Future Climate Datasets Research Articles

Modeling hydrological processes under Multi-Model projections of climate change in a cold region of Hokkaido, Japan

Local hydrological systems are highly sensitive to global warming, especially in cold-climate-dominated basins. The integrated climate-hydrological model is a powerful tool to analyze and predict such effects. However, modeling hydrological processes contains considerable uncertainty in cold areas due to the complex dynamics of freeze–thaw cycles and requirements of lot inputs data. Here, we developed a fast and simple approach to modify the soil temperature module in soil and water assessment tool (SWAT) model in a cold region of the Ishikari River basin (IRB), Hokkaido, Japan. Water balance components and sediment yield simulations were explored by the modified SWAT model, outperforming the original SWAT model by PBIAS less than 15 % and NSE higher than 0.60 in monthly streamflow and sediment yield during both calibration (2011–2015) and validation phases (2016–2019). The future prediction was integrated with future climate datasets under four Shared Socioeconomic Pathways (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) derived from two Global Circulation Models (GCMs), in two future stages (2045–2069 and 2075–2099) with relative to the baseline stage (1990–2014). The varying climate with warmer temperatures (+3.05℃) and less precipitation (-7%) in the future would lead to that evapotranspiration (ET, 67 %) increased. Water yield (WY, −9%), surface runoff (SURQ, −18 %), groundwater flow (GWQ, −10 %), lateral flow (LATQ, −7%), percolation (PERC, −10 %), streamflow (-7%) and sediment yield (-14 %) decreased across the whole basin. However, due to climatic spatial variation, at the subbasin scale, hydrological components (e.g., WY, GWQ, LATQ, and PERC) would increase in the northern region, at the same time, SURQ and ET rise significantly in the central and southern areas. Our findings could offer evidence for future water and soil protection policymaking across the IRB.

Climate data uncertainty for agricultural impact assessments in West Africa

Reliable information on climate impacts can support planning processes to make the agricultural sector—which has cascading effects on food security, livelihoods and the security situation—more resilient. Subsequently, uncertainties in past and future climate data need to be decreased and better understood. In this study, we analysed the quality and limitations of different past and future climate data sets to be used for agricultural impact assessments in West Africa. The high differences between the three analysed past climate data sets underline the high observational uncertainty in West Africa and show the influence of selecting the observational data set for the bias-adjustment of climate model data. The ten CMIP6 (Coupled Model Inter-comparison Project Phase 6) models show regional and model-dependent biases with similar systematic biases as have been observed in earlier CMIP versions. Although the bias-adjusted version of this data (ISIMIP3b—Inter-Sectoral Impact Model Intercomparison Project) aligns overall well with observations, we could detect some regional strong deviations from observations for some agroclimatological indices. The use of the multi-model ensemble mean has resulted in an improved agreement of CMIP6 and the bias-adjusted ISIMIP3b data with observations. Choosing a sub-ensemble of bias-adjusted models could only improve the performance of the ensemble mean locally but not over the whole region. Therefore, our results suggest the use of the whole model ensemble for agricultural impact assessments in West Africa. While averaging the impact results over all climate models can serve as a best guess, the spread of the results over all models should be considered to give insights into the uncertainties. This study can support agricultural impact modelling in quantifying climate risk hotspots as well as suggesting suitable adaptation measures to increase the resilience of the agricultural sector in West Africa.

Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies.

Impact of merging of historical and future climate data sets on land carbon cycle projections for South America

AbstractEarth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non‐linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias‐correct the UK Met Office ESM, HadGEM2‐ES, against the CRU–JRA observation‐based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2‐ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day−1, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO2 flux becomes a source to the atmosphere by the end of this century. Transitions to a CO2 source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias‐corrected simulations estimate the rise in South American land carbon stocks between pre‐industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate‐induced role of greater soil decomposition in the fate of the Amazon carbon sink.

