A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

How Frequently Will the Persistent Heavy Rainfall over the Middle and Lower Yangtze River Basin in Summer 2020 Happen under Global Warming?

The middle and lower Yangtze River basin (MLYRB) suffered persistent heavy rainfall in summer 2020, with nearly continuous rainfall for about six consecutive weeks. How the likelihood of persistent heavy rainfall resembling that which occurred over the MLYRB in summer 2020 (hereafter 2020PHR-like event) would change under global warming is investigated. An index that reflects maximum accumulated precipitation during a consecutive five-week period in summer (Rx35day) is introduced. This accumulated precipitation index in summer 2020 is 60% stronger than the climatology, and a statistical analysis further shows that the 2020 event is a 1-in-70-year event. The model projection results derived from the 50-member ensemble of CanESM2 and the multimodel ensemble (MME) of the CMIP5 and CMIP6 models show that the occurrence probability of the 2020PHR-like event will dramatically increase under global warming. Based on the Kolmogorov–Smirnoff test, one-third of the CMIP5 and CMIP6 models that have reasonable performance in reproducing the 2020PHR-like event in their historical simulations are selected for the future projection study. The CMIP5 and CMIP6 MME results show that the occurrence probability of the 2020PHR-like event under the present-day climate will be double under lower-emission scenarios (CMIP5 RCP4.5, CMIP6 SSP1-2.6, and SSP2-4.5) and 3–5 times greater under higher-emission scenarios (3.0 times for CMIP5 RCP8.5, 2.9 times for CMIP6 SSP3-7.0, and 4.8 times for CMIP6 SSP5-8.5). The inter-model spread of the probability change is small, lending confidence to the projection results. The results provide a scientific reference for mitigation of and adaptation to future climate change.     

Screening robust water infrastructure investments and their trade-offs under global change: A London example

We propose an approach for screening future infrastructure and demand management investments for large water supply systems subject to uncertain future conditions. The approach is demonstrated using the London water supply system. Promising portfolios of interventions (e.g., new supplies, water conservation schemes, etc.) that meet London’s estimated water supply demands in 2035 are shown to face significant trade-offs between financial, engineering and environmental measures of performance. Robust portfolios are identified by contrasting the multi-objective results attained for (1) historically observed baseline conditions versus (2) future global change scenarios. An ensemble of global change scenarios is computed using climate change impacted hydrological flows, plausible water demands, environmentally motivated abstraction reductions, and future energy prices. The proposed multi-scenario trade-off analysis screens for robust investments that provide benefits over a wide range of futures, including those with little change. Our results suggest that 60 percent of intervention portfolios identified as Pareto optimal under historical conditions would fail under future scenarios considered relevant by stakeholders. Those that are able to maintain good performance under historical conditions can no longer be considered to perform optimally under future scenarios. The individual investment options differ significantly in their ability to cope with varying conditions. Visualizing the individual infrastructure and demand management interventions implemented in the Pareto optimal portfolios in multi-dimensional space aids the exploration of how the interventions affect the robustness and performance of the system.     

Regional impact assessment of flooding under future climate and socio-economic scenarios for East Anglia and North West England

Interactive tools developed within the RegIS project for assessing the impacts of flooding provide information to support flood management policies and analyse the performance of possible adaptation activities to climate change. This paper describes the methodologies used in the development of these tools including tidal and fluvial flooding processes with different levels of climate pressures, represented by changes in sea level and peak river flows. Potential impacts of climate change for East Anglia and North West England are explored to the 2050s using four socio-economic scenarios to represent plausible futures. This includes changes in urban land use as well as adaptive responses to flooding comprising dike upgrade and realignment options. The results indicate that future climate will increase flood risk in both regions. East Anglia is more vulnerable to climate change than North West England at the present level of protection, especially in the extensive coastal lowlands of the Fens and Broads because of the combined effects of sea-level rise and increased fluvial flows. Although the present adaptive policy of upgrading defences in East Anglia will reduce the impacts of flooding, this policy is not effective in the case of the more extreme climate change scenarios by 2050s. In this case, more extensive adaptation would be required.     

