Forests play a key role in humanity's current challenge to mitigate climate change thanks to their capacity to sequester carbon. Preserving and expanding forest cover is considered essential to ...enhance this carbon sink. However, changing the forest cover can further affect the climate system through biophysical effects. One such effect that is seldom studied is how afforestation can alter the cloud regime, which can potentially have repercussions on the hydrological cycle, the surface radiation budget and on planetary albedo itself. Here we provide a global scale assessment of this effect derived from satellite remote sensing observations. We show that for 67% of sampled areas across the world, afforestation would increase low level cloud cover, which should have a cooling effect on the planet. We further reveal a dependency of this effect on forest type, notably in Europe where needleleaf forests generate more clouds than broadleaf forests.
Abstract
Seasonal climate forecasts are a key component of sectoral climate services. Skill and reliability in predicting agro-climate indicators, co-designed with and for European wheat farmers, are ...here assessed. The main findings show how seasonal climate forecast provides useful information for decision-making processes in the European winter wheat-producing sector. Flowering time can be reliably predicted already at the beginning of the growing season in central and eastern Europe, thus supporting effective variety selection and timely planning of agro-management practices. The predictability of climate events relevant for winter wheat production is strongly dependent on the forecast initialization time as well as the nature of the event being predicted. Overall, regionally skillful and reliable predictions of drought events during the sensitive periods of wheat flowering and grain filling can be made already at the end of winter. On the contrary, predicting excessive wetness seems to be very challenging as no or very limited skill is estimated during the entire wheat growing season. Other approaches, e.g., linked to the use of large-scale atmospheric patterns, should be identified to enhance the predictability of those harmful events.
Durum wheat (Triticum durum Desf.) is a minor cereal crop of key importance for making pasta, couscous, burghul, puddings, bread and many other traditional foods, due to its physical and chemical ...characteristics. The global demand for high-quality food made of durum wheat has been increasing, which poses a challenge in the face of climate change. Major share of durum wheat production is currently located in semi-arid climates, where the risk of climate extremes such as drought and heat stress will likely substantially increase in the future. To provide a first estimate of future global arable land climatically suitable for growing durum wheat, we develop a suitability model based on support vector machines. The current total share of global arable land climatically suitable to grow rainfed durum wheat is around 13%. Climate change may decrease the suitable area by 19% at mid-century and by 48% at the end of the century. Widespread loss of suitable areas is foreseen in the Mediterranean regions and northern America. On the other hand, climate may become suitable to grow durum wheat in many regions of central and western Europe, while the largest gain in suitability is estimated in some parts of Russia. The overall net loss of suitable areas requires the development and the future adoption of effective and sustainable strategies to stabilize production and adapt the entire food supply chain. Our study also clearly demonstrates the importance of limiting global warming to levels well below 2 °C at the end of the century, which would substantially limit the loss of climatically suitable areas.
Precipitation over Monsoon Asia Ceglar, Andrej; Toreti, Andrea; Balsamo, Gianpaolo ...
Journal of climate,
01/2017, Letnik:
30, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Reanalysis products represent a valuable source of information for different impactmodeling and monitoring activities over regions with sparse observational data. It is therefore essential to ...evaluate their behavior and their intrinsic uncertainties. This study focuses on precipitation over monsoon Asia, a key agricultural region of the world. Four reanalysis datasets are evaluated, namely ERA-Interim, ERA-Interim/Land, AgMERRA (an agricultural version of MERRA), and JRA-55. APHRODITE and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset are the two gridded observational datasets used for the evaluation; the former is based on rain gauge data and the latter on a combination of satellite and rain gauge data. Differences in seasonality, moderate-to-heavy precipitation events, daily distribution, and drought characteristics are analyzed. Results show remarkable differences between the APHRODITE and CHIRPS observational datasets as well as between these datasets and the reanalyses. AgMERRA generally achieves the best performance, but it is not updated at near–real time. ERA-Interim/Land shows good spatial performance, but when the interest is on the temporal evolution JRA-55 is recommended, as it exhibits the most stable temporal behavior. This study shows that the use of reanalyses for impact modeling and monitoring over monsoon Asia requires an accurate evaluation and choices to be tailored to the specific needs.
The MARS-Crop Yield Forecasting System (M-CYFS) is used since 1993 to forecast the yields of all major crops in the European Union (EU) based on gridded runs of the WOFOST crop model. Using 28 years ...of observation, from 1988 to 2015, we quantified the variability in crop yield reported by all 28 EU Member States (MS) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables. A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country/crop combinations, assuming the yield residuals from the trend depend on the inter-annual climate variability. The results are plotted and analyzed at different level: every 10 days for each country crop/combination and each predictor; synthetized every 10 days for each country/crop combination keeping the predictor showing the best relationship with the yield residuals; finally, the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale.