Climate change impact assessment on Northeast China's grain production.

The international community is paying more attention to climate change because a consensus has been reached that climate change has an adverse effect not only on the environment but also on agriculture. Therefore, in this study, present and future climate datasets (obtained from general circulation models) including atmospheric carbon concentration were used to assess the impact of climate change on grain production for an important base of China (Northeast). An empirical model has been developed using climate and other additional variables (effective irrigation area, fertilizer, and labor force) to assess the effect of climate change on grain production. The results revealed that maximum temperature is a key climate determinant in grain production of the study area. Atmospheric carbon concentration showed a significant impact on grain outputs in most of the cases. During the analysis, it was observed that precipitation displayed a declining trend while an effective irrigation area showed positive non-significant contribution to grain production. Analysis based on different representative concentration pathways exhibited that maximum temperature may contribute negatively to grain production in the future. Overall, the analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered.

Effect of Climate Change on a Monolithic Desulphurized Tailings Cover

A soil cover system can be viewed as a thin interface placed between the atmosphere and the underlying waste. Climate is a primary design variable in soil cover design; therefore, climate change poses a number of challenges to design, operation and long-term performance of covers. In this research climate change effects on the hydraulic behavior of soil covers at a Northern Ontario, Canada site were assessed. Covers were analyzed using historical and future climate datasets. Historical climate data were compiled from an Environment Canada weather station near the site. The future climate datasets were sourced for different Global Circulation Models (GCM) for various representative concentration pathways (RCP). The covers at the site were constructed with a single layer of desulphurized tailings. Soil covers were meant to limit oxygen ingress to the underlying reactive tailings by maintaining high water saturation in the covers. Oxygen flux through soil covers for current and future climates were predicted using variably saturated water flow and oxygen transport modeling using the finite element method. The results of this research indicate that the effect of climate change on soil cover depends on the hydraulic properties of the soil cover materials and that of the underlying tailings. The results of this study suggest that the effect of climate change on the coarse tailing covers could be marginal resulting in a maximum increase of 5% in oxygen flux at the cover surface for the future climates in comparison to the base climate. However, in the case of fine tailings covers, increases of up to 65% can be expected.

Modeling climate change impact on streamflow as affected by snowmelt in Nicolet River Watershed, Quebec

Frequent spring flooding in Southern Quebec’s Nicolet River watershed has a history of causing severe damage, which is likely to worsen as climate change progresses. Employing the ArcSWAT model, an attempt was made to assess the potential impacts of climate change on the Nicolet River watershed’s seasonal and annual streamflow, particularly that portion affected by snowmelt. Calibrated and validated against observed streamflow data for the periods of 1986–1990 and 1991–2000, respectively, the model reliably predicted daily streamflow (e.g., percent bias within ±15%, Nash-Sutcliffe model efficiency >0.50, and the ratio of root mean square error to the standard deviation ≤0.70). In an effort to investigate the impacts of climate change on streamflow, future climate datasets were generated for 2053–2067 by implementing the eleven sets of existing Regional Climate Model (RCM) simulations produced for the North American Regional Climate Change Assessment Program (NARCCAP) in the ArcSWAT model. The ArcSWAT model’s hydrological responses were closely tied to changes of climate variables: a strong correlation existed between simulated runoff and precipitation, and between temperature and predicted evapotranspiration, snowfall, and winter snowmelt. Projected future climate data showed increases in both average temperature (+2.5 °C) and precipitation (+21%). Significant greater total precipitation was forecasted for the winter season, while total snowfall was projected to decrease by 6%. However, the snowmelt showed an increasing trend for the late winter and earlier spring period. Streamflow was expected to increase annually and in most seasons except spring. Annual peak flows volumes would increase by 13% in the future and the occurrence of peak flows would shift to the winter (vs. the spring), indicating a greater risk of winter flooding in the future. The individual impact of temperature and precipitation on peak flows showed that increases in peak flows were mainly tied to increased precipitation, while the shift in their timing was mostly tied to warming temperatures.

