Projections Of Future Climate Change Research Articles

편의보정된 고해상도 다중지역기후모델을 이용한 서울의 미래 기후변화 불확실성 추정

This study analyzed future projections on daily mean values and extremes for temperature and daily precipitation over Seoul metropolitan city using bias-corrected high-resolution multi-regional climate models. The factors of uncertainty for the future projection of climate variables were defined. In the time series analysis of future projections for regional climate models, the average daily temperature and the number of days of the hot day-hot night were predicted to have a stable trend in the RCP2.6 scenario, and showed a tendency to increase continuously in the RCP8.5 scenario. The daily mean precipitation and RX1day (annual daily maximum precipitation) had large annual variabilities in the models. In the estimation of the fraction of total variance, the daily mean temperature was dominated by the internal variability in the early 21st century and the most contributing to the scenario uncertainty in the late 21st century. The daily mean precipitation showed a remarkable contribution from the internal variability over the entire period. The number of days of the hot day-hot night showed a similar contribution pattern to that of the daily mean temperature. For the RX1day, the internal variability dominated over the entire period, and the scenario uncertainty had little contribution. This study will help establish more scientific climate change adaptation policies by providing the uncertainty information for future climate change projection.

Global Carbon Budget 2020

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).

Expert assessment of future vulnerability of the global peatland carbon sink

The carbon balance of peatlands is predicted to shift from a sink to a source this century. However, peatland ecosystems are still omitted from the main Earth system models that are used for future climate change projections, and they are not considered in integrated assessment models that are used in impact and mitigation studies. By using evidence synthesized from the literature and an expert elicitation, we define and quantify the leading drivers of change that have impacted peatland carbon stocks during the Holocene and predict their effect during this century and in the far future. We also identify uncertainties and knowledge gaps in the scientific community and provide insight towards better integration of peatlands into modelling frameworks. Given the importance of the contribution by peatlands to the global carbon cycle, this study shows that peatland science is a critical research area and that we still have a long way to go to fully understand the peatland–carbon–climate nexus. Peatlands are impacted by climate and land-use changes, with feedback to warming by acting as either sources or sinks of carbon. Expert elicitation combined with literature review reveals key drivers of change that alter peatland carbon dynamics, with implications for improving models.

Effect of climate change and urbanisation on flood protection decision‐making

AbstractThe changing climate and the rapid urbanisation may alter flood severity and influence the decision‐making process for flood management. In this study, a Multi‐Criteria Decision Analysis (MCDA) framework for optimal decision‐making in flood protection is developed and applied to a central flood‐prone basin of Jakarta, Indonesia. Specifically, the decisions are on levees corresponding to protection under different rainfall return periods (RP), considering climate change and associated uncertainties, urbanisation, and evolving socio‐economic features of the flood plain. Three cases were studied to analyse future (year 2050) conditions (i) future rainfall/current urban, (ii) current rainfall/future urban and (iii) future rainfall/future urban. Future climate change projections from the NASA Earth Exchange are used to obtain information about changes in rainfall, whereas Landsat derived imperviousness maps along with the population projections are used for future urban conditions. Annual Expected Loss, Graduality, upgrade Construction cost and Net‐Socio‐Economic Vulnerability Index are the criteria used in the MCDA. It is found that climate change has a higher impact compared to urbanisation on the flood protection decisions. For the basin studied, the extreme future case of increased rainfall and urbanised conditions have the optimal decision in levee protection level corresponding to 250 years RP under current rainfall which corresponds to ~60 years RP under future rainfall.