While 61% of the grain maize (Zea mays L.) yield variability can be anticipated 80 days before harvest with the simulated water limited biomass for countries where rainfed maize prevails, 41% of the soft wheat (Triticum aestivum L.) yield variability can be reproduced a month before harvest, the best estimates being obtained where wheat is predominantly exposed to water stress. For the other crops analyzed, the results are also found to be reliable for crops predominantly exposed to water stress and becoming unreliable in agricultural systems exposed to an oceanic climate with a high level of inputs. The agro-meteorological processes related to an excess of water (nitrogen losses, diseases, anoxia, harvest conditions) would need to be disentangled and better integrated into the crop modeling system to improve the predictors.
The monthly cumulated meteorological predictors are performing only slightly worse than the crop model predictors and help to characterize the main processes responsible for the yield variability. Nevertheless, the predictive capacity of the meteorological predictors is spatially and temporally incoherent and differs according to the crop phenology. In comparison, the M-CYFS crop model predictors are more consistent since the predictors summarize the succession of agro-meteorological conditions determining the yield throughout the entire growing season.
•Crop model and meteorological variables are compared to yield variability.•Crop model simulations allow to anticipate yields of crops exposed to drought.•Impact of warm temperatures on yield during grain filling is reflected by WOFOST.•Impacts of water excess on crop growth are not reproduced by the crop model.•The simulated biomass of winter cereals at anthesis is poorly related to the yields.
Wheat is the main staple crop and an important commodity in the Mediterranean and the Middle East. These are among the few areas in the world where the climate is suitable for growing durum wheat but ...also are among the most rapidly warming ones, according to the available scenarios of climate projections. How much food security and market stability in the Mediterranean and the Middle East, both depending on wheat production and its interannual variability, are going to be compromised by global warming is an overarching question. To contribute in addressing it, we use a recently established indicator to quantify crop production climate resilience. We present a methodological framework allowing to compute the annual production resilience indicator from nonstationary time series. We apply this approach on the wheat production of the 10 most important producers in the Mediterranean and the Middle East. Our findings shows that if no adaptation will take place, wheat production reliability in the Mediterranean and the Middle East will be threatened by climate change already at 1.5 °C global warming. Average climate-related wheat production losses will exceed the worst past event even if the 2 °C mitigation target is met. These results call for urgent action on adaptation to climate change and support further efforts for mitigation, fully consistently with the Paris Agreement recommendations.
Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, ...while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.
Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing ...future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.
Seasonal climate forecasts could be an important planning tool for farmers, government and insurance companies that can lead to better and timely management of seasonal climate risks. However, ...climate seasonal forecasts are often under-used, because potential users are not well aware of the capabilities and limitations of these products. This study aims at assessing the merits and caveats of a statistical empirical method, the ensemble streamflow prediction system (ESP, an ensemble based on reordering historical data) and an operational dynamical forecast system, the European Centre for Medium-Range Weather Forecasts-System 4 (S4) in predicting summer drought in Europe. Droughts are defined using the Standardized Precipitation Evapotranspiration Index for the month of August integrated over 6 months. Both systems show useful and mostly comparable deterministic skill. We argue that this source of predictability is mostly attributable to the observed initial conditions. S4 shows only higher skill in terms of ability to probabilistically identify drought occurrence. Thus, currently, both approaches provide useful information and ESP represents a computationally fast alternative to dynamical prediction applications for drought prediction.
Climate services that can anticipate crop yields can potentially increase the resilience of food security in the face of climate change. These services are based on our understanding of how crop ...yield anomalies are related to climate anomalies, yet the share of global crop yield variability explained directly by climate factors is largely variable between regions. In Europe, France has been a major crop producer since the beginning of the 20th Century. Process based and statistical approaches to model crop yields driven by observed climate have proven highly challenging in France. This is especially due to a high regional diversity in climate but also due to environmental and agro-management factors. An additional level of uncertainty is introduced if these models are driven by seasonal-to-decadal surface climate predictions due to their low performances before the harvesting months of both wheat and maize in western Europe. On the other hand, large scale circulation patterns can possibly be better predicted than the regional surface climate, which offers the opportunity to rely on large scale circulation patterns as an alternative to local climate variables. This method assumes a certain degree of stationarity in the relationships between large scale patterns, surface climate variables, and crop yield anomalies. However, such an assumption was never tested, because of the lack of suitable long-term data. This study uses a unique dataset of subnational crop yields in France covering more than a century. By calibrating and comparing statistical models linking large scale circulation patterns and observed surface climate variables to crop yield anomalies, we can demonstrate that the relationship between large scale patterns and surface variables relevant for crop yields is not stationary. Therefore, large scale circulation pattern based crop yield forecasting methods can be adopted for seasonal predictions provided that regression parameters are constantly updated. However, the statistical crop yield models based on large-scale circulation patterns are not suitable for decadal predictions or climate change impact assessments at even longer time-scales.
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