Visually Communicating Future Climate in a Web Environment

AbstractWhile there is growing demand for use of climate model projections to understand the potential impacts of future climate on resources, there is a lack of effective visuals that convey the range of possible climates across spatial scales and with uncertainties that potential users need to inform their impact assessments and studies. We use usability testing including eye tracking to explore how a group of resource professionals (foresters) interpret and understand a series of graphical representations of future climate change, housed within a web-based decision support system (DSS), that address limitations identified in other tools. We find that a three-map layout effectively communicates the spread of future climate projections spatially, that location-specific information is effectively communicated if depicted both spatially on a map and temporally on a time series plot, and that model error metrics may be useful for communicating uncertainty and in demonstrating the utility of these future climate datasets.

Evaluation of regional climate model simulated rainfall over Indonesia and its application for downscaling future climate projections

ABSTRACTThe need for good quality information on climate and its future changes has become increasingly important for society. Of particular interest are analysis and predictions of rainfall at pertinent spatial scales. An option is the use of regional climate models (RCMs) as a physics‐based downscaling tool to retrieve higher spatial resolution information from coarser present and future climate datasets. In order to verify if simulations provide added values that result in an appropriate representation of the state of climate at finer spatial resolutions, the adoption of RCMs requires prior optimization. The performance of the Weather Research and Forecasting Model RCM in simulating precipitation over Indonesia is examined by a series of sensitivity experiments using different parameterized convective physics. Among four tested schemes, the best performance is provided by the Betts‐Miller‐Janjic (BMJ) parameterization. RCM multiannual seasonal rainfall bias outperforms or matches the reanalysis. Modeled results provide added value in simulating rainfall‐related climate indices but show low skill in recreating the annual rainfall pattern at monthly resolution. RCM precipitation exhibits complex spatial response to different ENSO phases, with El Niño conditions resulting in a general loss of model skill during the southern hemisphere spring. A series of regional climate simulations using the BMJ convective scheme forced by a future climate projection dataset show changes in rainfall aligned with previous studies.

Changes in future potential distributions of apex predator and mesopredator mammals in North America

Climate change has determined shifts in distributions of species and changing climates are likely to continue to affect species in the future. In this study, we used Maxent, an ecological niche modeling algorithm, to estimate the potential future distributions of apex predator and mesopredator mammals in boreal forest and tundra biomes of North America. We projected the climatic niche models of apex predators and mesopredators on future climate datasets based on three global circulation models (Beijing Climate Center Climate System Model, Hadley Global Environment Model, and Model for Interdisciplinary Research on Climate Earth System Model) and four greenhouse gas emission scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5). Under future climate projections, the potential distributions of most of the predators studied increased by 2050 and 2070. The potential distributions of two apex predators (brown bear, Ursus arctos, and polar bear, U. maritimus) and two mesopredator species (Canadian lynx, Lynx canadensis, and Arctic fox, Vulpes lagopus) were predicted to decline under all emission scenarios, by 2050 and 2070. The only apex predator that was predicted to increase its distribution under all greenhouse gas emission scenarios was U. americanus (American black bear). Similarly, distributions of mesopredators like Mephitis mephitis (striped skunk), and Procyon lotor (raccoon) were predicted to increase greatly under future climate conditions of all four emission scenarios. Predicted expansions of distribution ranges of most mesopredators and contractions of distribution ranges of apex predators included in this study may result in changes of species interactions in North American boreal forests and tundras in the future.

