Downscaled Climate Change Scenarios Research Articles

Assessing the impacts of human interventions and climate change on fluvial flooding using CMIP6 data and GIS-based hydrologic and hydraulic models

This study presents an approach for modelling and mapping fluvial flooding, considering both land use/land cover (LULC) change and climate change, and applies it to the Brahmani River Basin in eastern India. Climate change projections were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and their impacts on the hydrology of the catchment were investigated using HEC-HMS and HEC-RAS software. Results reveal that changes in LULC types, specifically an increase in proportions of agricultural and built-up areas and a decrease in forest cover, as undergone between years 1985 and 2018, have increased peak discharge following a storm, thereby causing an increase in spatial extent of floods of different return periods. Moreover, downscaled climate change scenarios from two General Circulation Models were used to determine potential changes in river discharge according to two GHG emission scenarios from the latest IPCC: SSP245 and SSP585. The projections indicate that peak discharge and the spatial extent of flooded areas will increase for floods with return periods ranging from two to 100 years. This study demonstrates the important influence that changes in LULC have had on the susceptibility of the BRB to flooding, with climate change projected to further enhance the risk of flooding as the century progresses.

Coupled regional Earth system modeling in the Baltic Sea region

Abstract. Nonlinear responses to externally forced climate change are known to dampen or amplify the local climate impact due to complex cross-compartmental feedback loops in the Earth system. These feedbacks are less well represented in the traditional stand-alone atmosphere and ocean models on which many of today's regional climate assessments rely (e.g., EURO-CORDEX, NOSCCA and BACC II). This has promoted the development of regional climate models for the Baltic Sea region by coupling different compartments of the Earth system into more comprehensive models. Coupled models more realistically represent feedback loops than the information imposed on the region by prescribed boundary conditions and, thus, permit more degrees of freedom. In the past, several coupled model systems have been developed for Europe and the Baltic Sea region. This article reviews recent progress on model systems that allow two-way communication between atmosphere and ocean models; models for the land surface, including the terrestrial biosphere; and wave models at the air–sea interface and hydrology models for water cycle closure. However, several processes that have mostly been realized by one-way coupling to date, such as marine biogeochemistry, nutrient cycling and atmospheric chemistry (e.g., aerosols), are not considered here. In contrast to uncoupled stand-alone models, coupled Earth system models can modify mean near-surface air temperatures locally by up to several degrees compared with their stand-alone atmospheric counterparts using prescribed surface boundary conditions. The representation of small-scale oceanic processes, such as vertical mixing and sea-ice dynamics, appears essential to accurately resolve the air–sea heat exchange over the Baltic Sea, and these parameters can only be provided by online coupled high-resolution ocean models. In addition, the coupling of wave models at the ocean–atmosphere interface allows for a more explicit formulation of small-scale to microphysical processes with local feedbacks to water temperature and large-scale processes such as oceanic upwelling. Over land, important climate feedbacks arise from dynamical terrestrial vegetation changes as well as the implementation of land-use scenarios and afforestation/deforestation that further alter surface albedo, roughness length and evapotranspiration. Furthermore, a good representation of surface temperatures and roughness length over open sea and land areas is critical for the representation of climatic extremes such as heavy precipitation, storms, or tropical nights (defined as nights where the daily minimum temperature does not fall below 20 ∘C), and these parameters appear to be sensitive to coupling. For the present-day climate, many coupled atmosphere–ocean and atmosphere–land surface models have demonstrated the added value of single climate variables, in particular when low-quality boundary data were used in the respective stand-alone model. This makes coupled models a prospective tool for downscaling climate change scenarios from global climate models because these models often have large biases on the regional scale. However, the coupling of hydrology models to close the water cycle remains problematic, as the accuracy of precipitation provided by atmosphere models is, in most cases, insufficient to realistically simulate the runoff to the Baltic Sea without bias adjustments. Many regional stand-alone ocean and atmosphere models are tuned to suitably represent present-day climatologies rather than to accurately simulate climate change. Therefore, more research is required into how the regional climate sensitivity (e.g., the models' response to a given change in global mean temperature) is affected by coupling and how the spread is altered in multi-model and multi-scenario ensembles of coupled models compared with uncoupled ones.

