Multiple Climate Models Research Articles

The global-scale impacts of climate change: the QUEST-GSI project

This paper introduces the special issue of Climatic Change on the QUEST-GSI project, a global-scale multi-sectoral assessment of the impacts of climate change. The project used multiple climate models to characterise plausible climate futures with consistent baseline climate and socio-economic data and consistent assumptions, together with a suite of global-scale sectoral impacts models. It estimated impacts across sectors under specific SRES emissions scenarios, and also constructed functions relating impact to change in global mean surface temperature. This paper summarises the objectives of the project and its overall methodology, outlines how the project approach has been used in subsequent policy-relevant assessments of future climate change under different emissions futures, and summarises the general lessons learnt in the project about model validation and the presentation of multi-sector, multi-region impact assessments and their associated uncertainties to different audiences.

Assessment of climate change impact on river flow regimes in The Red River Delta, Vietnam – A case study of the Nhue-Day River Basin

Global warming has caused dramatic changes in regional climate variability, particularly regarding fluctuations in temperature and rainfall. Thus, it is predicted that river flow regimes will be altered accordingly. The purpose of this paper is to present the results of modeling such changes by simulating discharge using the HEC-HMS model. The precipitation was projected using super-high resolution multiple climate models (20 km resolution) with newly updated emission scenarios as the input for the HEC-HMS model for flow analysis at the Red River Basin in the northern area of Vietnam. The findings showed that climate change impact on the river flow regimes tend towards a decrease in the dry season and a longer duration of flood flow. A slight runoff reduction is simulated for November while a considerable runoff increase is modeled for July and August amounting to 30% and 25%, respectively. The discharge scenarios serve as a basis for water managers to develop suitable adaptation methods and responses on the river basin scale.

Assessment of climate change impacts on watershed in cold-arid region: an integrated multi-GCM-based stochastic weather generator and stepwise cluster analysis method

An integrated multi-GCM-based stochastic weather generator and stepwise cluster analysis (MGCM-SWG–SCA) method is developed, through incorporating multiple global climate models (MGCM), stochastic weather generator (SWG), and stepwise-clustered hydrological model (SCHM) within a general framework. MGCM-SWG–SCA can investigate uncertainties of projected climate changes as well as create watershed-scale climate projections from large-scale variables. It can also assess climate change impacts on hydrological processes and capture nonlinear relationship between input variables and outputs in watershed systems. MGCM-SWG–SCA is then applied to the Kaidu watershed with cold-arid characteristics in the Xinjiang Uyghur Autonomous Region of northwest China, for demonstrating its efficiency. Results reveal that the variability of streamflow is mainly affected by (1) temperature change during spring, (2) precipitation change during winter, and (3) both temperature and precipitation changes in summer and autumn. Results also disclose that: (1) the projected minimum and maximum temperatures and precipitation from MGCM change with seasons in different ways; (2) various climate change projections can reproduce the seasonal variability of watershed-scale climate series; (3) SCHM can simulate daily streamflow with a satisfactory degree, and a significant increasing trend of streamflow is indicated from future (2015–2035) to validation (2006–2011) periods; (4) the streamflow can vary under different climate change projections. The findings can be explained that, for the Kaidu watershed located in the cold-arid region, glacier melt is mainly related to temperature changes and precipitation changes can directly cause the variability of streamflow.

Multi‐model analysis of the Atlantic influence on Southern Amazon rainfall

AbstractAmazon rainfall is subject to year‐to‐year fluctuation resulting in drought and flood in various intensities. A major climatic driver of the interannual variation of the Amazon rainfall is El Niño/Southern Oscillation. Also, the sea surface temperature over the Atlantic Ocean is identified as an important climatic driver on the Amazon water cycle. Previously, observational data sets were used to support the Atlantic influence on Amazon rainfall. Here, it is found that multiple global climate models do reproduce the Atlantic–Amazon link robustly. However, there exist differences in rainfall response, which primarily depends on the climatological rainfall amount.

