in QHL is 33% with a decreasing trend from 2006 to 2019. Altostratus and Nimbostratus are the main types of cloud systems in QHL. The cloud ice water content is 62.21 mg/m3 and the cloud liquid water content is 261.66 mg/m3. The highest value of the vertical cloud fraction occurs from March to June, at a height of 7–11 km in QHL. The height of the mixed-phase clouds is approximately 4–8 km and the ice clouds are above 8 km. The vertical distribution of ice particles is relatively dispersed, while the vertical distribution of liquid particles is relatively concentrated. The time and height of high particle effective radius and high particle concentration are consistent with the high value of cloud water content. The decrease in total cloud occurrence frequency in QHL is caused by the increase in temperature. This study helps to clarify the detailed structure of clouds and the distribution of cloud water resources, which has an important reference value for the study of climate change impact and the sustainable development of lake resources in QHL.

Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate

For climate-change impact studies at the catchment scale, meteorological variables are typically extracted from ensemble simulations provided by global and regional climate models, which are then downscaled and bias-adjusted for each study site. For bias adjustment, different statistical methods that re-scale climate model outputs have been suggested in the scientific literature. They range from simple univariate methods that adjust each meteorological variable individually, to more complex and more demanding multivariate methods that take existing relationships between meteorological variables into consideration. Over the past decade, several attempts have been made to evaluate such methods in various regions. There is, however, still no guidance for choosing appropriate bias adjustment methods for a study at hand. In particular, the question whether the benefits of potentially improved adjustments outweigh the cost of increased complexity, remains unanswered.This paper presents a comprehensive evaluation of the performance of two commonly used univariate and two multivariate bias adjustment methods in reproducing numerous univariate, multivariate and temporal features of precipitation and temperature series in different catchments in Sweden. The paper culminates in a discussion on trade-offs between the potential benefits (i.e., skills and added value) and disadvantages (complexity and computational demand) of each method to offer plausible, defensible and actionable insights from the standpoint of climate-change impact studies in high latitudes.We concluded that all selected bias adjustment methods generally improved the raw climate model simulations, but that not a single method consistently outperformed the other methods. There were, however, differences in the methods' performance for particular statistical features, indicating that other practical aspects such as computational time and heavy theoretical requirements should also be taken into consideration when choosing an appropriate bias adjustment method.

Performance evaluation of CORDEX-Africa regional climate models in simulating climate variables over Ethiopian main rift valley: Evidence from Gidabo river basin for impact modeling studies

Measuring the simulation skill of regional climate models (RCMs) is vital in selecting the best performing model that can be used for climate change studies. To that end, the performance of eleven Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa RCMs were evaluated against observed datasets from 1991 to 2005 over Gidabo river basin (GRB), main Ethiopian rift valley. RCMs’ outputs were evaluated using coefficient of variation (CV), percent of bias (PBIAS), Root Means Square Error (RMSE), Pearson's correlation coefficient (r), revised R-squared (RR2), Taylor Skill Score (TSS), Mann-Kendall (MK) trend test and Sen’s slope estimator. The results confirm the difference of RCMs in capturing the annual and seasonal climate variables. In relation to the spatial pattern of the rainfall, RACMO22T (EC-EARTH) strongly reproduced the mean annual rainfall. CCLM4–8 (MPI) and mean ensemble reproduced the annual patterns of the observed rainfall despite the fact with varying rainfall amounts reproduced. The seasonal rainfall pattern was satisfactorily captured by RACMO22T (EC-EARTH), CCLM4–8 (MPI) and REMO2009 (MPI). The agreement between the observed and modeled rainfall is superior in CCLM4–8 (MPI) and RACMO22T (EC-EARTH) at station level. CRCM5 (MPI) satisfactory replicated the patterns of both minimum and maximum temperature. RACMO22T (EC-EARTH) showed best performance in simulating annual and seasonal rainfall trends in GRB. In overall, models that performs better in replicating the observed climatology include RACMO22T (EC-EARTH), CCLM4–8 (MPI), CRCM5 (MPI), CCLM4–8 (CNRM), and REMO2009 (EC-EARTH). The study underscored the use of the mean ensemble of model simulation did not always guarantee better agreement with observation than individual models. Therefore prior to climate impact study, it is advisable to correct the systematic bias and employ the multi-model ensemble of best performing models for climate change impact and adaptation studies in the GRB.

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.

Advancing traditional strategies for testing hydrological model fitness in a changing climate

Mitigation of adverse effects of global warming relies on accurate flow projections under climate change. These projections usually focus on changes in hydrologic signatures, such as 100-year floods, which are estimated through statistical analyses of simulated flows under baseline and future conditions. However, models used for these simulations are traditionally calibrated to reproduce entire flow series, rather than statistics of hydrologic signatures. Here, we consider this dichotomy by testing whether performance indicators (e.g., Nash-Sutcliffe coefficient) are informative about model ability to reproduce distributions and trends in the signatures. Results of streamflow simulations in 50 high-latitude catchments with the 3DNet-Catch model show that high model performances according to traditional indicators do not assure that distributions or trends in hydrologic signatures are well reproduced. We, therefore, suggest that performance in reproducing distributions and trends in hydrologic signatures should be included in the process of model selection for climate change impacts studies.

