Resistance to Leaf and Yellow Rust in a Collection of Spanish Bread Wheat Landraces and Association with Ecogeographical Variables

A collection of 84 bread wheat Spanish landraces were inoculated with three isolates of leaf rust and one of yellow rust at the seedling stage in controlled conditions. The latency period of leaf rust on the susceptible landraces was also assessed. An extended collection of 149 landraces was planted in three locations in field trials to evaluate the naturally occurring leaf and yellow rust severity. Several landraces (36) were resistant to one leaf rust isolate at the seedling stage, but only one was resistant to all three isolates. Landraces resistant to PG14 leaf rust isolate originated from areas with higher precipitation and more uniform temperatures. Many resistant landraces were from the north-west zone of Spain, a region with high precipitation and uniform temperatures. Results from the field trials also confirmed this trend. Landraces from the north-west also possessed a longer latency period of leaf rust, an important component of partial resistance. Regarding yellow rust, 16 landraces showed a lower disease severity in the seedling tests. Again, the resistant landraces mostly originated from areas with higher precipitation (especially in winter) and more uniform temperature.

Extreme weather events and high Colombian food prices: A non-stationary extreme value approach

Given the importance of climate change and the increase of its severity under extreme weather events, we analyze the main drivers of high food prices in Colombia between 1985 and 2020 focusing on extreme weather shocks like a strong El Ni˜no.We estimate a non-stationary extreme value model for Colombian food prices. Our findings suggest that perishable foods are more exposed to extreme weather conditions in comparison to processed foods. In fact, an extremely low precipitation level explains only high prices in perishable foods. The risk of high perishable food prices is significantly larger for low rainfall levels (dry seasons) compared to high precipitation levels (rainy seasons). This risk gradually results in higher perishable food prices. It is non linear and is also significantly larger than the risk related to changes in the US dollar-Colombian peso exchange rate and fuel prices. Those covariates also explain high prices for both perishable and processed foods. Finally, we find that the events associated with the strongest El Ni˜no in 1988 and 2016 are expected to reoccur once every 50 years.

Process-Based Evaluation of Stochastic Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events

Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud-level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.

The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns.

The Masterful Water Engineers of Machu Picchu

The water engineering achievements of the Inca at Machu Picchu, when defined in technical terms common to modern engineers, demonstrate that the Inca were masterful planners, designers, and constructors. They demonstrated their technical skills through the planning, design, and construction of water supply, fountains, terraces, foundations, walls, and trails. The site of Machu Picchu was a difficult place to build, with high precipitation, steep terrain, and challenging access. Nonetheless, the Inca had the uncanny ability to plan public works and infrastructure in a manner that fit this problematic site and lasted for centuries.

Brief communication: Impact of common ice mask in surface mass balance estimates over the Antarctic ice sheet

Regional climate models compute ice sheet surface mass balance (SMB) over a mask that defines the area covered by glacier ice, but ice masks have not been harmonised between models. Intercomparison studies of modelled SMB therefore use a common ice mask. The SMB in areas outside the common ice mask, which are typically coastal and high precipitation regions, are discarded. Ice mask differences change integrated SMB by between 40.5 to 140.6 Gt yr−1, (1.8 % to 6.0 % of ensemble mean SMB), equivalent to the entire Antarctic mass imbalance. We conclude there is a pressing need for a common ice mask protocol.

Hot Coffee: The Identity, Climate Profiles, Agronomy, and Beverage Characteristics of Coffea racemosa and C. zanguebariae

Climate change poses a considerable challenge for coffee farming, due to increasing temperatures, worsening weather perturbations, and shifts in the quantity and timing of precipitation. Of the actions required for ensuring climate resilience for coffee, changing the crop itself is paramount, and this may have to include using alternative coffee crop species. In this study we use a multidisciplinary approach to elucidate the identity, distribution, and attributes, of two minor coffee crop species from East Africa: Coffea racemosa and C. zanguebariae. Using DNA sequencing and morphology, we elucidate their phylogenetic relationships and confirm that they represent two distinct but closely related species. Climate profiling is used to understand their basic climatic requirements, which are compared to those of Arabica (C. arabica) and robusta (C. canephora) coffee. Basic agronomic data (including yield) and sensory information are provided and evaluated. Coffea racemosa and C. zanguebariae possess useful traits for coffee crop plant development, particularly heat tolerance, low precipitation requirement, high precipitation seasonality (dry season tolerance) and rapid fruit development (c. 4 months flowering to mature fruit). These attributes would be best accessed via breeding programs, although these species also have niche-market potential, particularly after further pre-farm selection and post-harvest optimization.

Use of Selected Spectral Ratios to Assess the Response of Pineapple to Potassium Nutrition

Potassium (K) nutrition in pineapple grown on tropical peat can be problematic due to high precipitation which encourages leaching losses. Non-destructive tools that can assess K deficiency and the accompanying changes in biophysical and biochemical properties within pineapple is a good strategy to employ. In this study, we assessed the biophysical changes in pineapple (var. MD2) in response to different K rates by using a hyperspectral approach. K deficiency was detected at 171 days after planting. Shortage of K also exhibited a shift in red edge towards shorter wavelengths between 500-700 nm. In addition, spectral ranges of 430-680 nm, as well as 680-752 nm were found to be most effective in differentiating spectral response to varying K rates. Three vegetation indices, i.e. Normalized Pigment Chlorophyll Index (NPCI), Plant Senescence Index (PSRI) and Red-edge Vegetation Index (RVSI) were found to best describe K treatment effects on pineapple canopy reflectance. This study could be extended further to include pineapple varieties other than MD2, and also key nutrients, such as N and P, for better fertilizer management in peat-grown pineapple.

Estimation of the observation standard deviation error formula thanks to the a posteriori diagnosis

<p><em>Within the UERRA project, a daily precipitation reanalysis at a 5,5km resolution has been realized from 1961 to 2015. The reanalysis was obtained by the MESCAN analysis system which combines an a priori estimate of the atmosphere – called background – and observations using an optimum interpolation (OI) scheme. Such method requires the specification of observations and background errors. In general, constant standard deviation errors are used but more errors are made when high precipitation are observed. Then, to take this effect into account and to avoid a model over-estimation in case of light precipitation, a variable formula of the observation standard deviation error was purposed with a small value for null precipitation and greater values when precipitation are higher, following a linear equation.</em></p><p><em> Desroziers et al proposed a method to determine observations and background errors called a posteriori diagnosis. To use this iterative method, the analysis has to be ran several times until it converged. In this study, the a posteriori diagnosis is used per precipitation class to determine the observation standard deviation error formula. MESCAN was tested using the French operational model AROME at 1,3km resolution and the atmopsheric UERRA analysis downscaled to 5,5km background and combined to the French observational network over the 2016-2018 period. The observation standard deviation error formula obtained by the a posteriori diagnosis is then used in the MESCAN analysis system to produce precipitation analysis over the 2016-2018 period. Results are compared to UERRA precipitation reanalysis over independant observations by comparing bias, RMSE and scores per precipitation class.</em></p>