Mid-twenty-first century warm season climate change in the Central United States. Part I: regional and global model predictions

A regional climate model (RCM) constrained by future anomalies averaged from atmosphere–ocean general circulation model (AOGCM) simulations is used to generate mid-twenty-first century climate change predictions at 30-km resolution over the central U.S. The predictions are compared with those from 15 AOGCM and 7 RCM dynamic downscaling simulations to identify common climate change signals. There is strong agreement among the multi-model ensemble in predicting wetter conditions in April and May over the northern Great Plains and drier conditions over the southern Great Plains in June through August for the mid-twenty-first century. Projected changes in extreme daily precipitation are statistically significant over only a limited portion of the central U.S. in the RCM constrained with future anomalies. Projected changes in monthly mean 2-m air temperature are generally consistent across the AOGCM ensemble average, North American Regional Climate Change Assessment Program RCM ensemble average, and RCM constrained with future anomalies, which produce a maximum increase in August of 2.4–2.9 K over the northern and southern Great Plains and Midwest. Changes in extremes in daily 2-m air temperature from the RCM downscaled with anomalies are statistically significant over nearly the entire Great Plains and Midwest and indicate a positive shift in the warm tail of the daily 2-m temperature distribution that is larger than the positive shift in the cold tail.     

Ensemble projections for wine production in the Douro Valley of Portugal

Wine production is largely governed by atmospheric conditions, such as air temperature and precipitation, together with soil management and viticultural/enological practices. Therefore, anthropogenic climate change is likely to have important impacts on the winemaking sector worldwide. An important winemaking region is the Portuguese Douro Valley, which is known by its world-famous Port Wine. The identification of robust relationships between atmospheric factors and wine parameters is of great relevance for the region. A multivariate linear regression analysis of a long wine production series (1932–2010) reveals that high rainfall and cool temperatures during budburst, shoot and inflorescence development (February-March) and warm temperatures during flowering and berry development (May) are generally favourable to high production. The probabilities of occurrence of three production categories (low, normal and high) are also modelled using multinomial logistic regression. Results show that both statistical models are valuable tools for predicting the production in a given year with a lead time of 3–4 months prior to harvest. These statistical models are applied to an ensemble of 16 regional climate model experiments following the SRES A1B scenario to estimate possible future changes. Wine production is projected to increase by about 10 % by the end of the 21^st century, while the occurrence of high production years is expected to increase from 25 % to over 60 %. Nevertheless, further model development will be needed to include other aspects that may shape production in the future. In particular, the rising heat stress and/or changes in ripening conditions could limit the projected production increase in future decades.     

Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases

A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future “observed” extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO₂. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the A1B future emissions scenario to obtain projections of the 50 year return values for the 20 year period centred on 2050. We obtain 10th to 90th percentile ranges of 35.9–42.1 °C for summer daily maximum temperature, 35.5–52.4 mm for summer daily rainfall and 79.2, 97.0 mm for autumn 5 day total rainfall, compared to observed estimates for 1961–1990 of 35.7 °C, 42.1 and 78.4 mm respectively.     

Climate change impacts on forest fire potential in boreal conditions in Finland

The aim of this work was to study the forest fire potential and frequency of forest fires under the projected climate change in Finland (N 60°–N 70°). Forest fire index, generally utilized in Finland, was used as an indicator for forest fire potential due to climatological parameters. Climatic scenarios were based on the A2 emission scenario. According to the results, the forest fire potential will have increased by the end of this century; as a result of increased evaporative demand, which will increase more than the rise in precipitation and especially in southern Finland. The annual number of forest fire alarm days is expected to increase in southern Finland to 96–160 days by the end of this century, compared to the current 60–100 days. In the north, the corresponding increase was from 30 to 36 days. The expected increase in the annual frequency of forest fires over the whole country was about 20% by the end of this century compared to the present day. The greatest increase in the frequency of fires, per 1,000 km², was in the southernmost part of the country, with six to nine fires expected annually per 1,000 km² at the end of this century, meaning a 24–29% increase compared to the present day frequencies.     

High resolution regional climate model simulations for Germany: Part II—projected climate changes

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971–2000) and future A1B scenario (2021–2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of ?2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.     