Climate change and cocoyam (Colocasia esculenta (L.) Schott) production: assessing impacts and potential adaptation strategies in Zimbabwe

Tropical root and tuber crops such as cocoyam (Colocasia esculenta (L.)) are important for food security and livelihoods and yet neglected in climate change impact studies and large-scale crop improvement programs. The aim of this study was to apply the maximum entropy modelling approach to assess production potential for the orphan crop cocoyam under current and projected climatic conditions by 2050 and 2070 in Zimbabwe. A robust model fit was achieved (AUC > 0.9) with variable importance showing that precipitation-related factors were most important in determining the suitability of cocoyam. About 4.3% of the country is suitable for cocoyam production in Zimbabwe under current climatic conditions with the most suitable areas in eastern districts of Chipinge, Chimanimani, Mutare, Mutasa, Nyanga and Makoni. By 2050, model means project a decrease of 6%, 9%, 10% and 15% under RCP2.6, RCP4.5, RCP6.0 and RCP8.5, respectively. More drastic decreases are projected by 2070 with almost a quarter (23%) of the current suitable areas having lost their suitability for cocoyam production. There is a general model agreement in the direction of impacts except for RCP2.6 where CCSM4 model projects increases in suitability for cocoyam in the country while other models project decreases. We find that regulating canopy microclimate variation increases potential for cocoyam production under climate change and can be implemented to ensure resilience of cocoyam production systems. Therefore, stabilizing or improving orphan crops systems will substantially contribute to local food security and reduction of malnutrition especially during the lean season.

Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?

Abstract. Climate change impact studies on hydrological extremes often rely on hydrological models with parameters inferred through calibration procedures using observed meteorological data as input forcing. We show that this procedure can lead to a biased evaluation of the probability distribution of high streamflow extremes when climate models are used. As an alternative approach, we introduce a methodology, coined “Hydrological Calibration of eXtremes” (HyCoX), in which the calibration of the hydrological model, as driven by climate model output, is carried out by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The application to the Adige River catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produces reliable quantiles of the annual maximum streamflow to be used in assessment studies.

Study of Climate Change Impact on Crops and Soil Health in India: A Review

Climate change such as rising temperature, atmospheric carbon dioxide levels, rainfall variability and altering soil physical, chemical and biological properties are observed all around the world. A review over impacts of clim ate change with relation to crops and soil health was done at Department of Agricultural Meteorology, CCS HAU Hisar. IPCC, 2021 provides new estimates of the chances of crossing the global warming level of 1.5°C in the next decades and finds that unless there are immediate, rapid and large-scale reductions in greenhouse gas emissions, limiting warming to close to 1.5°C or even 2°C will be beyond reach. Impacts of climate change on soil properties are long time process as weather elements induce Physio-chemical reaction with soil which happens slowly in relation to weather elements. Climate change is generally expectedto increase the crop yields due to CO2 fertilization, radiation use efficiency and longer growing season but on the other hand, beyond a critical limit it may develop stress conditions like water stress, sun burns, scorching, bark cracking, closure stomata, leaf senescence and abscission etc. that can decrease crop production. Acidification, sodicity and salinization problem can develop in soil due to increase in temperature and acidic rainfall. So it can be held that, climate change would have intensive impacts on cereal and horticulture crops health and production as well as on soil properties.

A systematic study of climate change impact on fish and fisheries sector in Bangladesh and role of Geospatial science in mitigation

Aquaculture is the world’s fastest-growing food-producing sector, with a mean annual growth rate of 5.3% during 2001–2018. Bangladesh, despite being a densely populated and small country, has managed to ensure its high position in this growing economy. Despite widespread benefits, aquaculture in Bangladesh has recently encountered concerns due to climate change, as several climatic threats to fish production have been recognized. It is high time for Bangladesh to try to find an adaptive measure to cope with the adverse effect of climate change, especially in coastal areas. This study will identify the climate change impact on fish in Bangladesh aquaculture and propose how geospatial technology can be utilized to resolve these issues through a systematic literature review from available of past 5 years. The study is expected to benefit the beneficiaries such as aquafarmers, researchers, scientists, and policymakers to resolve the climate-related issues on Bangladesh aquaculture for sustainable production.

CALIBRATION AND VALIDATION OF CERES-SORGHUM CROP SIMULATION MODEL IN DSSATV 4.7

A study was conducted during rabi, 2016-17 and 2017-18 to calibrate and validate the CERESSorghummodel in DSSATv 4.7. The NRMSE (Normal Root Mean Square Error) values for simulations forphysiological maturity are 16.06 per cent, 19.7 per cent, 12.08 per cent and 14.3 per cent for Maldandi (V1),Phule Vasudha (V2), Phule Maulee (V3) and Phule Chitra (V4), respectively. The mean simulated grain yieldwas 37.4 (q/ha) against observed grain yield of 37.6 (q/ha) in case of V1. The mean observed grain yieldswere 44.7 and 44.3, 37 and 35.6, 45.2 and 41.8 (q/ha) in case of V2, V3 and V4, respectively. The NRMSE valuesfor simulations were 19.6%, 19.6%, 15.71% and 17.2% for V1, V2, V3 and V4, respectively. The mean simulatedstraw yield was 77.51 (q/ha) against observed yield of 91.0 (q/ha) case of V1. According to Loague and Green(1991) if NRMSE is between 10–20%, simulations are good and CERES- Sorghum model is good inpredicting the growth and yield of Sorghum. As the model is predicting the phenology and yield well,CERES-Sorghum model can be used for climate change impact studies.

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.