Seasonal Climate Forecasts Research Articles

Adapting innovation of information and communication technologies to climate change risks for agriculture sustainability in central Tanzania

Climate change is one of the most challenging and alarming global issues for threatening the existence of the living entities of ecosystems. Manifestations of climatic aberrations are evidently diverse and extensive in nature. Unfortunately, agricultural sector is heavily vulnerable to the vagaries of climate putting global food security at stake. Farming communities rarely have access to up-to-date weather and climate information, which hinders their ability to practice climate-smart agriculture. Extreme weather events variations in rainfall and temperature can result in reduced yields or even the destruction of crops, affecting farmers’ incomes and livelihoods. Unsustainable agricultural practices compound the problem by reducing in soil fertility and the soils’ capacity to retain water, contributing to soil erosion. This can drive ecosystem degradation and habitat loss, with a resulting loss of biodiversity. To cope up with this changing scenarios, strategic innovation is a must, especially for the highly populous developing countries like Tanzania. By virtue of the generative and systemic tools of Information and Communication Technologies (ICTs) and its efficient knowledge management system, ICTs offers a great potential to record, transform and disseminate information while strengthening adaptability to the changing situations in multifarious ways. Remote sensing tools and corresponding technological innovations can ensure a sustainable meal to the ever-surging population. Climate change can only be confronted when these future ready technologies will be coupled with efficient agricultural extension protocols. This paper was an attempt to snowball adapted ICTs tools to climate change adaptation strategies as an innovating way to sustain food security and climate resilience community. The recommended tools should cover agronomic data capture, on-farm data visualization, machine learning analysis, meteorological data curation, crop modeling and seasonal climate forecasting.

Delivering context specific, climate informed agro-advisories at scale: A case study of iSAT, an ICT linked platform piloted with rainfed groundnut farmers in a semi-arid environment

The influence of climate and weather-based advisories in planning and managing agricultural systems under highly variable conditions was evaluated to understand the potential benefits and challenges associated with the use of probabilistic climate and weather information. A pilot study, conducted with 720 farmers in Anantapur district of Andhra Pradesh, India over 3 cropping seasons in the period 2017–2020, used a semi-automated decision support tool, the “intelligent agricultural Systems Advisory Tool – iSAT” to generate and disseminate pre- and in-season advisories by integrating insights from historical trends in climate, current weather and climate and weather forecasts. The pre-season advisory was based on the seasonal climate forecast and aimed at improving the preparedness of farmers for the forthcoming season. The in-season advisories were aimed at providing advice on the various farm operations where weather may play a role in management, i.e., land preparation, timing of planting and harvesting, crop management etc. After piloting the advisory system over the 3 cropping systems, a survey was conducted to evaluate how and what operations were influenced by the advisories and how well iSAT performed in developing and disseminating context-specific advisories through the season. The results have indicated that the advisories have influenced both strategic and tactical management decisions made by farmers. Strategic decisions on crop diversification are evident from land allocation in the treatment compared to the control villages. The influence of tactical farm decisions varied between operations, villages and years. Overall, 80% of the farmers used the information for making decisions on harvesting, 79% for sowing while 65% of farmers used it for land preparation. Advisory information has impacted crop productivity positively, with increases in the climate-informed villages of between 1 and 56% over the uninformed. The results further indicate that the farmers considered advisories more useful during the normal and below normal seasons as compared to above normal seasons.

KEMAMPUAN RAMAL SISTEM PERKIRAAN IKLIM MUSIMAN MENGGUNAKAN FENOMENA ENSO DI KABUPATEN LOMBOK TENGAH, NUSA TENGGARA BARAT

Lombok Island, particularly the southern central part of Lombok, often experiences droughts that result in failed cultivation or harvests due to the El Nino and Southern Oscillation (ENSO) phenomena. Therefore, the objective of this research is to determine the forecasting skill or predictive ability of a seasonal climate prediction system using the ENSO indicator in the Central Lombok regency, West Nusa Tenggara Province. This study employs a descriptive method by comparing the average rainfall in El Nino years with non-El Nino years. The research is conducted in the Central Lombok regency, which is divided into the northern and southern parts. The data used includes monthly precipitation recorded by BMKG for approximately 50 years (1970-2019) in all districts of Central Lombok. The SOI data is accessed at http://www.bom.gov.au/climate/current/soi2.shtml. The results of this study indicate that the use of the ENSO indicator to forecast seasonal rainfall patterns in the Central Lombok regency shows high consistency and significant predictive ability. There is no significant difference in the predictive ability and consistency of the forecast results between the northern and southern parts of Central Lombok. In general, the predictive ability during the dry season exhibits higher consistency (25%) compared to the rainy season (14%).