Climate change in the Northeast United States: An analysis of the NARCCAP multimodel simulations

Based on the outputs from multiple regional climate models (RCMs) forced by global climate models (GCMs) in phase II of the North American Regional Climate Change Assessment Program, current simulations and future projections of surface air temperature and precipitation over the Northeast U.S. region are analyzed. We address two questions in this study: How do the combined biases from the GCM-RCM pairs compare to that from their driving GCMs? Are the future responses projected by these GCM-RCM pairs robust and consistent with that predicted by their driving GCMs? The GCM-RCM pair may produce decreased combined biases compared to the inherent biases in the driving GCM, but the possibility of compensating errors suggest that this does not necessarily imply improved representations of current climate. An RCM simulation may also impart biases which exacerbate those from the driving GCM, leading to a large combined error. The substantial underestimates of winter and summer temperature produced by Geophysical Fluid Dynamics Laboratory (GFDL) Experimental Climate Prediction-2 and GFDL Regional Climate Model version 3 are partly attributed to moderate cold biases from their driving GFDL, whereas dynamically downscaling Hadley Centre Climate Model version 3 with Hadley Centre Regional Climate Model leads to mitigated combined biases. The “probability density functions” of temperature and precipitation are estimated for each individual model pair to illustrate the temporal and spatial distributions of these two climate variables. Under the Special Report on Emissions Scenarios A2 emission scenario, the ensemble averaged changes in winter and summer temperature for the mid-21st century (2041–2070) vary between 2.5 to 3.2°C across the subregions. These warming signals are consistent and statistically significant across the model pairs and far exceed the estimated natural variability. The projected future changes in precipitation indicate generally wetter winters and drier summers, but the magnitudes, directions, and spatial distributions of precipitation changes are model-dependent. Moreover, the ensemble average summer precipitation changes (0.6 to −7.9% as estimated by percentage of present-day values) fall within the estimated range of natural variability. Different changes in moisture flux convergence-divergence appear to contribute to the disagreement of the precipitation responses between some GCM-RCM pairs and their driving GCMs. The “reliability ensemble averaging” procedure is also applied and provides a complement to a simpler averaging method to estimate the average and uncertainty range of the simulated climate changes.

Seasonal drought predictability and forecast skill over China

AbstractUnder a changing environment, seasonal droughts have been exacerbated with devastating impacts. However, the understanding of drought mechanism and predictability is limited. Based on the hindcasts from multiple climate models, the predictability and forecast skill for drought over China are investigated. The 3 month standardized precipitation index is used as the drought index, and the predictability is quantified by using a perfect model assumption. Ensemble hindcasts from multiple climate models are assessed individually, and the grand multimodel ensemble is also evaluated. Drought forecast skill for model ensemble mean is higher than individual ensemble members, and North American Multimodel Ensemble grand ensemble performs the best. Predictability is higher than forecast skill, indicating the room for improving drought forecast. Drought predictability and forecast skill are positively correlated in general, but they vary depending on seasons, regions, and forecast leads. Higher drought predictability and forecast skill are found over regimes where ENSO has significant impact. For the ENSO‐affected regimes, both drought predictability and forecast skill in ENSO years are higher than that in neutral years. This study suggests that predictability not only provides a measure for selecting climate models for ensemble drought forecast in ENSO‐affected regimes but also serves as an indicator for forecast skill especially when in situ and/or remote sensing measurements for the hindcast verifications are considered unreliable.

Interacting effects of climate change and habitat fragmentation on drought-sensitive butterflies

Climate change is expected to increase the frequency of some climatic extremes. These may have drastic impacts on biodiversity, particularly if meteorological thresholds are crossed, leading to population collapses. Should this occur repeatedly, populations may be unable to recover, resulting in local extinctions. Comprehensive time series data on butterflies in Great Britain provide a rare opportunity to quantify population responses to both past severe drought and the interaction with habitat area and fragmentation. Here, we combine this knowledge with future projections from multiple climate models, for different Representative Concentration Pathways (RCPs), and for simultaneous modelled responses to different landscape characteristics. Under RCP8.5, which is associated with ‘business as usual’ emissions, widespread drought-sensitive butterfly population extinctions could occur as early as 2050. However, by managing landscapes and particularly reducing habitat fragmentation, the probability of persistence until mid-century improves from around zero to between 6 and 42% (95% confidence interval). Achieving persistence with a greater than 50% chance and right through to 2100 is possible only under both low climate change (RCP2.6) and semi-natural habitat restoration. Our data show that, for these drought-sensitive butterflies, persistence is achieved more effectively by restoring semi-natural landscapes to reduce fragmentation, rather than simply focusing on increasing habitat area, but this will only be successful in combination with substantial emission reductions.

Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios

Previous studies have combined climate, crop and economic models to examine the impact of climate change on agricultural production and food security, but results have varied widely due to differences in models, scenarios and input data. Recent work has examined (and narrowed) these differences through systematic model intercomparison using a high-emissions pathway to highlight the differences. This paper extends that analysis to explore a range of plausible socioeconomic scenarios and emission pathways. Results from multiple climate and economic models are combined to examine the global and regional impacts of climate change on agricultural yields, area, production, consumption, prices and trade for coarse grains, rice, wheat, oilseeds and sugar crops to 2050. We find that climate impacts on global average yields, area, production and consumption are similar across shared socioeconomic pathways (SSP 1, 2 and 3, as we implement them based on population, income and productivity drivers), except when changes in trade policies are included. Impacts on trade and prices are higher for SSP 3 than SSP 2, and higher for SSP 2 than for SSP 1. Climate impacts for all variables are similar across low to moderate emissions pathways (RCP 4.5 and RCP 6.0), but increase for a higher emissions pathway (RCP 8.5). It is important to note that these global averages may hide regional variations. Projected reductions in agricultural yields due to climate change by 2050 are larger for some crops than those estimated for the past half century, but smaller than projected increases to 2050 due to rising demand and intrinsic productivity growth. Results illustrate the sensitivity of climate change impacts to differences in socioeconomic and emissions pathways. Yield impacts increase at high emissions levels and vary with changes in population, income and technology, but are reduced in all cases by endogenous changes in prices and other variables.

Net exchanges of methane and carbon dioxide on the Qinghai-Tibetan Plateau from 1979 to 2100

Methane (CH4) is a potent greenhouse gas (GHG) that affects the global climate system. Knowledge about land–atmospheric CH4 exchanges on the Qinghai-Tibetan Plateau (QTP) is insufficient. Using a coupled biogeochemistry model, this study analyzes the net exchanges of CH4 and CO2 over the QTP for the period of 1979–2100. Our simulations show that the region currently acts as a net CH4 source with 0.95 Tg CH4 y−1 emissions and 0.19 Tg CH4 y−1 soil uptake, and a photosynthesis C sink of 14.1 Tg C y−1. By accounting for the net CH4 emission and the net CO2 sequestration since 1979, the region was found to be initially a warming source until the 2010s with a positive instantaneous radiative forcing peak in the 1990s. In response to future climate change projected by multiple global climate models (GCMs) under four representative concentration pathway (RCP) scenarios, the regional source of CH4 to the atmosphere will increase by 15–77% at the end of this century. Net ecosystem production (NEP) will continually increase from the near neutral state to around 40 Tg C y−1 under all RCPs except RCP8.5. Spatially, CH4 emission or uptake will be noticeably enhanced under all RCPs over most of the QTP, while statistically significant NEP changes over a large-scale will only appear under RCP4.5 and RCP4.6 scenarios. The cumulative GHG fluxes since 1979 will exert a slight warming effect on the climate system until the 2030s, and will switch to a cooling effect thereafter. Overall, the total radiative forcing at the end of the 21st century is 0.25–0.35 W m−2, depending on the RCP scenario. Our study highlights the importance of accounting for both CH4 and CO2 in quantifying the regional GHG budget.

Evaluation of multiple regional climate models for summer climate extremes over East Asia