Coupling physical understanding and statistical modeling to estimate ice jam flood frequency in the northern Peace-Athabasca Delta under climate change

The Peace-Athabasca Delta (PAD) of northeastern Alberta is one of the largest inland freshwater deltas in the world, laying at the confluence of the Peace and Athabasca Rivers. The PAD is internationally recognized as having a unique ecological and cultural significance, and periodic ice jam flooding from both rivers is an important feature of its current ecology. Past studies have debated whether a change in ice jam flood frequency on the Peace River has recently occurred, and what factors might be driving any perceived changes. This study contributes to this debate by addressing two questions: (1) what factors are most predictive of large Peace River ice jam floods, and (2) how might climate change impact large ice jam flood frequency? This work starts with a physically-based conceptual model of the necessary conditions for a large Peace River ice jam flood, and the factors that indicate whether those conditions are met. Logistic regression is applied to the historical flood record to determine which combination of hydroclimatic and riverine factors best predict large ice jam floods and the uncertainty in those relationships given the available data. Climatic factors including winter precipitation and temperature are most predictive of large Peace River ice jam floods. Other factors including winter river flows and freeze-up elevation provide little additional predictive power. The best logistic regression model is then forced with downscaled climate change scenarios from multiple climate models to project large ice jam flood frequency for a variety of plausible futures. Parametric uncertainty in the best logistic regression model is propagated into the projections using a parametric bootstrap to sample many plausible statistical models. Although there is variability across emissions scenarios and climate models, all projections indicate that the frequency of large Peace River ice jam floods is likely to decrease substantially in the coming decades, and that average waiting times between future large ice jam floods will likely surpass recent experience.

Climate change scenarios at hourly time‐step over Switzerland from an enhanced temporal downscaling approach

AbstractMany physically‐based models for climate change impact studies require sub‐daily temporal resolution of the forcing data to provide meaningful predictions. However, climate scenarios are typically available at daily time step, severely limiting the application of such physically‐based models. In this study, we propose an enhanced delta‐change method for downscaling climate change scenarios from daily to hourly resolution. The approach presented provides objective criteria for assessing the quality of the determined delta and downscaled time series, while also fixing issues of common quantile mapping methods used for spatial downscaling related to the decrease of correlation between different variables. However, this new approach has limitations in correctly representing statistically extreme events and changes in the frequency of discontinuous events such as precipitation. Smoothing of historical and future data is required prior to applying the delta‐change method, and the related parameters are found to have a subtle impact on the correctness of the representation of the seasonal means as well as the resulting (artificial) variability in the scenario data product. This new method is universal and can be applied with smoothing approaches apart from the harmonic fitting used in this work and in the past. In this study, the assessment suggested the use of seven harmonics for the smoothing of the input data as a best choice of this parameter for the data used. The method is applied to a Swiss climate change scenario data set, CH2018, and to a complement of this set to a Swiss alpine measurement network obtained by spatial transfer of CH2018, resulting in a set of 68 climate change scenarios at hourly resolution for 188 stations over Switzerland significantly expanding upon the spatial and temporal resolution of the CH2018 data set. All source code to perform such an analysis and the complete data product are provided open access.

Projection of irrigation water demand based on the simulation of synthetic crop coefficients and climate change

Abstract. In the context of major changes (climate, demography, economy, etc.), the southern Mediterranean area faces serious challenges with intrinsically low, irregular, and continuously decreasing water resources. In some regions, the proper growth both in terms of cropping density and surface area of irrigated areas is so significant that it needs to be included in future scenarios. A method for estimating the future evolution of irrigation water requirements is proposed and tested in the Tensift watershed, Morocco. Monthly synthetic crop coefficients (Kc) of the different irrigated areas were obtained from a time series of remote sensing observations. An empirical model using the synthetic Kc and rainfall was developed and fitted to the actual data for each of the different irrigated areas within the study area. The model consists of a system of equations that takes into account the monthly trend of Kc, the impact of yearly rainfall, and the saturation of Kc due to the presence of tree crops. The impact of precipitation change is included in the Kc estimate and the water budget. The anthropogenic impact is included in the equations for Kc. The impact of temperature change is only included in the reference evapotranspiration, with no impact on the Kc cycle. The model appears to be reliable with an average r2 of 0.69 for the observation period (2000–2016). However, different subsampling tests of the number of calibration years showed that the performance is degraded when the size of the training dataset is reduced. When subsampling the training dataset to one-third of the 16 available years, r2 was reduced to 0.45. This score has been interpreted as the level of reliability that could be expected for two time periods after the full training years (thus near to 2050). The model has been used to reinterpret a local water management plan and to incorporate two downscaled climate change scenarios (RCP4.5 and RCP8.5). The examination of irrigation water requirements until 2050 revealed that the difference between the two climate scenarios was very small (< 2 %), while the two agricultural scenarios were strongly contrasted both spatially and in terms of their impact on water resources. The approach is generic and can be refined by incorporating irrigation efficiencies.