Role of the Maritime Continent in the remote influence of Atlantic Niño on the Pacific

Atlantic Niño, the dominant climate mode in the equatorial Atlantic, is known to remotely force a La Niña-like response in the Pacific, potentially affecting seasonal climate predictions. Here, we use both observations and large-ensemble simulations to explore the physical mechanisms linking the Atlantic to the Pacific. Results indicate that an eastward propagating atmospheric Kelvin wave from the Atlantic, through the Indian Ocean, to the Pacific is the primary pathway. Interaction of this Kelvin wave with the orography of the Maritime Continent induces orographic moisture convergence, contributing to the generation of a local Walker Cell over the Maritime Continent-Western Pacific area. Moreover, land friction over the Maritime Continent dissipates Kelvin wave energy, affecting the strength of the Bjerknes feedback and thus the development of the La Niña-like response. Therefore, improving the representation of land–atmosphere–ocean interactions over the Maritime Continent may be fundamental to realistically simulate Atlantic Niño’s impact on El Niño-Southern Oscillation.

Assessing the influence of the El Niño–Southern Oscillation phase on rainfall variability in the Gauteng province of South Africa

South Africa is subject to large rainfall variability, with El Niño–Southern Oscillation (ENSO) being the most important driver across the summer rainfall region. The aim of this study was to relate ENSO phase to historical rainfall variability over the densely populated and economically important Gauteng province of South Africa. Monthly rainfall totals for the period 1960–2021 were used to calculate Standardised Precipitation Index (SPI) values at different timescales for five stations. The Oceanic Niño Index (ONI) was used to classify seasons according to ENSO phase. For each station, the number of wet, near-normal or dry seasons falling in a particular ENSO phase was tallied and used to calculate probabilities of occurrence. Results revealed spatial inhomogeneities despite the study area being relatively small. Analysis of 3- and 6-month periods revealed that dry conditions occur more frequently during spring and autumn El Niño events, but are very unlikely during summer and autumn La Niña events. Analysis of 12-month periods revealed that near-normal conditions occur most frequently regardless of ENSO phase, with an increased chance of experiencing wet as opposed to dry conditions under La Niña. This information can aid various decision-makers to make better use of seasonal climate predictions.

Skill decreases in real-time seasonal climate prediction due to decadal variability

Skill decreases in real-time seasonal climate prediction due to decadal variability

Downscaling long lead time daily rainfall ensemble forecasts through deep learning

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.

Evaluating Multimodel Ensemble Seasonal Climate Forecasts on Rangeland Plant Production in the California Annual Grassland

Seasonal precipitation and temperature directly affect total plant production in the California Annual Grassland (CAG). Technological advances have resulted in skillful seasonal climate forecasts (i.e., significant correlations between actual and forecasted climate), which could be input into plant production models to inform stocking and other rangeland management decisions. This study presents a procedure for forecasting plant production in the CAG ecosystem to predict annual plant production for grazing, restoration, or other rangeland management practices using a combination of historical gridMET climate data and seasonal hindcasts (i.e., retrospective forecasts, from the North American Multi-Model Ensemble program). The results of this study first confirmed high forecast skill, throughout the growing season at all sites. We also identified skillful plant production forecasts across most of the growing season at two sites and in three of the seven forecasting months at one study site. Forecasting climate and end-of-year plant production across the growing season at three CAG sites allowed us to identify the places and times in the growing season when forecasting might be most helpful in informing management decisions. Integrating plant production forecasting into rangeland management practices could significantly improve rangeland management outcomes. These procedures provide a user guide for creating plant production forecasts for any given area of interest and may be applicable across a wide range of other agricultural and rangeland management systems.