In this study, five regional climate models (RCMs) participating in the CORDEX-East Asia project (HadGEM3-RA, RegCM4, SNU-MM5, SNU-WRF, and YSU-RSM) are evaluated in terms of their performances in simulating the climatology of summer extremes in East Asia. Seasonal maxima of daily mean temperature and precipitation are analyzed using the generalized extreme value method. RCMs show systematic bias patterns in both seasonal means and extremes. A cold bias is located along the coast, whereas a warm bias occurs in northern China. Overall, wet bias occurs in East Asia, but with a substantial dry bias centered in South Korea. This dry bias appears to be related to the colder ocean surface around South Korea, positioning the monsoonal front further south compared to observations. Taylor diagram analyses reveal that the models simulate temperature means more accurately compared to extremes because of the higher spatial correlation, whereas precipitation extremes are simulated better than their means because of the higher spatial variability. The latter implies that extreme rainfall events can be captured more accurately by RCMs compared to the driving GCM despite poorer simulation of mean rainfall. Inter-RCM analysis indicates a close relationship between the means and extremes in terms of model skills, but it does not show a clear relationship between temperature and precipitation. Sub-regional analysis largely supports the mean–extreme skill relationship. Analyses of frequency and intensity distributions of daily data for three selected sub-regions suggest that overall shifts of temperature distribution and biases in moderate–heavy precipitations contribute importantly to the seasonal mean biases.

Stochastic Water Quality Modeling of an Impaired River Impacted by Climate Change

AbstractA new stochastic water quality modeling tool was applied to quantify potential climate change effects on a nutrient impaired reach in the Pacific Northwest. This tool allows for multiple stochastic inputs for steady state river water quality simulations. A previously published calibrated deterministic model of the targeted reach was adapted for this study. This model simulates steady-state nutrient, algae, and dissolved oxygen dynamics with both point and nonpoint pollutant loadings. It also includes simulation of diurnally varying water temperature, calculated as a function of air temperature, shading, and streamflow using heat budget equations. For this study, local summer air temperature and critical low flows were treated stochastically in the model. Parameterization of these inputs was based on analysis of multiple global climate model (GCM) projections for the study area corresponding to a 2060 planning horizon. Continuous probability distribution functions were fitted to ensemble GCM data s...

Scientific and practical tools for dealing with water resource estimations for the future

Abstract. Future flow regimes will be different to today and imperfect knowledge of present and future climate variations, rainfall–runoff processes and anthropogenic impacts make them highly uncertain. Future water resources decisions will rely on practical and appropriate simulation tools that are sensitive to changes, can assimilate different types of change information and flexible enough to accommodate improvements in understanding of change. They need to include representations of uncertainty and generate information appropriate for uncertain decision-making. This paper presents some examples of the tools that have been developed to address these issues in the southern Africa region. The examples include uncertainty in present day simulations due to lack of understanding and data, using climate change projection data from multiple climate models and future catchment responses due to both climate and development effects. The conclusions are that the tools and models are largely available and what we need is more reliable forcing and model evlaution information as well as methods of making decisions with such inevitably uncertain information.

Future hydrological extremes: the uncertainty from multiple global climate and global hydrological models

Abstract. Projections of changes in the hydrological cycle from global hydrological models (GHMs) driven by global climate models (GCMs) are critical for understanding future occurrence of hydrological extremes. However, uncertainties remain large and need to be better assessed. In particular, recent studies have pointed to a considerable contribution of GHMs that can equal or outweigh the contribution of GCMs to uncertainty in hydrological projections. Using six GHMs and five GCMs from the ISI-MIP multi-model ensemble, this study aims: (i) to assess future changes in the frequency of both high and low flows at the global scale using control and future (RCP8.5) simulations by the 2080s, and (ii) to quantify, for both ends of the runoff spectrum, GCMs and GHMs contributions to uncertainty using a two-way ANOVA. Increases are found in high flows for northern latitudes and in low flows for several hotspots. Globally, the largest source of uncertainty is associated with GCMs, but GHMs are the greatest source in snow-dominated regions. More specifically, results vary depending on the runoff metric, the temporal (annual and seasonal) and regional scale of analysis. For instance, uncertainty contribution from GHMs is higher for low flows than it is for high flows, partly owing to the different processes driving the onset of the two phenomena (e.g. the more direct effect of the GCMs' precipitation variability on high flows). This study provides a comprehensive synthesis of where future hydrological extremes are projected to increase and where the ensemble spread is owed to either GCMs or GHMs. Finally, our results underline the need for improvements in modelling snowmelt and runoff processes to project future hydrological extremes and the importance of using multiple GCMs and GHMs to encompass the uncertainty range provided by these two sources.