Impact of Climate Change on Agricultural Droughts in Spain

Drought is an important natural hazard that is expected to increase in frequency and intensity as a consequence of climate change. This study aimed to evaluate the impact of future changes in the temperature and precipitation regime of Spain on agricultural droughts, using novel static and dynamic drought indices. Statistically downscaled climate change scenarios from the model HadGEM2-CC, under the scenario representative concentration pathway 8.5 (RCP8.5), were used at a total of 374 sites for the period 2006 to 2100. The evolution of static and dynamic drought stress indices over time show clearly how drought frequency, duration and intensity increase over time. Values of static and dynamic drought indices increase over time, with more frequent occurrences of maximum index values equal to 1, especially towards the end of the century (2071–2100). Spatially, the increase occurs over almost the entire area, except in the more humid northern Spain, and in areas that are already dry at present, which are located in southeast Spain and in the Ebro valley. This study confirms the potential of static and dynamic indices for monitoring and prediction of drought stress.

Desertification risk assessment in Northeast Brazil: Current trends and future scenarios

AbstractDesertification is one of the major environmental problems that humanity faces today, since it reduces the availability of ecosystem services, increases food insecurity and poverty, and affects the wellbeing of societies. The analysis of the process is extremely complex, since the phenomenon takes place at different temporal and spatial scales, influenced by different factors. It is widely recognized that land use/land cover change (LULC) is one of the main drivers of desertification since environmental degradation is always triggers by the removal of natural vegetation cover. In this study, a LULC change model was calibrated and validated using climate model simulations, demographic data and land susceptibility maps for the historical period 2000–2010. Then, desertification susceptibility of the Northeast of Brazil were estimated by integrating LULC trajectories with three different downscaled climate change scenarios and projection of population growth for the period 2015–2025, 2025–2035, and 2035–2045 for the emission scenarios RCP4.5 and RCP8.5. Results indicated that, between 2010 and 2035–2045, areas of moderate susceptibility decreased 10.34%, while areas of high susceptibility increased 12.28% in the case of the RCP4.5. For the RCP8.5 scenario, those numbers are −16.85% for moderate susceptibility, and +19.62% for high susceptibility. Among all indicators included in the analysis, land management was the main driver of desertification susceptibility, which indicates that mitigation and adaptation strategies for the region should pursue sustainable land use policies.

Assessment of the Impact of Potential Climate Change on the Surface Water of a Trans-boundary Basin: Case Study Yarmouk River

In this paper, the long-term hydrological response of a semi-arid trans-boundary basin to climate changes was analyzed. The basin is the Yarmouk River. The basin is located in a semi-arid region between Jordan and Syria. The methodology adopted is based on simulating the hydrological response of the basin under sstatistically downscaled climate change scenarios. For this goal, the physically process-based semi-distributed hydrological model Soil and Water Assessment Tool (SWAT) has been used. The impacts of climate change on YRB flow regime and water cycle components under pre-development and post-development conditions were evaluated by driving SWAT model under the projected 21st-century climate change scenarios. The scenarios applied were the SRES A1B and A2 and CMIP5 RCP 2.6, RCP 4.5 and RCP 8.5 that had been downscaled from the CGCM3 and CanESM2 climate variables output, respectively. These scenarios were downscaled by Statistical Downscaling Model (SDSM).The results showed that the YRB surface water is very vulnerable to climate change. Under the pre-development conditions, the impacts are believed to be at high risk. Under the post-development conditions, the impacts were of much less risk and proved the water harvesting is an efficient way for adaptation. The applied changes of temperature from +0.9 to +3.7 C˚ and of precipitation from +6% to -32% during the 21st century indicated a reduction in river flow ranges from -2.6% to 60% under the pre-development conditions and from -0% to -36.4% under post-development conditions. Such evaluation can help in enhancing the basin management, sustainable planning and building the adaptation schemes