Energy use implications of different design strategies for multi-storey residential buildings under future climates

The effects of climate change on the final and primary energy use of versions of a multi-storey residential building have been analysed. The building versions are designed to the Swedish building code (BBR 2015) and passive house criteria (Passive 2012) with different design and overheating control strategies under different climate scenarios. Future climate datasets are based on Representative Concentration Pathway scenarios for 2050–2059 and 2090–2099. The analysis showed that strategies giving the lowest space heating and cooling demands for the Passive 2012 building version remained the same under all climate scenarios. In contrast, strategies giving the lowest space heating and cooling demands for the BBR 2015 version varied, as cooling demand became more significant under future climate scenarios. Cooling demand was more dominant than heating for the Passive 2012 building version under future climate scenarios. Household equipment and technical installations based on best available technology gave the biggest reduction in total primary energy use among considered strategies. Overall, annual total operation primary energy decreased by 37–54% for the building versions when all strategies are implemented under the considered climate scenarios. This study shows that appropriate design strategies could result in significant primary energy savings for low-energy buildings under changing climates.

Smallholder farmers managing climate risk in India: 2. Is it climate-smart?

Research about adaptation of crops to climate change at a regional scale is based on simplifying assumptions about current and future weather and about farmer management practices. Additionally, the impacts of adaptations are usually measured only in production terms and the feasibility of implementing proposed adaptations is rarely tested. In this study into adaptations of rice based cropping systems to future climate scenarios in Telangana, India, all adaptations were generated through participatory engagement, and were field-tested with local smallholder households in three villages as well as by cropping system simulation analysis. Adaptation options were first evaluated for historical climate variability, with outcomes assessed in terms of production, profitability and environmental consequences before they were evaluated as climate-smart adaptations to medium term climate change. In an earlier study, participatory intervention at household level was used to identify and evaluate new practices. These adaptations to climate variability were then tested with the cropping systems simulator APSIM on local historical weather data. Here we test the applicability of these adaptations to likely climate scenarios in 2021–2040 by using and statistically downscaling two contrasting global circulation models to generate contrasting climate change scenarios for each location. Adaptations were simulated with these future climate data sets and evaluated in terms of their gross margin, yield, yield stability, gross margin stability, global warming potential, greenhouse gas emissions intensity and, where irrigation treatments were varied, net water use, irrigation water productivity, contribution to the recharge of aquifers and nitrogen leached from the root zone. Compared with variability in historic yields the simulated yield changes in 2021–2040 climate scenarios were modest and their direction was dependent on the global circulation model used. Sustainability polygons were used to compare historic and future climate scenarios. These polygons clearly showed that adaptation options mostly resulted in trade-offs between productivity and environmental outcomes and between competing environmental outcomes. Results that were simulated for historic weather were strongly reflected in the two future weather scenarios, leading to the conclusion that participatory action research with smallholder farmers, coupled with field testing and simulation analysis can produce practical, sustainable and productive adaptations to climate variability that are also climate smart in that they are robust for future climate scenarios to 2021–2040. We propose that sustainability polygons may be a useful quantitative tool for analysis of the degree to which adaptations may be regarded as climate smart.

Making energy simulation easier for future climate – Synthesizing typical and extreme weather data sets out of regional climate models (RCMs)

Higher availability of future climate data sets, generated by regional climate models (RCMs) with fine temporal and spatial resolutions, improves and facilitates the impact assessment of climate change. Due to significant uncertainties in climate modeling, several climate scenarios should be considered in the impact assessment. This increases the number of simulations and size of data sets, complicating the assessment and decision making. This article suggests an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies. The method is based on synthesizing three sets of weather data out of one or more RCMs: one typical and two extremes. The method aims at decreasing the number of weather data sets without losing the quality and details of the original future climate scenarios. The application of the method is assessed for an office building in Geneva and the residential building stock in Stockholm.Results show that using the synthesized data sets provides an accurate estimation of future conditions. Variations and uncertainties of future climate are represented by the synthesized data. In the case of synthesizing weather data using multiple climate scenarios, the number of simulations and the size of data sets are decreased enormously. Combining the typical and extreme data sets enables to have better probability distributions of future conditions, very similar to the original RCM data.