Variations in cereal crop phenology in Spain over the last twenty-six years (1986–2012)

Over recent years, the Iberian Peninsula has witnessed an increase both in temperature and in rainfall intensity, especially in the Mediterranean climate area. Plant phenology is modulated by climate, and closely governed by water availability and air temperature. Over the period 1986–2012, the effects of climate change on phenology were analyzed in five crops at 26 sites growing in Spain (southern Europe): oats, wheat, rye, barley and maize. The phenophases studied were: sowing date, emergence, flag leaf sheath swollen, flowering, seed ripening and harvest. Trends in phenological response over time were detected using linear regression. Trends in air temperature and rainfall over the period prior to each phenophase were also charted. Correlations between phenological features, biogeographical area and weather trends were examined using a Generalized Lineal Mixed Model approach. A generalized advance in most winter-cereal phenophases was observed, mainly during the spring. Trend patterns differed between species and phenophases. The most noticeable advance in spring phenology was recorded for wheat and oats, the “Flag leaf sheath swollen” and “Flowering date” phenophases being brought forward by around 3 days/year and 1 day/year, respectively. Temperature changes during the period prior to phenophase onset were identified as the cause of these phenological trends. Climate changes are clearly prompting variations in cereal crop phenology; their consequences could be even more marked if climate change persists into the next century. Changes in phenology could in turn impact crop yield; fortunately, human intervention in crop systems is likely to minimize the negative impact.     

Projected change in East Asian summer monsoon precipitation under RCP scenario

Future changes in East Asian summer monsoon precipitation climatology, frequency, and intensity are analyzed using historical climate simulations and future climate simulations under the RCP4.5 scenario using the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project 5 (CMIP5) multi-model dataset. The model reproducibility is evaluated, and well performance in the present-day climate simulation can be obtained by most of the studied models. However, underestimation is obvious over the East Asian region for precipitation climatology and precipitation intensity, and overestimation is observed for precipitation frequency. The overestimation of precipitation frequency is mainly due to the large positive bias of the light precipitation (precipitation <10 mm/day) days, and the underestimation of precipitation intensity is mainly caused by the negative bias of the intense precipitation (precipitation >10 mm/day) intensity. For the future climate simulations, simple multi-model ensemble (MME) averages using all of the models show increases in precipitation and its intensity over almost all of East Asia, while the precipitation frequency is projected to decrease over eastern China and around Japan and increase in other regions. When the weighted MME is considered, no large difference can be observed compared with the simple MME. For the MME using the six best models that have good performance in simulating the present-day climate, the future climate changes over East Asia are very similar to those predicted using all of the models. Further analysis shows that the frequency and intensity of intense precipitation events are also projected to significantly increase over East Asia. Increases in precipitation frequency and intensity are the main contributors to increases in precipitation, and the contribution of frequency increases (contributed by 40.8 % in the near future and by 58.9 % by the end of the twenty-first century) is much larger than that of intensity increases (contributed by 29.9 % in the near future and by 30.1 % by the end of the twenty-first century). This finding also implies an increased risk of intense precipitation events over the East Asian region under global warming scenario. These results regarding future climate simulations show much greater reliability than those using CMIP3 simulations.     

Combining climatological and participatory approaches for assessing changes in extreme climatic indices at regional scale

This paper combines the climatological and societal perspectives for assessing future climatic extremes over Kangasabati River basin in India using an ensemble of four high resolution (25 km) regional climate model (RCM) simulations from 1970 to 2050. The relevant extreme indices and their thresholds are defined in consultation with stakeholders and are then compared using RCM simulations. To evaluate the performance of RCM in realistically representing atmospheric processes in the basin, model simulations driven with ERAInterim global re-analysis data from 1989 to 2008 are compared with observations. The models perform well in simulating seasonality, interannual variability and climatic extremes. Future climatic extremes are evaluated based on RCM simulations driven by GCMs, for present (1970–1999) and for the SRES A1B scenario for future (2021–2050) period. The analysis shows an intensification of majority of extremes as projected by future ensemble mean. The study suggests that there is a marked consistency in stakeholder observed changes in climate extremes and future predicted trends.     