Assessment of the Impacts of Climate Change on Climatic Zones over the Korean Peninsula

In assessing the impact of climate change, the use of a multimodel ensemble (MME) is required to quantify uncertainties between scenarios and produce downscaled outlines for the simulation of climate under the influence of different factors including topography. This study of climate change scenarios from 13 global climate models (GCMs) assesses the impacts of future climate change. Unlike South Korea, North Korea lacks studies using climate change scenarios of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and only recently did the country start the projection of extreme precipitation episodes. As such, one of the main purposes of this study is to predict changes in the average climatic conditions of North Korea in the future. The result of comparing downscaled climate change scenarios with observation data for a reference period indicates the high applicability of the MME. Furthermore, this study classifies climatic zones by applying the Köppen–Geiger climatic zones classification to the MME, which is validated for future precipitation and temperature. The result suggests that the continental climate that covers the inland area for the reference climate is expected to shift into the temperate climate. Moreover, the coefficient of variation (CV) in the temperature ensemble is particularly low for the southern coast of the Korean Peninsula, and, accordingly, a high possibility of the shifting climatic zone of the coast is predicted.

Hydropower from the Alpine Cryosphere in the Era of Climate Change: The Case of the Sabbione Storage Plant in Italy

Greenhouse gas reduction policies will have to rely as much as possible upon renewable, clean energy sources. Hydropower is a very good candidate, since it is the only renewable energy source whose production can be adapted to demand, and still has a large exploitation margin, especially in developing countries. However, in Europe the contribution of hydropower from the cold water in the mountain areas is at stake under rapid cryospheric down wasting under global warming. Italian Alps are no exception, with a large share of hydropower depending upon cryospheric water. We study here climate change impact on the iconic Sabbione (Hosandorn) glacier, in the Piemonte region of Italy, and the homonymous reservoir, which collects water from ice melt. Sabbione storage plant has operated since 1953 and it was, until recently, the highest altitude dam of Europe at 2460 m asl, and the former second largest dam of the Alps with 44 Mm3. We use two models, namely Poly-Hydro and Poly-Power, to assess (i) present hydrological budget and components (i.e., ice/snow melt, rainfall), and (ii) hydropower production under optimal reservoirs’ management, respectively. We then project forward hydrological cycle including Sabbione glacier’s fate, under properly downscaled climate change scenarios (three General Circulation Models, three Representative Concentration Pathways, nine scenarios overall) from IPCC until 2100, and we assess future potential for hydropower production under the reservoir’s re-operation. Mean annual discharge during 2000–2017 is estimated at 0.90 m3 s−1, with ice melt contribution of ca. 11.5%, and ice cover as measured by remote sensing changing from 4.23 km2 in 2000 to 2.94 km2 in 2017 (−30%). Mean hydropower production during 2005–2017 is estimated as 46.6 GWh. At the end of the century ice covered area would be largely depleted (0–0.37 km2), and ice melt contribution would drop largely over the century (0%–10%, 5% on average at half century, and null in practice at the end of century). Therefore, decreased ice cover, and uncertain patterns of changing precipitation, would combine to modify the future stream fluxes (−22% to −3%, −10% on average at half century, and −28% to 1%, average −13%, at the end of century). Power production, driven by seasonal demand and water availability, would change (decrease) in the future (−27% to −8%, −15% on average at half century, and −32% to −5%, −16% at the end of century). Our results demonstrate potential for decrease of cold water in this area, paradigmatic of the present state of hydropower in the Alps, and subsequent considerable hydropower losses under climate change, and claim for adaptation measures therein.