Evaluating Seasonal Climate Forecasts from Dynamical Models over South America

Abstract Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Significance Statement Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models.

Evaluation of downscaling seasonal climate forecasts for crop yield forecasting in Zimbabwe

Meteorology and weather forecasting are crucial for water-limited agriculture. We evaluate the added value of downscaling seven-months global deterministic seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather, Research and Forecasting (WRF) model over Zimbabwe for ten growing seasons (2011–2021). Downscaling reduces the area of significant differences between forecasted and observed total seasonal rainfall. Downscaling also improves the score for droughts as measured through the standardized precipitation index and 3-class method. Yield forecasts by the WOrld FOod STudies (WOFOST) model reveal that downscaling improves the estimated growing season evolution and maize yield in all studied regions across the country. For the main maize production region Karoi, the bias, root mean square error, mean absolute error and mean absolute percentage error reduce by 33% (0.2 ton/ha), 27% (0.4 ton/ha), 31% (0.4 ton/ha) and 27% (8.3 %) respectively by downscaling. Hence we illustrate that downscaling the deterministic seasonal forecasts may assist in food security in a crucial area in southern Africa.

Konsistensi Sistem Prakiraan Iklim Musiman Menggunakan Indikator ENSO di Daerah Tipe Iklim D3 dan D4 Kabupaten Lombok Tengah

The purpose of this study was to determine the consistency of seasonal climate forecasts using the ENSO phenomenon. The ENSO indicator categorized with El Nini, Neutral and La Nina were coorelated to rainfall categorized with Below Normal, normal and above normal. The research was carried out in the D3 dan D4 type of climate in ccentral Lombok regency. The method used in this research is descriptive method, which is a method aimed at solving problems by collecting data, compiling, analyzing, interpreting data, and drawing conclusions. This research was conducted in Central Lombok in climate types D3 and D4 covering the areas of Kopang, Mantang, Praya, Puyung, Pringgarata, Penujak, Mujur, Pujut, and Janapria. Using monthly rainfall data for ± 50 years from 1970-2019. Rainfall data were taken at Kopang, Mantang, Praya, Puyung, Pringgarata stations for type D3 and Penujak, Mujur, Pujut, and Janapriafor type D4. SOI data can be accessed at http://www.bom.gov.au/climate/current/soi2.shtml. The results showed that from 1970-2019 (50 years) there was a consistent value in all areas that were predicted, namely above 50% and inconsistency below 50%. Consistency describes how accurate the results of the forecast are. If the percentage of the consistency value is above 50%, it means that it is better to predict using the forecasting method by looking for consistency than using the average. Therefore, if you use a forecasting system, if an El Nino phenomenon occurs there will be a shortage of water, you can adjust the planting time, prepare irrigation water first, reduce the planting area, and delay the planting time first.Keywords: Climate Variations, SOI, Rainfall, Climate D3 and D4

Translating seasonal climate forecasts into water balance forecasts for decision making

Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Resampling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way.

Soil surface treatments and precipitation timing determine seedling development across southwestern US restoration sites.

Restoration in dryland ecosystems often has poor success due to low and variable water availability, degraded soil conditions, and slow plant community recovery rates. Restoration treatments can mitigate these constraints but, because treatments and subsequent monitoring are typically limited in space and time, our understanding of their applicability across broader environmental gradients remains limited. To address this limitation, we implemented and monitored a standardized set of seeding and soil surface treatments (pits, mulch, and ConMod artificial nurse plants) designed to enhance soil moisture and seedling establishment across RestoreNet, a growing network of 21 diverse dryland restoration sites in the southwestern USA over 3 years. Generally, we found that the timing of precipitation relative to seeding and the use of soil surface treatments were more important in determining seeded species emergence, survival, and growth than site-specific characteristics. Using soil surface treatments in tandem with seeding promoted up to 3× greater seedling emergence densities compared with seeding alone. The positive effect of soil surface treatments became more prominent with increased cumulative precipitation since seeding. The seed mix type with species currently found within or near a site and adapted to the historical climate promoted greater seedling emergence densities compared with the seed mix type with species from warmer, drier conditions expected to perform well under climate change. Seed mix and soil surface treatments had a diminishing effect as plants developed beyond the first season of establishment. However, we found strong effects of the initial period seeded and of the precipitation leading up to each monitoring date on seedling survival over time, especially for annual and perennial forbs. The presence of exotic species exerted a negative influence on seedling survival and growth, but not initial emergence. Our findings suggest that seeded species recruitment across drylands can generally be promoted, regardless of location, by (1) incorporation of soil surface treatments, (2) employment of near-term seasonal climate forecasts, (3) suppression of exotic species, and (4) seeding at multiple times. Taken together, these results point to a multifaceted approach to ameliorate harsh environmental conditions for improved seeding success in drylands, both now and under expected aridification.

Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning

The quasi-periodic signals in the earth system could be the predictability source for sub-seasonal to seasonal (S2S) climate prediction because of the connections among the lead-lag time of those signals. The Madden–Julian Oscillation (MJO) is a typical quasi-periodic signal, which is the dominant S2S variability in the tropics. Besides, significantly periodic features in terms of both intensity and location are identified in 10–40 days for the concurrent variation of the subtropical and polar jet streams over Asia in this study. So far, those signals contribute less and are not fully applied to the S2S prediction. The deep learning (DL) approach, especially the long-short term memory (LSTM) networks, has the ability to take advantage of the information at the previous time to improve the prediction after then. This study presents the application of the DL in the postprocessing of S2S prediction using quasi-periodic signals predicted by the operational model to improve the prediction of minimum 2-m air temperature over Asia. With the help of deep learning, it finds the best weights for the ensemble predictions, and the quasi-periodic signals in the atmosphere can further benefit the S2S operational prediction.

Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory

Ocean primary productivity generated by phytoplankton is critical for ocean ecosystems and the global carbon cycle. Accurate ocean primary productivity forecasting months in advance is beneficial for marine management. Previous persistence-based prediction studies ignore the temporal memories of multiple relevant factors and the seasonal forecasting skill drops quickly with increasing lead time. On the other hand, the emerging ensemble climate forecasts are not well considered as new predictability sources of ocean conditions. Here we proposed a joint forecasting model by combining the seasonal climate predictions from ten heterogeneous models and the temporal memories of relevant factors to examine the monthly predictability of ocean productivity from 0.5- to 11.5-month lead times. The results indicate that a total of ~90% and ~20% productive oceans are expected to be skillfully predicted by the combination of seasonal SST predictions and local memory at 0.5- and 4.5-month leads, respectively. The joint forecasting model improves by 10% of the skillfully predicted areas at 6.5-month lead relative to the prediction by productivity persistence. The hybrid data-driven and model-driven forecasting approach improves the predictability of ocean productivity relative to individual predictions, of which the seasonal climate predictions contribute largely to the skill improvement over the equatorial Pacific and Indian Ocean. These findings highlight the advantages of the integration of climate predictions and temporal memory for ocean productivity forecasting and may provide useful seasonal forecasting information for ocean ecosystem management.

Accurate long-term air temperature prediction with Machine Learning models and data reduction techniques

In this paper, three customised Artificial Intelligence (AI) frameworks, considering Deep Learning, Machine Learning (ML) algorithms and data reduction techniques, are proposed for a problem of long-term summer air temperature prediction. Specifically, the prediction of the average air temperature in the first and second August fortnights, using input data from previous months, at two different locations (Paris, France) and (Córdoba, Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the heatwave of 2003, which affected France and the Iberian Peninsula. Three different computational frameworks for air temperature prediction are proposed: a Convolutional Neural Network (CNN), with video-to-image translation, several ML approaches including Lasso regression, Decision Trees and Random Forest, and finally a CNN with pre-processing step using Recurrence Plots, which convert time series into images. Using these frameworks, a very good prediction skill has been obtained in both Paris and Córdoba regions, showing that the proposed approaches can be an excellent option for seasonal climate prediction problems.