Regional hydrological impacts of climate change: implications for water management in India

Abstract. Climate change is most likely to introduce an additional stress to already stressed water systems in developing countries. Climate change is inherently linked with the hydrological cycle and is expected to cause significant alterations in regional water resources systems necessitating measures for adaptation and mitigation. Increasing temperatures, for example, are likely to change precipitation patterns resulting in alterations of regional water availability, evapotranspirative water demand of crops and vegetation, extremes of floods and droughts, and water quality. A comprehensive assessment of regional hydrological impacts of climate change is thus necessary. Global climate model simulations provide future projections of the climate system taking into consideration changes in external forcings, such as atmospheric carbon-dioxide and aerosols, especially those resulting from anthropogenic emissions. However, such simulations are typically run at a coarse scale, and are not equipped to reproduce regional hydrological processes. This paper summarizes recent research on the assessment of climate change impacts on regional hydrology, addressing the scale and physical processes mismatch issues. Particular attention is given to changes in water availability, irrigation demands and water quality. This paper also includes description of the methodologies developed to address uncertainties in the projections resulting from incomplete knowledge about future evolution of the human-induced emissions and from using multiple climate models. Approaches for investigating possible causes of historically observed changes in regional hydrological variables are also discussed. Illustrations of all the above-mentioned methods are provided for Indian regions with a view to specifically aiding water management in India.

Changes of seasonal storm properties in California and Nevada from an ensemble of climate projections

AbstractPrecipitation characteristics, such as intensity, frequency, duration, and event pattern, are changing due to the increases in greenhouse gases, transition of ocean oscillation phases, etc. In this paper, we evaluate the ability of 11 realizations from multiple regional climate model (RCM)/global climate model pairs in the North American Regional Climate Change Assessment Program (NARCCAP) to simulate the seasonal variability and magnitude of storm properties, including storm duration, interstorm period, and storm intensity. The results indicate that NARCCAP RCMs simulate the seasonal variability better in the Greater Sacramento and San Joaquin than in Las Vegas, which may be due to the RCMs' inability to simulate local convective precipitation associated with the North American Monsoon. We also investigate the impacts of climate change on these storm characteristics by comparing the percentage change and absolute change of storm properties determined from NARCCAP historical runs and future runs. We find that individual RCMs exhibit great uncertainty in the percentage changes in storm duration, interstorm period, and average storm intensity. The ensemble means of storm properties across 11 future NARCCAP RCM projections show different responses to climate change in different locations. Our analyses provide guidelines for selecting the appropriate RCMs for hydrologic studies related to storm properties and provide forecasters and water managers with detailed information of future changes in storm properties so that they can sustainably manage water resources. Our results may also contribute to the nonstationary precipitation scenario development by incorporating the percentage changes of storm properties caused by human‐induced warming into the stochastic precipitation model.

Regional Variations in Potential Plant Habitat Changes in Response to Multiple Global Warming Scenarios*

Abstract This study examines the impacts of global warming on the timing of plant habitat changes in the twenty-first century using climate scenarios from multiple global climate models (GCMs). The plant habitat changes are predicted by driving the bioclimate rule in a dynamic global vegetation model using the climate projections from 16 coupled GCMs. The timing of plant habitat changes is estimated by the first occurrence of specified fractional changes (10%, 20%, and 30%). All future projections are categorized into three groups by the magnitude of the projected global-mean land surface temperature changes: low (<2.5 K), medium (2.5–3.5 K), and high (>3.5 K) warming. During the course of the twenty-first century, dominant plant habitat changes are projected in ecologically transitional (i.e., from tropical to temperate and temperate to boreal) regions. The timing of plant habitat changes varies substantially according to regions. In the low-warming group, habitat changes of 10% in southern Africa occur in 2028, earlier than in the Americas by more than 70 yr. Differences in the timing between regions increase with the increase in warming and fractional threshold. In the subtropics, fast plant habitat changes are projected for the Asia and Africa regions, where countries of relatively small gross domestic product (GDP) per capita are concentrated. Ecosystems in these regions will be more vulnerable to global warming, because countries of low economic power lack the capability to deal with the warming-induced habitat changes. Thus, it is important to establish international collaboration via which developed countries provide assistance to mitigate the impacts of global warming.