Projected Changes in the Frequency of Peak Flows along the Athabasca River: Sensitivity of Results to Statistical Methods of Analysis

Flows originating from alpine dominated cold region watersheds typically experience extended winter low flows followed by spring snowmelt and summer rainfall driven high flows. In a warmer climate, there will be a temperature-induced shift in precipitation from snowfall towards rain along with changes in precipitation intensity and snowmelt timing, resulting in alterations in the frequency and magnitude of peak flow events. This study examines the potential future changes in the frequency and severity of peak flow events in the Athabasca River watershed in Alberta, Canada. The analysis is based on simulated flow data by the variable infiltration capacity (VIC) hydrologic model driven by statistically downscaled climate change scenarios from the latest coupled model inter-comparison project (CMIP5). The hydrological model projections show an overall increase in mean annual streamflow in the watershed and a corresponding shift in the freshet timing to an earlier period. The river flow is projected to experience increases during the winter and spring seasons and decreases during the summer and early fall seasons, with an overall projected increase in peak flow, especially for low frequency events. Both stationary and non-stationary methods of peak flow analysis, performed at multiple points along the Athabasca River, show that projected changes in the 100-year peak flow event for the high emissions scenario by the 2080s range between 4% and 33% depending on the driving climate models and the statistical method of analysis. A closer examination of the results also reveals that the sensitivity of projected changes in peak flows to the statistical method of frequency analysis is relatively small compared to that resulting from inter-climate model variability.

Projections of water, carbon, and nitrogen dynamics under future climate change in an old-growth Douglas-fir forest in the western Cascade Range using a biogeochemical model

Statistically downscaled climate change scenarios from four General Circulation Models for two Representative Concentration Pathways (RCP) were applied as inputs to a biogeochemical model, PnET-BGC, to examine potential future dynamics of water, carbon, and nitrogen in an old-growth Douglas-fir forest in the western Cascade Range. Projections show 56% to 77% increases in stomatal conductance throughout the year from 1986–2010 to 2076–2100, and 65% to 104% increases in leaf carbon assimilation between October and June over the same period. However, future dynamics of water and carbon under the RCP scenarios are affected by a 49% to 86% reduction in foliar biomass resulting from severe air temperature and humidity stress to the forest in summer. Important implications of future decreases in foliar biomass include 1) 20% to 71% decreases in annual transpiration which increase soil moisture by 7% to 15% in summer and fall; 2) decreases in photosynthesis by 77% and soil organic matter by 62% under the high radiative forcing scenario; and 3) altered foliar and soil carbon to nitrogen stoichiometry. Potential carbon dioxide fertilization effects on vegetation are projected to 1) amplify decreases in transpiration by 4% to 9% and increases in soil moisture in summer and fall by 1% to 2%; and 2) alleviate decreases in photosynthesis by 4%; while 3) having negligible effects on the dynamics of nitrogen. Our projections suggest that future decrease in transpiration and moderate water holding capacity may mitigate soil moisture stress to the old-growth Douglas-fir forest. Future increases in nitrogen concentration in soil organic matter are projected to alleviate the decrease in net nitrogen mineralization despite a reduction in decomposition of soil organic matter by the end of the century.

Dynamic adaptive pathways in downscaled climate change scenarios

The parallel scenario process enables characterization of climate-related risks and response options to climate change under different socio-economic futures and development prospects. The process is based on representative concentration pathways, shared socio-economic pathways, and shared policy assumptions. Although this scenario architecture is a powerful tool for evaluating the intersection of climate and society at the regional and global level, more specific context is needed to explore and understand risks, drivers, and enablers of change at the national and local level. We discuss the need for a stronger recognition of such national-scale characteristics to make climate change scenarios more relevant at the national and local scale, and propose ways to enrich the scenario architecture with locally relevant details that enhance salience, legitimacy, and credibility for stakeholders. Dynamic adaptive pathways are introduced as useful tools to draw out which elements of a potentially infinite scenario space connect with decision-relevant aspects of particular climate-related and non-climate-related risks and response options. Reviewing adaptation pathways for New Zealand case studies, we demonstrate how this approach could bring the global-scale scenario architecture within reach of local-scale decision-making. Such a process would enhance the utility of scenarios for mapping climate-related risks and adaptation options at the local scale, involving appropriate stakeholder involvement.