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

     

Modelling the impacts of climate change on wheat yield and field water balance over the Murray�CDarling Basin in Australia

The study used a modelling approach to assess the potential impacts of likely climate change and increase in CO₂ concentration on the wheat growth and water balance in Murray?CDarling Basin in Australia. Impacts of individual changes in temperature, rainfall or CO₂ concentration as, well as the 2050 and 2070 climate change scenarios, were analysed. Along an E?CW transect, wheat yield at western sites (warmer and drier) was simulated to be more sensitive to temperature increase than that at eastern sites; along the S?CN transect, wheat yield at northern warmer sites was simulated to be more sensitive to temperature increase, within 1?C3°C temperature increase. Along the E?CW and S?CN transects, wheat at drier sites would benefit more from elevated [CO₂] than at wetter sites, but more sensitive to the decline in rainfall. The increase in temperature only did not have much impact on water balance. Elevated [CO₂] increased the drainage in all the sites, whilst rainfall reduction decreased evapotranspiration, runoff and drainage, especially at drier sites. In 2050, wheat yield would increase by 1?C10% under all climate change scenarios along the S?CN transect, except for the northernmost site (Dalby). Along the E?CW transect, the most obvious increase of wheat yields under all climate change scenarios occurred in cooler and wetter eastern sites (Yass and Young), with an average increase rate of 7%. The biggest loss occurred at the driest sites (Griffith and Swan Hill) under A1FI and B2 scenarios, ranging from ?5% to ?16%. In 2070, there would be an increased risk of yield loss in general, except for the cool and wet sites. Water use efficiency was simulated to increase at most of the study sites under all the climate change scenarios, except for the driest site. Yield variability would increase at drier sites (Ardlethan, Griffith and Swan Hill). Soil types would also impact on the response of wheat yield and water balance to future climate change.     

气候变化科学方面的几个最新认知

IPCC第六次评估报告(AR6)第一工作组报告主要从以下几个方面的进展提升了我们对气候系统变化、气候变化原因以及预估未来气候系统变化等方面的认知,对过去气候变化及其与人类活动的关系有了更加清晰、可靠的认识.综合多重证据评估指出,全球气候正经历着前所未有的变化;包括极端事件在内的归因进展已把人类活动对气候系统影响的认识从...     

Precipitation extremes in a karst region: a case study in the Guizhou province, southwest China

The topography of hilly landscapes modifies crop environment changing the fluxes of water and energy, increasing risk in these vulnerable agriculture systems, which could become more accentuated under climate change (drought, increased variability of rainfall). In order to quantify how wheat production in hilly terrain will be affected by future climate, a newly developed and calibrated micro-meteorological model for hilly terrain was linked to a crop growth simulation model to analyse impact scenarios for different European regions. Distributions of yield and growing length of rainfed winter wheat and durum wheat were generated as probabilistic indices from baseline and low (B2) and high (A2) emission climate scenarios provided from the Hadley Centre Regional Climate Model (HadRM3). We used site-specific terrain parameters for two sample catchments in Europe, ranging from humid temperate (southeast UK) to semi-arid Mediterranean (southern Italy). Results for baseline scenario show that UK winter wheat is mainly affected by annual differences in precipitation and yield distributions do not change with terrain, whilst in the southern Mediterranean climate yield variability is significantly related to a slope × elevation index. For future climate, our simulations confirm earlier predictions of yield increase in the UK, even under the high emission scenario. In the southern Mediterranean, yield reduction is significantly related to slope × elevation index increasing crop failure in drier elevated spots but not in wet years under baseline weather. In scenarios for the future, the likelihood of crop failure rises sharply to more than 60%, and even in wet years, yields are likely to decrease in elevated spots.     

Possible impacts of climate change on debris-flow activity in the Swiss Alps

This study uses a long dataset of past debris flows from eight high-elevation catchments in the Swiss Alps for which triggering conditions since AD 1864 have been reconstructed. The torrents under investigation have unlimited sediment supply and the triggering of debris flows is thus mainly controlled by climatic factors. Based on point-based downscaled climate scenarios for meteorological stations located next to the catchments and for the periods 2001–2050 and 2051–2100, we study the evolution of temperature and rainfall above specific thresholds (10, 20, 30, 40 and 50 mm) and durations (1, 2 or 3 days). We conclude that the drier conditions in future summers and the wetting of springs, falls and early winters are likely to have significant impacts on the behavior of debris flows. Based on the current understanding of debris-flow systems and their reaction to rainfall inputs, one might expect only slight changes in the overall frequency of events by the mid-21^st century, but possibly an increase in the overall magnitude of debris flows due to larger volumes of sediment delivered to the channels and an increase in extreme precipitation events. In the second half of the 21^st century, the number of days with conditions favorable for the release of debris flows will likely decrease, especially in summer. The anticipated increase of rainfall during the shoulder seasons (March, April, November, December) is not expected to compensate for the decrease in future heavy summer rainfall over 2 or 3 days.     