The atmospheric component of the Mediterranean Sea water budget in a WRF multi-physics ensemble and observations

The use of high resolution atmosphere–ocean coupled regional climate models to study possible future climate changes in the Mediterranean Sea requires an accurate simulation of the atmospheric component of the water budget (i.e., evaporation, precipitation and runoff). A specific configuration of the version 3.1 of the weather research and forecasting (WRF) regional climate model was shown to systematically overestimate the Mediterranean Sea water budget mainly due to an excess of evaporation (~1,450 mm yr^?1) compared with observed estimations (~1,150 mm yr^?1). In this article, a 70-member multi-physics ensemble is used to try to understand the relative importance of various sub-grid scale processes in the Mediterranean Sea water budget and to evaluate its representation by comparing simulated results with observed-based estimates. The physics ensemble was constructed by performing 70 1-year long simulations using version 3.3 of the WRF model by combining six cumulus, four surface/planetary boundary layer and three radiation schemes. Results show that evaporation variability across the multi-physics ensemble (～10 % of the mean evaporation) is dominated by the choice of the surface layer scheme that explains more than ～70 % of the total variance and that the overestimation of evaporation in WRF simulations is generally related with an overestimation of surface exchange coefficients due to too large values of the surface roughness parameter and/or the simulation of too unstable surface conditions. Although the influence of radiation schemes on evaporation variability is small (～13 % of the total variance), radiation schemes strongly influence exchange coefficients and vertical humidity gradients near the surface due to modifications of temperature lapse rates. The precipitation variability across the physics ensemble (～35 % of the mean precipitation) is dominated by the choice of both cumulus (～55 % of the total variance) and planetary boundary layer (～32 % of the total variance) schemes with a strong regional dependence. Most members of the ensemble underestimate total precipitation amounts with biases as large as 250 mm yr^?1 over the whole Mediterranean Sea compared with ERA Interim reanalysis mainly due to an underestimation of the number of wet days. The larger number of dry days in simulations is associated with a deficit in the activation of cumulus schemes. Both radiation and planetary boundary layer schemes influence precipitation through modifications on the available water vapor in the boundary layer generally tied with changes in evaporation.     

Simulating the impacts of climate change on cotton production in India

General circulation models (GCMs) project increases in the earth’s surface air temperatures and other climate changes by the mid or late 21st century, and therefore crops such as cotton (Gossypium spp L.) will be grown in a much different environment than today. To understand the implications of climate change on cotton production in India, cotton production to the different scenarios (A2, B2 and A1B) of future climate was simulated using the simulation model Infocrop-cotton. The GCM projections showed a nearly 3.95, 3.20 and 1.85 °C rise in mean temperature of cotton growing regions of India for the A2, B2 and A1B scenarios, respectively. Simulation results using the Infocrop-cotton model indicated that seed cotton yield declined by 477 kg?ha^?1 for the A2 scenario and by 268 kg?ha^?1 for the B2 scenario; while it was non-significant for the A1B scenario. However, it became non-significant under elevated [CO₂] levels across all the scenarios. The yield decline was higher in the northern zone over the southern zone. The impact of climate change on rainfed cotton which covers more than 60 % of the country’s total cotton production area (mostly in the central zone) and is dependent on the monsoons is likely to be minimum, possibly on account of marginal increase in rainfall levels. Results of this assessment suggest that productivity in northern India may marginally decline; while in central and southern India, productivity may either remain the same or increase. At the national level, therefore, cotton production is unlikely to change with climate change. Adaptive measures such as changes in planting time and more responsive cultivars may further boost cotton production in India.     

How is CO2 affecting yields and technological progress? A statistical analysis

This paper analyzes the impact of climate, crop production technology, and atmospheric carbon dioxide (CO₂) on current and future crop yields. The analysis of crop yields endeavors to advance the literature by estimating the effect of atmospheric CO₂ on observed crop yields. This is done using an econometric model estimated over pooled historical data for 1950–2009 and data from the free air CO₂ enrichment experiments. The main econometric findings are: 1) Yields of C3 crops (soybeans, cotton, and wheat) directly respond to the elevated CO₂, while yields of C4 crops (corn and sorghum) do not, but they are found to indirectly benefit from elevated CO₂ in times and places of drought stress; 2) The effect of technological progress on mean yields is non-linear; 3) Ignoring atmospheric CO₂ in an econometric model of crop yield likely leads to overestimates of the pure effects of technological progress on crop yields of about 51, 15, 17, 9, and 1 % of observed yield gain for cotton, soybeans, wheat, corn and sorghum, respectively; 4) Average climate conditions and climate variability contribute in a statistically significant way to average crop yields and their variability; and 5) The effect of CO₂ fertilization generally outweighs the effect of climate change on mean crop yields in many regions resulting in an increase of 7–22, 4–47, 5–26, 65–96, and 3–35 % for yields of corn, sorghum, soybeans, cotton, and wheat, respectively.     