Effectiveness of agrometeorological services for smallholder farmers: The case study in the regions of Dosso and Tillabéri in Niger

The increasing frequency of extreme events in West Africa, such as droughts and floods, has made populations that base their subsistence mostly on rainfed agriculture even more vulnerable to climate threats. Climate Services (CS) are largely acknowledged as effective tools for tackling climate risks in agriculture, particularly in semi-arid developing countries but evidences of their effectiveness are still jeopardized. In Niger a climate service (CS) has been set up in the regions of Dosso and Tillabéri by the National Meteorological Service (NMS) to provide salient information for smallholder farmers. The CS was built on a robust collaboration among NMS, local extension services and authorities and farmers in 8 municipalities. The case study shows that a large share of farmers receives throughout the cropping season climatic information and advice via roving seminars and various media, including instant messaging services and radio broadcasts. Nevertheless, the results indicate that access to CS alone doesn’t imply relevant positive impacts on crop yields while the training of farmers in the use of the information results to be a significant factor. Indeed, in 2020, yields of trained farmers are significantly higher by around 17% compared to those of non-trained ones. Training and iterative interaction between farmers and NMS could also have indirect effects on information uptake, contributing to building reciprocal trust and therefore stronger action by trained farmers. The study confirms the importance of the social learning process in CS co-development. Since the study is limited by the small sample and the dataset covering only one cropping season, further research is needed to deepen cost-benefit analysis and disentangle the relative contribution of the CS components to yield increase. Indeed, evidences of the positive impact of CS could represent a leverage for local governments and international funders to support CS co-development and related capacity building activities. Practical implicationClimate variability and a strong increase in extreme hydro meteorological events are affecting agriculture production and exacerbating food insecurity in West Africa. In Niger, the vulnerability of agricultural production systems is coupled to ecosystem fragility and soil degradation. In this area, the rural population is the most vulnerable to climate threats because they have a reduced capability to implement effective risk reduction and climate change adaptation strategies and national government has limited resources to invest in climate policies. The CS implemented for the regions of Dosso and Tillabéri in Niger demonstrates that it is possible to set up an effective network for disseminating agrometeorological information for smallholder farmers at the municipal level with the aim of reducing the impact of climate threats on agriculture production. The information produced by the National Meteorological Service (NMS) is spread through extension services and rural radios to reach farmers. At the same time the agrometeorological field data are collected by local farmers and sent to the national service, ensuring the continuous monitoring of the cropping season. Subsequently, the agrometeorological information is coupled with setting up roving seminars in each municipality to spread tailored advice for farmers concerning seasonal climate forecasts and to build capacities in the use of agrometeorological advices during the season. During these seminars, rain gauges are also distributed to farmers and their use explained. In this way, farmers become able to autonomously take some tactical decisions, such as better timing the sowing of crops or performing farming activities, basing these choices on direct observations. The present case study demonstrates that the mere receipt of the climate information is not clearly related to an increase in yields; contrariwise, farmers who received training on how to properly use the information, have significantly higher yields. Repeated capacity building and information distribution over the years represent an element of trust building for end users who are more prone to use these CS in their agricultural choices, integrating their traditional knowledge. The next challenge is to guarantee the sustainability of these networks over time, because, even if technology advances could reduce the costs of the production and distribution of climate services, the training activities and maintaining the rural observation network are challenging. A possible way to make it sustainable is to reinforce institutional collaboration. Moreover, the use of a participatory approach in co-designing the CS could be a key element in pursuing the active involvement of the local population and administrations and could increase their motivation in the data exchange process. Basing on obtained results, the authors recommend to pursue the development of tailored CS for smallholder farmers in similar rural contexts, since these services constitute a real contribution to climate change adaptation at the local level in rural areas and future experiences could ensure the fine tuning of the climate information products, reducing delivery costs and increasing benefits for stakeholders. Finally, it is also recommended to further assess the cost/benefit ratio of CS in order to leverage funds and ensure scaling up and sustainability.