Implications of Modelled Climate and Land Cover Changes on Runoff in the Middle Route of the South to North Water Transfer Project in China

Sustainable management of water for human uses and maintaining river health requires reliable information about the future availability of water resources. We quantified the separate and combined impacts of climate and land cover changes on runoff for the historical record and for modelled future scenarios in the upper Han River and Luan River, supply and demand zones respectively of the middle route of the South to North Water Transfer Project in China, the world’s largest inter-basin water transfer project. We used a precipitation-runoff model, averaged multiple climate model predictions combined with three emissions scenarios, a combined CA-Markov model to predict land cover change, and a range of statistical tests. Comparing baseline with 2050: climate change would cause an average reduction in runoff of up to 15 % in the upper Han River and up to 9 % in the Luan River catchment; a scenario involving increased forest cover would reduce runoff by up to 0.19 % in the upper Han River and up to 35 % in the Luan River; a scenario involving increased grass cover would increase runoff by up to 0.42 % in the upper Han River and up to 20 % in the Luan River. In the lower Luan River, the mean annual flow after 1998 fell to only 17 % of that of the baseline period, posing a serious threat to river health. This was explained largely by extraction of surface water and groundwater, rather than climate and land use change.

Temporal constraints on future accumulation-area loss of a major Arctic ice cap due to climate change (Vestfonna, Svalbard).

Arctic glaciers and ice caps are major contributors to past, present and future sea-level fluctuations. Continued global warming may eventually lead to the equilibrium line altitudes of these ice masses rising above their highest points, triggering unstoppable downwasting. This may feed future sea-level rise considerably. We here present projections for the timing of equilibrium-line loss at the major Arctic ice cap Vestfonna, Svalbard. The projections are based on spatially distributed climatic mass balance modelling driven by the outputs of multiple climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) forced by the Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0 and 8.5. Results indicate strongly decreasing climatic mass balances over the 21st century for all RCPs considered. Glacier-wide mass-balance rates will drop down to −4 m a−1 w.e. (water equivalent) at a maximum. The date at which the equilibrium line rises above the summit of Vestfonna (630 m above sea level) is calculated to range between 2040 and 2150, depending on scenario.

Evaluating the impacts of climate change on diurnal wind power cycles using multiple regional climate models

Electrical utility system operators must plan resources so that electricity supply matches demand throughout the day. As the proportion of wind‐generated electricity in the US grows, changes in daily wind patterns have the potential either to disrupt the utility or increase the value of wind to the system over time. Wind power projects are designed to last many years, so at this timescale, climate change may become an influential factor on wind patterns. We examine the potential effects of climate change on the average diurnal power production cycles at 12 locations in North America by analyzing averaged and individual output from nine high‐resolution regional climate models comprising historical (1971–1999) and future (2041–2069) periods. A semi‐parametric mixed model is fit using cubic B‐splines, and model diagnostics are checked. Then, a likelihood ratio test is applied to test for differences between the time periods in the seasonal daily averaged cycles, and agreement among the individual regional climate models is assessed. We investigate the significant changes by combining boxplots with a differencing approach and identify broad categories of changes in the amplitude, shape, and position of the average daily cycles. We then discuss the potential impact of these changes on wind power production. Copyright © 2015 John Wiley & Sons, Ltd.

The Potential Impact of Regional Climate Change on Fire Weather in the United States

Climate change is expected to alter the frequency and severity of atmospheric conditions conducive for wildfires. In this study, we assess potential changes in fire weather conditions for the contiguous United States using the Haines Index (HI), a fire weather index that has been employed operationally to detect atmospheric conditions favorable for large and erratic fire behavior. The index summarizes lower atmosphere stability and dryness into an integer value with higher values indicting more fire-prone conditions. We use simulations produced by the North American Regional Climate Change Assessment Program (NARCCAP) from multiple regional climate models (RCMs) driven by multiple general circulation models (GCMs) to examine changes by midcentury in the seasonal percentage of days and the consecutive number of days with high (values ≥ 5) HI across the United States. Despite differences among the six RCM–GCM combinations in the magnitude and location of the projected changes, the results consistently suggest an increase in the number of days with high HI values over most of the United States during the summer season, with the dryness factor of the HI contributing more than the stability parameter to the projected changes. In addition, the consecutive number of days with high HI is projected to increase in summer. Together, these results suggest that future summers might be more conducive to large and dangerous fires. The projections for other seasons are inconsistent among the model combinations.