Climate change impacts on regional rice production in China

Rice (Oryza sativa L.) production is an important contributor to China’s food security. Climate change, and its impact on rice production, presents challenges in meeting China’s future rice production requirements. In this study, we conducted a comprehensive analysis of how rice yield responds to climate change under different scenarios and assessed the associated simulation uncertainties of various regional-scale climate models. Simulation was performed based on a regional calibrated crop model (CERES-Rice) and spatially matched climatic (from 17 global climate models), soil, management, and cultivar parameters. Grain-filling periods for early rice were shortened by 2–7 days in three time slices (2030s, 2050s, and 2070s), whereas grain-filling periods for late rice were shortened by 10–19 days in three time slices. Most of the negative effects of climate change were predicted to affect single-crop rice in central China. Average yields of single-crop rice treated with CO₂ fertiliser in central China were predicted to be reduced by 10, 11, and 11% during the 2030s, 2050s, and 2070s, respectively, compared to the 2000s, if planting dates remained unchanged. If planting dates were optimised, single-crop rice yields were predicted to increase by 3, 7, and 11% during the 2030s, 2050s, and 2070s, respectively. In response to climate changes, early and single-crop rice should be planted earlier, and late rice planting should be delayed. The predicted net effect would be to prolong the grain-filling period and optimise rice yield.     

Improved confidence in climate change projections of precipitation further evaluated using daily statistics from ENSEMBLES models

Probability density functions for daily precipitation data are used as a validation tool comparing station measurements to seven transient regional climate model runs, with a horizontal resolution of 25 km and driven by the SRES A1B scenario forcing, within the ENSEMBLES project. The validation is performed for the control period 1961–1990 for eight predefined European subregions, and a ninth region enclosing all eight subregions, with different climate characteristics. Models that best match the observations are then used for making climate change projections of precipitation distributions during the twenty-first century for each subregion separately. We find, compared to the control period, a distinct decrease in the contribution to the total precipitation for days with moderate precipitation and a distinct increase for days with more intense precipitation. This change in contribution to the total precipitation is found to amplify with time during all of the twenty-first century with an average rate of 1.1% K^?1. Furthermore, the crossover point separating the decreasing from the increasing contributions does not show any significant change with time for any specific subregion. These results are a confirmation and a specification of the results from a previous study using the same station measurements but with a regional climate model ensemble within the PRUDENCE project.     

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.     

Projection of heat waves over China for eight different global warming targets using 12 CMIP5 models

Simulation and projection of the characteristics of heat waves over China were investigated using 12 CMIP5 global climate models and the CN05.1 observational gridded dataset. Four heat wave indices (heat wave frequency, longest heat wave duration, heat wave days, and high temperature days) were adopted in the analysis. Evaluations of the 12 CMIP5 models and their ensemble indicated that the multi-model ensemble could capture the spatiotemporal characteristics of heat wave variation over China. The inter-decadal variations of heat waves during 1961–2005 can be well simulated by multi-model ensemble. Based on model projections, the features of heat waves over China for eight different global warming targets (1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, and 5.0 °C) were explored. The results showed that the frequency and intensity of heat waves would increase more dramatically as the global mean temperature rise attained higher warming targets. Under the RCP8.5 scenario, the four China-averaged heat wave indices would increase from about 1.0 times/year, 2.5, 5.4, and 13.8 days/year to about 3.2 times/year, 14.0, 32.0, and 31.9 days/year for 1.5 and 5.0 °C warming targets, respectively. Those regions that suffer severe heat waves in the base climate would experience the heat waves with greater frequency and severity following global temperature rise. It is also noteworthy that the areas in which a greater number of severe heat waves occur displayed considerable expansion. Moreover, the model uncertainties exhibit a gradual enhancement with projected time extending from 2006 to 2099.     

