Regional Climate Change Scenarios Research Articles

Global river economic belts can become more sustainable by considering economic and ecological processes

High-quality regional development requires coupling of socioeconomic and natural domains, but it remains unclear how to effectively integrate the regional economy with river basin ecosystems. Here we establish a developmental perspective of 65 river economic belts, formed through history along the main stems of the world’s great rivers, covering initial, developing, and developed stages. We find that river economic belts characterized by basin-based regional integration can substantially upgrade their eco-efficiency through the harmonization of enhanced regional economic growth and efficient utilization of basin resources, once key prerequisites (e.g., gross domestic product per capita, de-industrialization status, and human development index) are met for river economic belts entering the developed stage. Importantly, primary concerns such as resource stress, environmental pollution, and biodiversity loss are also inherently addressed. Under representative scenarios of regional development planning and climate change (2015–2050), the basin-based regional integration strategy would provide river economic belts with new opportunities and pathways towards sustainability in emerging regions worldwide.

Future climatic conditions may threaten adaptation capacities for vineyards along Lake Neuchâtel, Switzerland

In Switzerland, as elsewhere in the world, climate change is challenging viticulture. Knowledge of the potential impacts is essential for preparing adaptation measures. Two aspects directly impacted by increasing temperatures are the choice of grapevine varieties and the location of vineyards. To help address these impacts, we analysed future trends in two bioclimatic indices, average growing season temperature (GST) and Huglin’s heliothermal index (HI), in the Swiss canton of Neuchâtel. We conducted our analysis based on regional climate change scenarios referring to the emission pathways RCP4.5 and RCP8.5. Under the assumption of RCP8.5, trends in GST and HI indicate that the climate in this region will become too hot for most grapevine varieties currently cultivated, especially Pinot noir. Moreover, adaptation problems under RCP8.5 are expected to originate from an increase in climate extremes in both temperature and precipitation. Results based on RCP4.5 indicate a broader scope for adaptation, as the climate will remain suitable for a larger number of grapevine varieties within the current altitudinal limits of the Neuchâtel vineyards. In theory, an altitudinal shift of Pinot noir would also be possible under this emission pathway. In practice, however, the possibility of establishing vineyards above 600 m would be limited by the presence of protected forests and rocky areas. Our results highlight that vineyards in this region will need important adaptation measures if anthropic greenhouse gas emissions do not decrease rapidly and considerably, limiting the global temperature increase to < 1.5 °C.

Projected changes in mean annual cycle of temperature and precipitation over the Czech Republic: Comparison of CMIP5 and CMIP6

The multi-model ensembles like CMIP5 or CMIP6 provide a tool to analyze structural uncertainty of climate simulations. Currently developed regional and local climate change scenarios for the Czech Republic assess the uncertainty based on state-of-the-art Global Climate Model (GCM) and Regional Climate Model (RCM) ensembles. Present study focuses on multi-model spread of projected changes in long-term monthly means and inter-annual variability of monthly mean minimum, mean and maximum daily air temperature and monthly mean precipitation. We concentrate in more detail on the simulation of CNRM-ESM2-1, the driving GCM for the convection permitting ALADIN-Climate/CZ simulation contributing to the local scenarios in very high resolution. For this GCM, we also analyze a mini-ensemble with perturbed initial conditions to evaluate the range of internal climate variability. The results for the Czech Republic reveal minor differences in model performance in the reference period whereas quite substantial inter-generation shift in projected future change towards higher air temperature and lower summer precipitation in CMIP6 comparing to CMIP5. One of the prominent features across GCM generations is the pattern of summer precipitation decrease over central Europe. Further, projected air temperature increase is higher in summer and autumn than in winter and spring, implying increase of thermal continentality of climate. On the other hand, slight increase of winter precipitation and tendency towards decrease of summer precipitation lead to projected decrease of ombric continentality. The end of 21st century projections also imply higher probability of dry summer periods, higher precipitation amounts in the cold half of the year and extremely high temperature in summer. Regarding the CNRM-ESM2-1, it is often quite far from the multi-model median. Therefore, we strictly recommend to accompany any analysis based on the simulation of nested Aladin-CLIMATE/CZ with proper uncertainty estimate. The range of uncertainty connected to internal climate variability based on one GCM is often quite large in comparison to the range of whole CMIP6 ensemble. It implies that when constructing climate change scenarios for the Central Europe region, attention should be paid not only to structural uncertainty represented by inter-model differences and scenario uncertainty, but also to the influence of internal climate variability.