A modeling framework to assess the impact of climate change on a river runoff

Global climate change is anticipated to have consequences on water resources and the envi-ronment both at global and local/regional levels. Efforts towards proper management of future water resources and resolving potential water-related conflicts require the formulation of appropriate techniques to downscale the output of global climate models (GCM) to local conditions for hydrologic prediction. The paper presents an integrated framework for modeling the impact of climate change on river runoff that combines methodology for downscaling climate change scenarios for a basin scale with a hydrological model to estimate the impact of climate change on a river runoff. The modeling framework uses long-term observations of meteorological and hydrological variables together with a climate change scenario to provide a projection of future flows for the specified time horizon. The framework is based on a spatial weather generator and a distributed rainfall-runoff model. Such a configuration enables a reflection of the uncertainty of future conditions by running multiple realizations of future conditions, and also take into account the spatial variability of hydrological properties in the catchment by maintaining the physical details at a given grid size. The performance of the framework was presented for the Kaczawa basin that is one of the main left bank tributaries of the Odra River – the second biggest river in Poland. The results show simulated changes of the future river flow regime caused by climatic changes for two time horizons: 2040 and 2080.

Risks of summertime extreme thermal conditions in buildings as a result of climate change and exacerbation of urban heat islands

This study explores the role of global and local warming on indoor thermal environments of representative buildings in two warm climate cities in the U.S. (Chicago IL, and Houston TX). It uses downscaled climate change scenarios to drive whole-building model simulations of representative apartment buildings. Simulations were conducted under (a) current conditions; (b) conditions that include a global warming effect; and (c) conditions that include global warming with concurrent intensification of the urban heat island. Building thermal conditions are assessed for typical operating conditions, for conditions associated with failure of cooling equipment, and for complete power loss during a heat wave.Simulations show that warming by itself may have minimal effects on indoor thermal comfort in summer. For example, in Houston the Predicted Percent Dissatisfied (PPD) comfort metric was approximately 5–6% for current and future climate scenarios under normal operating conditions. Under conditions of AC failure, however, this increased to 61.9% for the current climate and 71.4% for the 2050 climate. In the case of Chicago PPD was between 6.2% and 7.9% for all climate scenarios when equipment operated normally. Under conditions of equipment failure, however, PPD increased to 34.1% for the current climate and 39.2% for the 2050 climate. In simulations for both cities, a complete power failure resulted in peak temperatures that were approximately 2 °C cooler than the case of AC failure only. This is due to reduction in internal gains during a power blackout.

The uncertainty associated with estimating future groundwater recharge: A summary of recent research and an example from a small unconfined aquifer in a northern humid-continental climate

Summary Global climate models (GCMs) project significant changes to regional and globally-averaged precipitation and air temperature, and these changes will likely have an associated impact on groundwater recharge. A common approach in recent climate change-impact studies is to employ multiple downscaled climate change scenarios to drive a hydrological model and project an envelope of recharge possibilities. However, each step in this process introduces variability into the hydrological results, which translates to uncertainty in the future state of groundwater resources. In this contribution, seven downscaled future climate scenarios for a northern humid-continental climate in eastern Canada were generated from selected combinations of GCMs, emission scenarios, and downscaling approaches. Meteorological data from the climate scenarios and field data from a small unconfined aquifer were used to estimate groundwater recharge with the soil water balance model HELP3. HELP3 simulations for the period 2046–2065 indicated that projected recharge was most sensitive to the selected downscaling/debiasing algorithm and GCM. Projected changes in average annual recharge varied from an increase of 58% to a decrease of 6% relative to the 1961–2000 reference period. Such a large range in projected recharge provides very little useful information regarding the future state of groundwater resources. Additional results from recent comparable studies are compiled and discussed. Based on the results obtained from the present case study and the other studies reviewed, the limitations of current approaches for projecting future recharge are identified, and several suggestions for research opportunities to advance this field are offered.

Downscaled Climate Change Scenarios for Baja California and the North American Monsoon during the Twenty-First Century