Performance of seasonal forecasts for the flowering and veraison of two major Portuguese grapevine varieties

Seasonal phenology forecasts are becoming increasingly demanded by winegrowers and viticulturists. Forecast performance needs to be investigated over space and time before practical applications. We assess seasonal forecast performance (skill, probability and accuracy) in predicting flowering and veraison stages of two representative varieties in Portugal over 1993–2017. The state-of-the-art forecast system ECMWF-SEAS5 provides 7-month seasonal forecasts and is coupled with a locally adapted phenology model. Overall, findings illustrate the dependence of forecast performance on initialization timings, regions and predicting subjects (stages and varieties). Forecast performance improves by delaying the initialization timing and only forecasts initialized on April 1st show better skills than climatology on predicting phenology terciles (early/normal/late). The considerable bias of daily values of seasonal climate predictions can represent the main barrier to accurate forecasts. Better prediction performance is consistently found in Central-Southern regions compared to Northern regions, attributing to an earlier phenology occurrence with a shorter forecast length. Comparable predictive skills between flowering and veraison for both varieties imply better predictability in summer. Consequently, promising seasonal phenology predictions are foreseen in Central-Southern wine regions using forecasts initialized on April 1st with approximately 1–2/3–4 months lead time for flowering/veraison: potential prediction errors are ∼2 weeks, along with an overall moderate forecast skill on categorical events. However, considerable inter-annual variability of forecast performance over the same classified phenology years reflects the substantial influence of climate variability. This may represent the main challenge for reliable forecasts in Mediterranean regions. Recommendations are suggested for methodological innovations and practical applications towards reliable regional phenology forecasts.

POTENCIAL USE OF LOCAL WISEDOM OF SASAK (WARIGE) AS SEASONAL RAINCAST IN LOMBOK

Climate anomaly has pronounced impact on agriculture in Lombok. Occurence of drought results in crop failure. Up to date our knowledge about the cause of climate anomaly has still vey limited. Better understanding on that matter may contribute better water management and cropping strategy. The aim of this study was to examine the skill of seasonal climate forecast based on Sasak local native knowlledge (warige) which was validated with real time planting practised by farmers in different types of climate in Lombok. The output of this study is expected to be used as additional climate information for integrated crop calendar in Lombok. This study applied descriptive methods with survey technique. Data collected included Luni-solar calendar from 1950 to 2012, monthly rainfall data of 1950 to 2012 from four types of climate in Lombok. Five year daily rainfall in selected areas. To obtain moonphase at overhead sun from 1950 to 2012 it was used Kalender Jawa obtained from http:// www.kalendarjawa.com. Ten points of filed crop observation was established in each tipe of climate (C3); D3 and D4. C3 belongs to technical irrigation, D3 belongs to semi-technical irrigation and and D4 belongs to rainfed adn embung irrigation. All farmers involved in this study were also questioned whether they understand and apply warige in their seasonal crop planning. Results of this studyshowed that SCF based on Sasak local native knowledge had good skill in forecasting wetness and dryness of dry season. The different in amount of rainfall in dry and wet years is fairly significant, but usually wet year rainfall did not differ to moderate years’, neither moderate years’ to dry years’. Nevertheless warige was able to predict distribution of rainfall within month during the dry season. Thus we can predict wether it will enough rainfall for several in coming months

Predictability of China winter temperature under different intensities of La Niña events in seasonal climate forecast models

AbstractIn this study, we focus on the features and predictability of winter 2‐m air temperature (t2m) over China under different intensities of La Niña events. The seasonal and intraseasonal prediction skills of winter t2m over China are examined by four state‐of‐art seasonal climate forecast models. The results show that the China winter t2m anomaly is sensitive to the intensity of La Niña. Strong and moderate (S/M) La Niña events are often accompanied by cold winter over China, while a positive anomaly of mean t2m over China frequently occurs in the weak La Niña events. The intensity difference of the East Asian winter monsoon (EAWM) results in this discrepancy. All models have a slightly higher anomaly correlation coefficient (ACC) for the S/M La Niña than that for the weak La Niña in leading 1 month (LT1). Both individual models and MME exhibit higher skills of anomaly sign consistency rate (ASCR) for the S/M La Niña, which implies that models predict t2m anomaly over China more accurately in the S/M La Niña events. Furthermore, the winter t2m over China presents larger intraseasonal variability for the S/M La Niña than that for the weak La Niña. Models can capture the t2m anomaly over China more accurately when the temperature varies gently with small intraseasonal variability during winter for the S/M La Niña.