CMIP5 climate projections for the Yamzhog Yumco Basin: an environmental testbed for alpine lakes

As projections of future climate change rely on global climate model (GCM) results, the performances of five GCMs included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are compared with ground observations (temperature and precipitation, 1961–2005) in southern Qinghai-Tibetan Plateau (TP). The comparison suggests that the models capture the climatological patterns of the observed climate but with cold and wet biases. Future (twenty-first century) trends based on the GCMs’ projections produced a warming trend, and the annual precipitation of the multi-model mean is generally projected to increase. Relative to the 1986–2005 mean, both the average annual temperature and precipitation of the five GCMs are projected to increase in the short term (2016–2035), medium term (2046–2065), and long term (2081–2100) under the scenarios of rcp2.6, rcp4.5, and rcp8.5. While the temperature and precipitation changes and the difference among scenarios become substantial as time evolves. Possible regional climate change impact on the Yamzhog Yumco Basin in southern TP is demonstrated by future lake area estimates based on a minimalist parametric model and the average annual precipitation predicted by the five GCMs: the lake area of Yamzhog Yumco (including Kongmu Co) will expand under the three scenarios for the three periods relative to the 1974–1993 mean. It is expected to obtain more reliable local information with the downscaling of GCM simulations to regional scales by using regional climate models for assessing regional hydrological impacts in more detail for further studies in the southern TP.

Optimization Assessment of Projection Methods of Climate Change for Discrepancies between North and South China

Downscaling methods have been widely used due to the coarse and biased outputs of general circulation models (GCMs), which cannot be applied directly in regional climate change projection. Hence, appropriate selection of GCMs and downscaling methods is important for assessing the impacts of climate change. To explicitly explore the influences of multi-GCMs and different downscaling methods on climate change projection in various climate zones, the Heihe River Basin (HRB) and the Zhanghe River Basin (ZRB) were selected in this study to represent the north arid region and the south humid region in China, respectively. We first evaluated the performance of multi-GCMs derived from Coupled Model Inter-comparison Project Phase 5 (CMIP5) in the two regions based on in-situ measurements and the 40 year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data. Subsequently, to construct appropriate climate change projection techniques, comparative analysis using two statistical downscaling methods was performed with consideration of the significant north–south meteorological discrepancies. Consequently, specific projections of future climate change for 2021–2050 under three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) were completed for the HRB and ZRB, including daily precipitation, maximum air temperature, and minimum air temperature. The results demonstrated that the score-based method with multiple criteria for performance evaluation of multiple GCMs more accurately captured the spatio-temporal characteristics of the regional climate. The two statistical downscaling methods showed respective advantages in arid and humid regions. The statistical downscaling model (SDSM) showed more accurate prediction capacities for air temperature in the arid-climate HRB, whereas model output statistics (MOS) better captured the probability distribution of precipitation in the ZRB, which is characterized by a humid climate. According to the results obtained in this study, the selection of appropriate GCMs and downscaling methods for specific climate zones with different meteorological features significantly impact regional climate change projection. The statistical downscaling models developed and recommended for the north and south of China in this study provide scientific reference for sustainable water resource management subject to climate change.

Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research

Abstract. Integrated assessment models (IAMs) project future anthropogenic emissions which can be used as input for climate models. However, the full list of climate-relevant emissions is lengthy and most IAMs do not model all of them. Here we present Silicone, an open-source Python package which infers anthropogenic emissions of unmodelled species based on other reported emissions projections. For example, it can infer nitrous oxide emissions in one scenario based on carbon dioxide emissions from that scenario plus the relationship between nitrous oxide and carbon dioxide emissions found in other scenarios. Infilling broadens the range of IAMs available for exploring projections of future climate change, and hence Silicone forms part of the open-source pipeline for assessments of the climate implications of IAM scenarios, led by the Integrated Assessment Modelling Consortium (IAMC). This paper presents a variety of infilling options and outlines their suitability for different cases. We recommend certain infilling techniques as good defaults but emphasise that considering the specifics of the model being infilled will produce better results. We demonstrate the package's utility with three examples: infilling all required gases for a pathway with data for only one emission species, splitting up a Kyoto emissions total into separate gases, and complementing a set of idealised emissions curves to provide a complete, consistent emissions portfolio. The code and notebooks explaining details of the package and how to use it are available on GitHub (https://github.com/GranthamImperial/silicone, last access: 2 November 2020). The repository with this paper's examples and uses of the code to complement existing research is available at https://github.com/GranthamImperial/silicone_examples (last access: 2 November 2020).

Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern California

Projections of future climate change impacts suggest an increase of wildfire activity in Mediterranean ecosystems, such as southern California. This region is a wildfire hotspot and fire managers are under increasingly high pressures to minimize socio-economic impacts. In this context, predictions of high-risk fire seasons are essential to achieve adequate preventive planning. Regional-scale weather patterns and climatic teleconnections play a key role in modulating fire-conducive conditions across the globe, yet an analysis of the coupled effects of these systems onto the spread of large wildfires is lacking for the region. We analyzed seven decades (1953–2018) of documentary wildfire records from southern California to assess the linkages between weather patterns and large-scale climate modes using various statistical techniques, including Redundancy Analysis, Superposed Epoch Analysis and Wavelet Coherence. We found that high area burned is significantly associated with the occurrence of adverse weather patterns, such as severe droughts and Santa Ana winds. Further, we document how these fire-promoting events are mediated by climate teleconnections, particularly by the coupled effects of El Niño Southern Oscillation and Atlantic Multidecadal Oscillation.

Geomorphological and ecological change in a coastal foreland dune system, Sandscale haws, Cumbria, UK: the management challenges posed by climate change

Many coastal sand dune systems in the UK and other parts of the world have experienced a decline in bare sand extent and dune mobility over the past 70 years, accompanied by increased coverage of coarse grass, scrub and woodland vegetation. This has raised concerns amongst nature conservationists about the survival of dune specialist species. Opinions are divided, however, regarding what management interventions should be made to counteract these trends. This paper reviews the factors which have contributed to progressive dune system stabilization at Sandscale Haws, located on the Irish Sea coast in Northwest England, considers the implications for the site of UKCP18 future climate change projections, and discusses the options and plans for future adaptive management. Analaysis of aerial photographs indicated a large reduction in bare sand area between 1946 and 1988 (from >34% to 9% of the dune system), since when there has been a further decline to 1.7%. Consideration of the evidence suggests that a trend towards a warmer, slightly wetter and less windy climate has been the main driver of stabilization, although atmospheric nitrogen deposition, increasing atmospheric CO2 concentrations, reduced grazing intensity, reduced human disturbance from the mid-1980s onwards and a tendency for shoreline progradation on the west-facing coast have played contributing roles. Some species characteristic of open habitats can still be found in good numbers in parts of the dune system, but without increased management it is likely they will decline further and some may become locally extinct. Since the site is of national and international importance for a number of nationally scarce plant and animal species it is considered that there is both a legal and an ethical duty to attempt to maintain suitable habitat for their survival. As a National Nature Reserve Sandscale Haws provides a suitable ‘natural laboratory’ within which to research the effectiveness of different intervention methods. A number of small-scale dune rejuvenation methods have been trialled since 2015, and options for larger scale interventions, including turf stripping and possible dune notching, are currently being evaluated as part of the nation-wide Dynamic Dunescapes project.

Historically-based run-time bias corrections substantially improve model projections of 100 years of future climate change

Climate models and/or their output are usually bias-corrected for climate impact studies. The underlying assumption of these corrections is that climate biases are essentially stationary between historical and future climate states. Under very strong climate change, the validity of this assumption is uncertain, so the practical benefit of bias corrections remains an open question. Here, this issue is addressed in the context of bias correcting the climate models themselves. Employing the ARPEGE, LMDZ and CanAM4 atmospheric models, we undertook experiments in which one centre’s atmospheric model takes another centre’s coupled model as observations during the historical period, to define the bias correction, and as the reference under future projections of strong climate change, to evaluate its impact. This allows testing of the stationarity assumption directly from the historical through future periods for three different models. These experiments provide evidence for the validity of the new bias-corrected model approach. In particular, temperature, wind and pressure biases are reduced by 40–60% and, with few exceptions, more than 50% of the improvement obtained over the historical period is on average preserved after 100 years of strong climate change. Below 3 °C global average surface temperature increase, these corrections globally retain 80% of their benefit.