AIRCC-Clim: A user-friendly tool for generating regional probabilistic climate change scenarios and risk measures

Complex physical models are the most advanced tools available for producing realistic simulations of the climate system. However, such levels of realism imply high computational cost and restrictions on their use for policymaking and risk assessment. Two central characteristics of climate change are uncertainty and that it is a dynamic problem in which international actions can significantly alter climate projections and information needs, including partial and full compliance of global climate goals. Here we present AIRCC-Clim, a simple climate model emulator that produces regional probabilistic climate change projections of monthly and annual temperature and precipitation, as well as risk measures, based both on standard and user-defined emissions scenarios for six greenhouse gases. AIRCC-Clim emulates 37 atm-ocean coupled general circulation models with low computational and technical requirements for the user. This standalone, user-friendly software is designed for a variety of applications including impact assessments, climate policy evaluation and integrated assessment modelling.

Kemajuan Penelitian Pemodelan Prediksi Demam Berdarah Dengue menggunakan Faktor Iklim di Indonesia : A Systematic Literature Review

Since discovered firstly in 1968, number of cases and areas affected by DHF in Indonesia has been increased. In 2019, dengue cases have found in all provinces within 481 districts/cities (94%). Our research is conducted to analyze the current status and gaps of climate relationship and its modeling to DHF in Indonesia. A systematic searching of literature was carried out through the search engine PubMed and Google Scholar. The method includes determining questions, publication period, keywords, and criteria of literature. Thirty-two literatures have been selected according to the criteria. The study area has covered all provinces in Java, Bali and West Nusa Tenggara, several locations in Sumatra, Kalimantan, Sulawesi, while the eastern region has still limited study. Spatial and temporal variations were used predominantly at the city with monthly data scale. Relationship analysis between DHF cases and climate/non-climate has been used the Spearman’s and Pearson’s correlation. DHF prediction modeling involves dominant climate parameters such as rainfall, temperature, humidity and non climate parameters using linear/non-linear relationships and static/dynamic models. Climate model development needs to be improved with a narrower spatial resolution and shorter time scale, elevation, mobilization, regional climate, and climate change scenarios to get appropriate model on a specific location.

Projection of future wildfire emissions in western USA under climate change: contributions from changes in wildfire, fuel loading and fuel moisture

Numerous devastating air pollution events from wildfire smoke occurred in this century in the western USA, leading to severe environmental consequences. This study projects future fire emissions in this region under climate change with a focus on comparing the relative contributions from future changes in burned area, fuel loading and fuel moisture. The three properties were projected using an empirical fire model, a dynamical global vegetation model and meteorological conditions respectively. The regional climate change scenarios for the western USA were obtained by dynamical downscaling of global climate projections. The results show overall increasing wildfires and fuel loading and decreasing fuel moisture. As a result, fire emissions are projected to increase by ~50% from 2001–2010 to 2050–2059. The changes in wildfires and fuel loading contribute nearly 75% and 25% of the total fire emission increase, respectively, but the contribution from fuel moisture change is minimal. The findings suggest that the air pollution events caused by wildfire smoke could become much more serious in the western USA by the middle of this century, and that it would be essential to take the future changes in fuel conditions into account to improve the accuracy of fire emission projections.