Agrometeorological study of crop drought vulnerability and avoidance in northeast of Iran

Drought is one of the crucial environmental factors affecting crop production. Synchronizing crop phenology with expected or predicted seasonal soil moisture supply is an effective approach to avoid drought impact. To assess the potential for drought avoidance, this study investigated the long-term climate data of four locations (Bojnourd, Mashhad, Sabzevar, and Torbat Heydarieh) in Khorasan province, in the northeast of Iran, with respect to the four dominant crops (common bean, lentil, peanut, and potato). Weekly water deficit defined as the difference between weekly precipitation and weekly potential evapotranspiration was calculated. Whenever the weekly water deficit was larger than the critical water demand of a crop, the probability for drought was determined. Results showed that Sabzevar has the highest average maximum temperature (24.6 °C), minimum temperature (11.7 °C), weekly evapotranspiration (32.1 mm), and weekly water deficit (28.3 mm) and has the lowest average weekly precipitation (3.8 mm). However, the lowest mean maximum temperature (19.7 °C), minimum temperature (6.9 °C), weekly evapotranspiration (22.5 mm), and weekly water deficit (17.5 mm) occur in Bojnourd. This location shows the shortest period of water deficit during the growing season for all crops except potato, which also experienced drought at the end of the growing season. Sabzevar and Torbat Heydarieh experienced the highest probability of occurrence and longest duration of drought during the growing season for all crops. The result of this study will be helpful for farmers in order to reduce drought impact and enable them to match crop phenology with periods during the growing season when water supply is more abundant.     

Adaptation to climate change impacts on urban storm water: a case study in Arvika, Sweden

Already today, the functionality of many sewer and storm water systems are not up to the required standards and consequently flooding problems are experienced in case of heavy storms. System upgrades are required, which are however complicated by the expected future increase in short-term rainfall intensities as a result of climate change. In this case study, focusing on the town of Arvika, Sweden, this issue is investigated in three main steps. In the first, extreme value analyses of 30-min rainfall from an ensemble of climate projections are carried out to estimate the future increase and generate a future design storm. In the second, the existing system’s response to both today’s and future design storms are simulated by a coarse sewer model setup (MOUSE) and a detailed coupled surface-sewer model setup (TSR). In the third and final step, system upgrades are designed and evaluated by both models. The results indicate an increase by 10–30 % of today’s short-term rainfall extremes by the end of the century. Upgrading the system to achieve a satisfactory performance for the future design storm would cost approximately twice as much as an upgrade based on today’s design storm.     

Smallholders adaptation to climate change in Mali

This study was undertaken to assess the potential impacts of climate change on agriculture in the Sikasso region of southern Mali, as part of an effort by the U.S. Agency for International Development (USAID) to integrate climate change adaptation considerations into their development projects. The region is considered to be the breadbasket of Mali, providing a substantial amount of the country’s food supplies as well as cotton for exchange earnings. The project had two components: modeling how climate change could affect production of cereal and cash crops in southern Mali; and conducting a stakeholder-driven vulnerability and adaptation assessment to identify potential options for addressing current and projected risks to agriculture from climate change. Projected changes in crop yields were based on a previous analysis that was extended for the purposes of this study. The projections suggested that the sensitivity of maize to changing weather conditions is relatively small (generally less than 10% change) under both dry and wet scenarios in 2030 and 2060. White (Irish) potatoes, the primary cash crop, are the most sensitive to changing weather conditions, with yields decreasing under both dry and wet conditions; yields could decrease by about 25% by 2060. Stakeholder workshops, field interviews, and an expert analysis were used to assess current and future climate-related vulnerability and to identify potential adaptation options. The main focus of the assessment was farmers in a village of about 3,000 people in the Sikasso region that practiced a rice-potato rotation system typical to the region. The farmers emphasized adaptation measures that require outside financial and technical assistance, for example installation of a water gate that would retain more water in the inland valley and increase the water table to flood rice fields during the rainy season and for furrow irrigation of potatoes during the dry season. Adaptations emphasized by both the farmers and representatives of regional technical services were crop diversification and germplasm improvement; soil and water management; access to equipment (plows, carts, oxen, and improved stoves); credit stockage villageois (CSV); and fertilizer.     

On the future reduction of snowfall in western and central Europe

Large parts of western and central Europe face a 20–50 % future reduction in snowfall on Hellmann days (days with daily-mean temperatures below freezing). This strong reduction occurs in addition to the expected 75 % decrease of the number of Hellmann days near the end of the twenty first century. The result is insensitive to the exact freezing-level threshold, but is in sharp contrast with the winter daily precipitation, which increases under most global warming scenarios. Not only climate model simulations show this. Observational records also reveal that probabilities for precipitation on Hellmann days have been larger in the past. The future reduction is a consequence of the freezing-level threshold becoming a more extreme quantile of the temperature distribution in the future. Only certain circulation types permit these quantiles to be reached, and it is shown that these have intrinsically low precipitation probability.     

Sea water surface energy balance in the Arctic fjord (Hornsund,SW Spitsbergen) in May–November 2014

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.