Agricultural risk management policies under climate uncertainty

Climate change is forecasted to increase the variability of weather conditions and the frequency of extreme events. Due to potential adverse impacts on crop yields it will have implications for demand of agricultural risk management instruments and farmers’ adaptation strategies. Evidence on climate change impacts on crop yield variability and estimates of production risk from farm surveys in Australia, Canada and Spain, are used to analyse the policy choice between three different types of insurance (individual, area-yield and weather index) and ex post payments. The results are found to be subject to strong uncertainties and depend on the risk profile of different farmers and locations; the paper provides several insights on how to analyse these complexities. In general, area yield performs best more often across our countries and scenarios, in particular for the baseline and marginal climate change (without increases in extreme events). However, area yield can be very expensive if farmers have limited information on how climate change affects yields (misalignment in expectations), and particularly so under extreme climate change scenarios. In these more challenging cases, ex post payments perform well to increase low incomes when the risk is systemic like in Australia; Weather index performs well to reduce the welfare costs of risks when the correlation between yields and index is increased by the extreme events. The paper also analyses the robustness of different instruments in the face of limited knowledge of the probabilities of different climate change scenarios; highlighting that this added layer of uncertainty could be overcome to provide sound policy advice under uncertainties introduced by climate change. The role of providing information to farmers on impacts of climate change emerges as a crucial result of this paper as indicated by the significantly higher budgetary expenditures occurring across all instruments when farmers’ expectations are misaligned relative to actual impacts of climate change.     

A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom

Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.     

Atmosphere-land surface interactions and their influence on extreme rainfall and potential abrupt climate change over southern Africa

In a changing climate, changes in rainfall variability and, in particular, extreme rainfall events are likely to be highly significant for environmentally vulnerable regions such as southern Africa. It is generally accepted that sea-surface temperatures play an important role in modulating rainfall variability, thus the majority work to date has focused on these mechanisms. However past research suggests that land surface processes are also critical for rainfall variability. In particular, work has suggested that the atmosphere-land surface feedback has been important for past abrupt climate changes, such as those which occurred over the Sahara during the mid-Holocene or, more recently, the prolonged Sahelian drought. Therefore the primary aim of this work is to undertake idealised experiments using both a regional and global climate model, to test the sensitivity of rainfall variability to land surface changes over a location where such abrupt climate changes are projected to occur in the future, namely southern Africa. In one experiment, the desert conditions currently observed over southwestern Africa were extended to cover the entire subcontinent. This is based on past research which suggests a remobilisation of sand dune activity and spatial extent under various scenarios of future anthropogenic global warming. In the second experiment, savanna conditions were imposed over all of southern Africa, representing an increase in vegetation for most areas except the equatorial regions. The results suggest that a decrease in rainfall occurs in the desert run, up to 27% of total rainfall in the regional model (relative to the control), due to a reduction in available moisture, less evaporation, less vertical uplift and therefore higher near surface pressure. This result is consistent across both the regional and global model experiments. Conversely an increase in rainfall occurs in the savanna run, because of an increase in available moisture giving an increase in latent heat and therefore surface temperature, increasing vertical uplift and lowering near surface pressure. These experiments, however, are only preliminary, and form the first stage of a wider study into how the atmosphere-land surface feedback influences rainfall extremes over southern Africa in the past (when surface i.e. vegetation conditions were very different) and in the future under various scenarios of future climate change. Future work will examine how other climate models simulate the atmosphere-land surface feedback, using more realistic vegetation types based on past and future surface conditions.     

Developing climate change scenarios for Tamil Nadu, India using MAGICC/SCENGEN

This paper describes the projection of climate change scenarios under increased greenhouse gas emissions, using the results of atmospheric-ocean general circulation models in the Coupled Model Intercomparison Project phase 3 dataset. A score is given to every model based on global and regional performance. Four out of 20 general circulation models (GCMs) were selected based on skill in predicting observed annual temperature and precipitation conditions. The ensemble of these four models shows superiority over the individual model scores. These models were subjected to increases in future anthropogenic radiative forcings for constructing climate change scenarios. Future climate scenarios for Tamil Nadu were developed with MAGICC/SCENGEN software. Model results show both temperature and precipitation increases under increased greenhouse gas scenarios. Northeast and northwest parts of Tamil Nadu show a greater increase in temperature and precipitation. Seasonally, the maximum rise in temperature occurred during the MAM season, followed by DJF, JJA, and SON. Decreasing trends of precipitation were observed during DJF and MAM.     