Observed and projected changes in temperature and rainfall in Cambodia

Temperature and rainfall patterns in Cambodia are governed by monsoons and characterized by two major wet and dry seasons. The average annual rainfall is 1400mm in the central low land regions and may reach 4000mm in certain coastal zones or in highland areas. The annual average temperature is 28°C, with an average maximum temperature of 38°C in April and an average minimum temperature of 17°C in January. This paper presents the climate change scenarios using MAGICC–SCENGEN program, which links emissions scenarios with global and regional climate change and has adopted the regional climate model (PRECIS) in combination with a number of GCM models with resolution of 50×50km, using observation data and two historical and future climate data sets generated by RCM model downscaling under the two emission scenarios SRES A2 and SRES B2. Projections of maximum and minimum temperatures and rainfall patterns from 2008 to 2099 are described. For future studies, daily data are required perform vulnerability and adaptation assessments.

Modelling the Impacts of Climate Change on Agro-Ecological Zones – a Case Study of Taita Hills, Kenya

Climate variations all over the world have huge potential impacts to the environment at large. Human and economic activities are not left behind when climate change issues are concerned. Agriculture, being the core human and one of the basic economic activities is severely hampered by climate variations. In Africa, and specifically in Kenya, there has been rampant change in the growing patterns of some crops. Farmers lack knowledge that ascertains the reason behind the low income from their farm produce. The main research objective in this study is to determine the variation of agro-ecological zones (AEZ) of Taita hills in Kenya based on climate changes. Specific objectives are: Mapping historical climate data (1960-2010) and comparing with future climate datasets of 2050, matching the layers in a grid cell (Agro-ecological cell) using Geographical Information System (GIS) and delineating the AEZ based on selected parameters. This paper shows the development of AEZ for Taita hills in Kenya from a GIS point of view and develops a model of its variation given that climate is varying over a period of time. It gives a prediction for the variation of the zones based on climate change in the year 2050. A comparison of the historical and future AEZ is shown and is used to assess the impact of climate change on agricultural land. Finally it addresses the sensitive question of economical value to the farmers given that there will be a change and gives some remarks on possible farming practices that the farmers can adopt.

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine-scale climate data may provide markedly different estimates of climate-change impacts than coarse-scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse-scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000-fold range of spatial scales (0.008-16km(2) ). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90m digital elevation models and deriving bioclimatic predictors from them. As climate-data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine- and coarse-scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.

Use of Representative Climate Futures in impact and adaptation assessment

A key challenge for climate projection science is to serve the rapidly growing needs of impact and adaptation assessments (hereafter risk assessments) in an environment where there are substantial differences in the regional projections of climate models, an expanding number of potentially relevant climate model results, and a desire amongst many users to limit the number of future climate scenarios in their assessments. While it may be attractive to select a small number of climate models based on their ability to replicate current climate, there is no robust method for doing this. We outline and illustrate a method that addresses this challenge in a different way. The range of plausible future climates simulated by climate models is classified into a small set of Representative Climate Futures (RCFs) and the relative likelihood of these estimated. For each region, the RCFs are then used as a framework in which to classify more detailed information, such as available climate model and downscaled data sets. Researchers wishing to apply the RCFs in risk assessments can then choose to use a subset of RCFs, such as the “most likely”, “high risk” and “least change” cases for their impact system. Preparation and analysis of future climate data sets can therefore be confined to those models whose simulations best represent the selected RCFs. This significantly reduces the number of models involved, and potentially the effort required to undertake the risk assessment. Consistently applied within a region, RCFs, rather than individual climate models, can become the boundary objects which anchor discussion between the climate science and risk assessment communities, simplifying communication. Since the RCF descriptions need not change as new climate model results emerge, they can also provide a stable framework for assimilating risk assessments undertaken at different times with different sets of climate models. Systematic application of this approach requires various challenges to be addressed, such as robustly classifying future regional climates into a small set and estimating likelihoods.