Modeling the Dynamic Landscape Evolution of a Volcanic Coastal Environment Under Future Climate Trajectories

As anthropogenic forcing continues to rapidly modify worldwide climate, impacts on landscape changes will grow. Olivine weathering is a natural process that sequesters carbon out of the atmosphere, but is now being proposed as a strategy that can be artificially implemented to assist in the mitigation of anthropogenic carbon emissions. We use the landscape evolution model Badlands to identify a region (Tweed Caldera catchment in Eastern Australia) that has the potential for naturally enhanced supply of mafic sediments, known to be a carbon sink, into coastal environments. Although reality is more complex than what can be captured within a model, our models have the ability to unravel and estimate how erosion of volcanic edifices and landscape dynamics will react to future climate change projections. Local climate projections were taken from the Australian government and the IPCC in the form of four alternative pathways. Three additional scenarios were designed, with added contributions from the Antarctic Ice Sheet, to better understand how the landscape/dynamics might be impacted by an increase in sea level rise due to ice sheet tipping points being hit. Three scenarios were run with sea level held constant and precipitation rates increased in order to better understand the role that precipitation and sea level plays in the regional supply of sediment. Changes between scenarios are highly dependent upon the rate and magnitude of climatic change. We estimate the volume of mafic sediment supplied to the erosive environment within the floodplain (ranging from ~27-30 million m3 by 2100 and ~78-315 million m3 by 2500), the average amount of olivine within the supplied sediment under the most likely scenarios (~7.6 million m3 by 2100 and ~30 million m3 by 2500), and the amount of CO2 that is subsequently sequestered (~53-73 million tons by 2100 and ~206-284 million tons by 2500). Our approach not only identifies a region that can be further studied in order to evaluate the efficacy and impact of enhanced silicate weathering driven by climate change, but can also help identify other regions that have a natural ability to act as a carbon sink via mafic rock weathering.

Influence of bias-correcting global climate models for regional climate simulations over the CORDEX-Australasia domain using WRF

Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored.

Equatorial Pacific Warming Attenuated by SST Warming Patterns in the Tropical Atlantic and Indian Oceans

AbstractMost climate models project an enhanced mean sea surface temperature (SST) warming in the equatorial Pacific and Atlantic, and a zonal SST dipole in the Indian Ocean. The remote influences of these SST change patterns remain uncertain. To examine the extent to which the patterns of SST changes in the tropical Indian and Atlantic Oceans modulate the warming in the tropical Pacific Ocean, we compare nudging experiments with prescribed structured and uniform SST changes in the tropics outside the Pacific. We find that the warming patterns in the tropical Indian and Atlantic Oceans, respectively, drive a canonical La Niña‐like and elongated equatorial cooling through the Bjerknes feedback, acting to attenuate the warming in the equatorial Pacific substantially. The different SST cooling responses emanate from subtle differences between the initial wind forcing driven by the two basins' SST change patterns. These results have significant implications for future climate change projections.

A geospatial approach to assess climate change impact on soil organic carbon in a semi-arid region

The changes in the atmosphere and climate influence soils by disturbing the functioning of hydrologic and biogeochemical cycles, specifically that of Carbon (C). The impact of the climatic change on soil organic carbon (SOC) stocks over a semi-arid region of India (Medak district, Telangana) was assessed using remote sensing-based indices and geostatistical modeling. The global soil organic carbon (GSOC) stocks (0–30 cm depth) at 1 km spatial resolution and a set of environmental variables like Normalized difference vegetation index (NDVI), Net primary productivity (NPP), slope, elevation and various climate parameters such as rainfall, temperature, etc., were used to estimate the SOC stocks in current and future (2050 and 2070) climate change projections using regression kriging (RK) technique. The results of the study indicate that among the environmental covariates rainfall was the predominant factor that controls the SOC stocks in the study region. The current SOC stock in the study area was estimated as 25.4 Tg in top 0–30 cm depth. The future (2050 and 2070) SOC stocks were predicted using climate parameters of three Global Circulation Models (GCMs); namely CCSM4, HadGEM2-AO, and HadGEM2-ES. Results show a reduction in SOC stocks in 2050 and 2070 due to the projected climate change in the study region. The projected decrease in total SOC stocks varies from 1.12 to 4.93 and 0.45 to 4.49 Tg by 2050 and 2070 respectively. The uncertainty associated with the prediction of the current stock of SOC was 8.44%. The uncertainties associated with the SOC prediction in various future climate change scenarios for the year 2050 and 2070 were in the range of 8.83–10.48% and 8.15–10.26% respectively.

Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models

Abstract. Climate sensitivity to CO2 remains the key uncertainty in projections of future climate change. Transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets. Equilibrium climate sensitivity (ECS) is vital for understanding longer-term climate change and stabilisation targets. In the IPCC 5th Assessment Report (AR5), the stated “likely” ranges (16 %–84 % confidence) of TCR (1.0–2.5 K) and ECS (1.5–4.5 K) were broadly consistent with the ensemble of CMIP5 Earth system models (ESMs) available at the time. However, many of the latest CMIP6 ESMs have larger climate sensitivities, with 5 of 34 models having TCR values above 2.5 K and an ensemble mean TCR of 2.0±0.4 K. Even starker, 12 of 34 models have an ECS value above 4.5 K. On the face of it, these latest ESM results suggest that the IPCC likely ranges may need revising upwards, which would cast further doubt on the feasibility of the Paris targets. Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to constrain the likely range of TCR to 1.3–2.1 K, with a central estimate of 1.68 K. We reach this conclusion through an emergent constraint approach which relates the value of TCR linearly to the global warming from 1975 onwards. This is a period when the signal-to-noise ratio of the net radiative forcing increases strongly, so that uncertainties in aerosol forcing become progressively less problematic. We find a consistent emergent constraint on TCR when we apply the same method to CMIP5 models. Our constraints on TCR are in good agreement with other recent studies which analysed CMIP ensembles. The relationship between ECS and the post-1975 warming trend is less direct and also non-linear. However, we are able to derive a likely range of ECS of 1.9–3.4 K from the CMIP6 models by assuming an underlying emergent relationship based on a two-box energy balance model. Despite some methodological differences; this is consistent with a previously published ECS constraint derived from warming trends in CMIP5 models to 2005. Our results seem to be part of a growing consensus amongst studies that have applied the emergent constraint approach to different model ensembles and to different aspects of the record of global warming.

Potential Future Changes of the Geographic Range Size of Juniperus phoenicea in Algeria based on Present and Future Climate Change Projections

MaxEnt (Maximum Entropy), a Species Distribution Modelling (SDM) method, was applied in the current study in order to assess present and future spatial distribution of the Phoenician juniper (Juniperus phoenicea). Potential future changes in the geographic range size of J. phoenicea in Algeria was determined for the two horizons 2050 and 2070 based on CCSM4 model of the IPCC. Three types of data were used in SDM namely: 21 edaphic factors, 10 topographic parameters, and 19 climatic factors. The AUC value (Area Under Curve) scored 0.966, which showed the high performance of the MaxEnt model. The most contributing variables were: total soil carbon (22.1%), Bio14: driest month precipitation (19.2%), slope (11.1%), Bio15: seasonality of precipitation (coefficient of variation) (10.3), total soil nitrogen (7%), soil available water capacity during summer (6.3%). The presence probability map obtained shows a narrowing of the favorable area of the species by about 52.5% by the year 2070. Such a result asserts the vulnerable state of this species toward the climate change, which results in altitudinal, longitudinal and latitudinal species distribution range shift as a response reflecting the becoming of unfavorable changes of the Phoenician juniper habitats. Based on these results, it is necessary to adopt necessary planning measures for the protection and conservation of the species regarding its vulnerability to climate change.