Shoreline evolution under climate change wave scenarios

This paper investigates changes in shoreline evolution caused by changes in wave climate. In particular, a number of nearshore wave climate scenarios corresponding to a ??present?? (1961?C1990) and a future time-slice (2071?C2100) are used to drive a beach evolution model to determine monthly and seasonal statistics. To limit the number of variables, an idealised shoreline segment is adopted. The nearshore wave climate scenarios are generated from wind climate scenarios through point wave hindcast and inshore transformation. The original wind forcing comes from regional climate change model experiments of different resolutions and/or driving global climate models, representing different greenhouse-gas emission scenarios. It corresponds to a location offshore the south central coast of England. Hypothesis tests are applied to map the degree of evidence of future change in wave and shoreline statistics relative to the present. Differential statistics resulting from different global climate models and future emission scenarios are also investigated. Further, simple, fast, and straightforward methods that are capable of accommodating a great number of climate change scenarios with limited data reduction requirements are proposed to tackle the problem under consideration. The results of this study show that there are statistically significant changes in nearshore wave climate conditions and beach alignment between current and future climate scenarios. Changes are most notable during late summer for the medium-high future emission scenario and late winter for the medium-low. Despite frequent disagreement between global climate change models on the statistical significance of a change, all experiments agreed in future seasonal trends. Finally, a point of importance for coastal management, material shoreline changes are generally linked to significant changes in future wave direction rather than wave height.     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

Deep uncertainty in long-term hurricane risk: Scenario generation and implications for future climate experiments

Current projections of long-term trends in Atlantic hurricane activity due to climate change are deeply uncertain, both in magnitude and sign. This creates challenges for adaptation planning in exposed coastal communities. We present a framework to support the interpretation of current long-term tropical cyclone projections, which accommodates the nature of the uncertainty and aims to facilitate robust decision making using the information that is available today. The framework is populated with projections taken from the recent literature to develop a set of scenarios of long-term hurricane hazard. Hazard scenarios are then used to generate risk scenarios for Florida using a coupled climate–catastrophe modeling approach. The scenarios represent a broad range of plausible futures; from wind-related hurricane losses in Florida halving by the end of the century to more than a four-fold increase due to climate change alone. We suggest that it is not possible, based on current evidence, to meaningfully quantify the relative confidence of each scenario. The analyses also suggest that natural variability is likely to be the dominant driver of the level and volatility of wind-related risk over the coming decade; however, under the highest scenario, the superposition of this natural variability and anthropogenic climate change could mean notably increased levels of risk within the decade. Finally, we present a series of analyses to better understand the relative adequacy of the different models that underpin the scenarios and draw conclusions for the design of future climate science and modeling experiments to be most informative for adaptation.     

An exploratory study on occurrence and impact of climate change on agriculture in Tamil Nadu,India

This study has been undertaken to examine the occurrence of climate change in Tamil Nadu, the southernmost state of India and its impact on rainfall pattern which is a primary constraint for agricultural production. Among the five sample stations examined across the state, the minimum temperature has increased significantly in Coimbatore while the same has decreased significantly in Vellore whereas both minimum and maximum temperatures have increased significantly in Madurai since 1969 with climate change occurring between late 1980s and early 1990s. As a result, the south-west monsoon has been disturbed with August rainfall increasing with more dispersion while September rainfall decreasing with less dispersion. Thus, September, the peak rainfall month of south-west monsoon before climate change, has become the monsoon receding month after climate change. Though there has been no change in the trend of the north-east monsoon, the quantity of October and November rainfall has considerably increased with increased dispersion after climate change. On the whole, south-west monsoon has decreased with decreased dispersion while north-east monsoon has increased with increased dispersion. Consequently, the season window for south-west monsoon crops has shortened while the north-east monsoon crops are left to fend against flood risk during their initial stages. Further, the incoherence in warming, climate change and rainfall impact seen across the state necessitates devising different indigenous and institutional adaptation strategies for different regions to overcome the adverse impacts of climate change on agriculture.

     

Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods

Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.     

Development of an airmass-based regional climate change scenario

Summary In middle latitudes, regional climates are largely determined by the frequency and character of different airmasses advected across the region. Airmass characteristics and frequencies are expected to be different in a warmer world. General circulation models are, for example, unanimous in projecting large temperature changes for high latitudes, the source region for polar airmasses. Conventional approaches to the construction of regional climate change scenarios are not able to capture such differences between airmasses. Here we present a new approach that assigns each day in the observed and model-produced records to one of three classes based on the upper-level flow, the steering current for airmasses. This approach permits an evaluation of a model's ability to reproduce the observed regional climate in terms of airmasses which is more insightful than a comparison of monthly means. The model used here, the CCM0 version of the NCAR model, was found to reproduce many of the observed December airflow features (the month chosen to demonstrate the approach) for the Lake Superior basin. The approach also permits a more insightful analysis of the projected changes under 2*CO₂ conditions. The CCM0 projects a significant warming and moistening only for the northerly airflows. The northerly flows are also projected to become more frequent. To illustrate the significance of these results, daily scenarios of climate change were constructed from these projections and used in a lake evaporation model. It is found that the changes in the northerly flows projected by this model translate into a 19% reduction in the evaporative power of the air over Lake Superior (wind speeds held at present level).With 3 Figures