Abstract Regional climate change scenarios for Baja California/Southern California (BCC) and the North American monsoon (NAM) were produced as part of the Baja California State Climate Change Action Program (PEACC-BC). Bias-corrected and spatially downscaled scenarios (BCSD) from six general circulation models (GCMs) with a total of 12 realizations were analyzed for two scenarios of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES): B1 (low emissions) and A2 (high emissions) during the twenty-first century. A validation of the original GCM realizations and the BCSD scenarios with observed data during 1961–90 show that the ensemble GCM produces too much precipitation during autumn and winter, which could be the cause of the observed delay of the summer monsoon rains; the ensemble BCSD considerably improves the mean annual cycles and spatial distributions of precipitation and temperature in the region. However, both ensembles greatly underestimate the observed interannual variability of precipitation. BCSD scenarios of temperature and precipitation during the twenty-first century were evaluated on the basis of the multimodel median change relative to 1961–90. The scenarios of precipitation change show large interannual variations and larger uncertainties than the scenarios of temperature change. The A2 scenarios show the largest reductions of precipitation in the last 20 yr of the twenty-first century; a decrease of 30% is projected for BCC mainly in winter and spring, while precipitation in the NAM region could be weakened by 20% during winter, spring, and summer. After 2050, a significant reduction of precipitation is expected in northwestern Mexico and the southwestern United States south of 35°N, and temperature changes larger than 2°C warming.

Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

Abstract. As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously non-affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology, depending on actual weather conditions, and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980–2009 and 2045–2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the ENSEMBLES multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045–2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1% on average today to over 60% in the future for the median climate change signal of the multi-model projections), the actual risk will critically depend on the pace of the adaptation of the codling moth with respect to the critical photoperiod. To control this additional generation, an intensification and prolongation of control measures (e.g. insecticides) will be required, implying an increasing risk of pesticide resistances. For fire blight, the projected changes in infection days are less certain due to uncertainties in the leaf wetness approximation and the simulation of the blooming period. Two compensating effects are projected, warmer temperatures favoring infections are balanced by a temperature-induced advancement of the blooming period, leading to no significant change in the number of infection days under future climate conditions for most stations.

Evaluation of a Weather Generator-Based Method for Statistically Downscaling Non-Stationary Climate Scenarios for Impact Assessment at a Point Scale

Non-stationarity is a major concern for statistically downscaling climate change scenarios for impact assessment. This study evaluates whether a statistical downscaling method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zones. Ten stations were selected from polar to tropical climates around the world. The measured data were split into calibration and validation periods in such a way that the difference of the mean annual precipitation between the two periods was maximized. Transition probabilities of wet-following-wet (Pw/w) and wet-following-dry (Pw/d) days generally increased linearly with an increase in mean monthly precipitation for all calendar months and locations in all climatic zones. The transition probabilities of the validation periods, interpolated with linear regressions, agreed well with those directly calculated from the observed data of the periods, with model efficiency ranging from 0.786 to 0.966. Due to good estimation of Pw/w and Pw/d, generated frequency distributions of dry and wet spell lengths agreed reasonably well with the measured distributions for the validation period. Overall, statistics of the downscaled daily and monthly precipitation amounts, annual maximum daily amounts, and dry and wet spells were similar to those of the measured data for stations whose skewness coefficients were not greater than 3.5, suggesting that caution be exercised when generating daily precipitation with the Pearson type III distribution if the skewness coefficient is greater than 3.5. This downscaling method can be easily used with the two-parameter gamma distribution for daily precipitation to circumvent the skewness issue, if necessary. This study has demonstrated that the downscaling method can generate proper daily precipitation series for climates having non-stationary changes.

Climate change vulnerability and adaptive capacity in the agricultural sector in Northern Norway

As a primary industry, agriculture is directly dependent on natural conditions and therefore potentially vulnerable to the consequences of climate change. In Norway and Northern Norway in particular, the future climatic changes are expected to be overall positive. Still, the consequences for agriculture are not straightforward, but dependent on the interaction between different weather and biological elements, as well as political, economic and social conditions. In this interdisciplinary study we have assessed biological and agronomic effects of climate change, and their interaction with political, economic and social factors, to identify farmers' vulnerability and adaptive capacity to climate change. The assessments are based on downscaled climate change scenarios and interviews with local farmers in the three northernmost counties in Northern Norway (latitude 65.5° to 70°). The study shows that the farmers to a degree are vulnerable to a changing climate, not mainly because of the direct effects of changing growing conditions, but because these changes are an added factor to an already tenuous situation created by Norwegian agricultural policy and socio-economic development in general. We have found that farmers are highly adaptive, to both changing growing conditions and changing agricultural policies. However, changes in policy are currently a greater challenge to farmers than climate change, and such changes are therefore a more salient driver of vulnerability.