Nonadditive and Legacy Effects of Spring and Autumn Warming on Soil Respiration in an Old-Field Grassland

Asymmetrically seasonal warming has been widely observed on the Earth, but great uncertainties remain for its effects on terrestrial carbon (C) cycling. As a part of a 7-year seasonal warming experiment in an old-field grassland in Central China, this study demonstrated that spring and autumn warming reduced soil respiration by 8.3% and 9.8%, respectively. In addition, spring warming decreased soil respiration by 1.0% and 15.9% without and with autumn warming, respectively. Autumn warming suppressed soil respiration by 2.5% and 17.2% without and with spring warming, respectively. Decreased soil respiration in summer under both spring and autumn warming dominated the treatment-induced suppressions in annual mean soil respiration, which could be primarily attributable to the reductions in plant photosynthesis and growth (aboveground net primary productivity). These observations suggest that the asymmetrically seasonal warming may diminish soil C loss and its feedback to climate warming. Our findings highlight the urgent need of incorporating the interactive and legacy effects of seasonal warming into models used in the IPCC reports for the robust projections of future climate change.

Using Statistical and Dynamical Downscaling to Assess Climate Change Impacts on Mine Reclamation Cover Water Balances

The oil sands industry in Canada uses soil–vegetation–atmosphere-transfer (SVAT) water balance models, calibrated against short-term (<≈ 10 years) field monitoring data, to evaluate long-term (≈60 years) reclamation cover design performance. These evaluations use long-term historical climate data; however, the effects of climate change should also be incorporated in these analyses. Although statistical downscaling of global climate change projections is commonly used to obtain local, site-specific climate, high resolution dynamical downscaling can also be used. The value of this latter approach to obtain local site-specific projections for mine reclamation covers has not been evaluated previously. This study explored the differences in key water balance components of three reclamation covers and three natural sites in northern Alberta, Canada, under future, site-specific, statistical, and dynamical climate change projections. Historical meteorological records were used to establish baseline periods. Temperature datasets were used to calculate potential evapotranspiration (PET) using the Hargreaves–Samani method. Statistical downscaling uses the Long Ashton Research Station Weather Generator (LARS-WG) and global circulation model (GCM) projections of temperature and precipitation. Dynamical climate change projections were generated on a 4 km grid using the weather research and forecasting (WRF) model. These climate projections were applied to a physically-based water balance model (i.e. Hydrus-1D) to simulate actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods. The key findings were: (a) LARS-WG outperformed WRF in simulating baseline temperatures and precipitation; (b) both downscaling methods showed similar directional shifts in the future temperatures and precipitation; (c) this, in turn, created similar directional shifts in future growing season median AET and NP, although the increase in future NP for LARS-WG was higher than that for WRF. The relative increases in future NP were much higher than the relative increases in future AET, particularly for the reclamation covers.

Assessment of the Future Climate Change Projections on Streamflow Hydrology and Water Availability over Upper Xijiang River Basin, China

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991–1997) and validated (1998–2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January–April period, followed by the wet monsoon season in the May–September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin.

Facing Water Stress in a Changing Climate: A Case Study of Drought Risk Analysis Under Future Climate Projections in the Xi River Basin, China

China, with its fragile environment and ecosystems, is vulnerable to climate change. Continuous changes in climatic conditions have altered precipitation patterns in most regions of China. Droughts become more frequent and severe in the Xi River basin in South China. It is expected that rapid urbanization and climate change will continue to aggravate water stress in this region. There is an urgent need to develop sustainable water management strategies in face of growing water demand and changing water availability. Projection of future climate change impacts on drought conditions has thus become imperative to support improved decision-making in sustainable water management. In this study, we assessed the risk of extreme droughts under future climate projections in the Xi River basin. The Variable Infiltration Capacity (VIC) model was applied to simulate the hydrological processes of the basin under a multitude of future climate scenarios from CMIP5. Based on the precipitation and runoff series obtained from the VIC model, a comprehensive analysis with respect to the major characteristics of meteorological and hydrological droughts had been carried out. This study is of practical and theoretical importance to both policymakers and scholars. First, this study may be a readily available reference work for policymakers when taking consideration of building drought mitigation plans into future water management practices. Second, the findings in this study may provide some valuable insights into the inherent connection between climatic and hydrological changes under a changing climate. Recognition of the connection and interrelation may contribute to the improvement of climatic and hydrological models in practices.