Risk Reduction and Productivity Increase Through Integrating Arachis pintoi in Cattle Production Systems in the Colombian Orinoquía

In many parts of the foothills of the Orinoquía region of Colombia, cattle production takes place on poorly drained soils. The region is dominated by extensive grazing systems of Brachiaira humidicola cv. Humidicola, a grass with high adaptation potential under temporal waterlogging conditions. Inadequate management practices and low soil fertility result in degradation, however, with important negative effects on pasture productivity and the quality and provision of (soil) ecosystem services–a situation that is likely to worsen in the near future due to climate change. Against this background, AGROSAVIA (Corporación Colombiana de Investigación Agropecuaria) selected Arachis pintoi CIAT 22160 cv. Centauro (Centauro) as a promising alternative for the sustainable intensification of livestock production and rehabilitation of degraded areas. This study assesses dual-purpose milk production in the foothills of the Colombian Orinoquía from an economic perspective. We compare two production systems: the Centauro–Brachiaira humidicola cv. Humidicola association (new system) and Brachiaira humidicola cv. Humidicola as a monoculture (traditional system). We used cashflow and risk assessment models to estimate economic indicators. The projections for economic returns consider changes in forage characteristics under regional climate change scenarios RCP (2.6, 8.5). The LIFE-SIM model was used to simulate dairy production. Results show that the inclusion of Centauro has the potential to increase animal productivity and profitability under different market scenarios. The impact of climatic variables on forage production is considerable in both climate change scenarios. Both total area and potential distribution of Centauro could change, and biomass production could decline. Brachiaira humidicola cv. Humidicola showed better persistence due to higher nitrogen levels in soil when grown in association with Centauro. The legume also provides a number of ecosystem services, such as improving soil structure and composition, and also contributes to reducing greenhouse gas emissions. This helps to improve the adaptation and mitigation capacity of the system.

An Application of ANN Ensemble for Estimating of Precipitation Using Regional Climate Models

Climate change scenarios are used for predicting future precipitation. More detailed regional climate change scenarios are being used through dynamic downscale based on global circulation model results. There is a global tendency to utilize simulated precipitation data from downscaled regional climate models (RCMs) suitable for each country. In Korea, there are studies for improving the accuracy of climate change scenario precipitation forecasts compared with observed precipitation. In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. The ANN ensemble of RCMs simulates the actual observed precipitation more accurately than the individual RCM. In particular, it is more effective inland than in coastal areas, where precipitation patterns are complex. Pearson correlation coefficient of ANN is high as 0.04 compared with MRA. It is expected that more detailed analysis will be possible if it is applied not only to four cities but also to other regions in Korea. If observed precipitation data are collected in sufficient quantity, the applicability of the ANN model will widen.

A climate service for ecologists: sharing pre-processed EURO-CORDEX regional climate scenario data using the eLTER Information System

Abstract. eLTER was a “Horizon 2020” project with the aim of advancing the development of long-term ecosystem research infrastructure in Europe. This paper describes how eLTER Information System infrastructure has been expanded by a climate service data product providing access to specifically pre-processed regional climate change scenario data from a state-of-the-art regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) for 702 registered ecological research sites across Europe. This tailored, expandable, easily accessible dataset follows FAIR principles and allows researchers to describe the climate at these sites, explore future projections for different climate change scenarios and make regional climate change assessments and impact studies. The data for each site are available for download from the EUDAT collaborative data infrastructure B2SHARE service and can be easily accessed and visualised through the Dynamic Ecological Information Management System – Site and Dataset Registry (DEIMS-SDR), a web-based information management system which shares detailed information and metadata on ecological research sites around the globe. This paper describes these data and how they can be accessed by users through the extended eLTER Information System architecture. The data and supporting information are available from B2SHARE. Each individual site (702 sites are available) dataset has its own DOI. To aid data discovery, a persistent B2SHARE lookup table has been created which matches the DOIs of the individual B2SHARE record with each DEIMS site ID. This lookup table is available at https://doi.org/10.23728/b2share.bf41278d91b445bda4505d5b1eaac26c (eLTER EURO-CORDEX Climate Service, 2020).

Desertification susceptibility over Rio de Janeiro, Brazil, based on aridity indices and geoprocessing

AbstractDesertification is a serious threat to human development and sustainability. This process is characterized by land degradation over areas with dry sub‐humid, semi‐arid and arid climate. In this work, climate susceptibility to desertification was identified for the state of Rio de Janeiro (SRJ), Brazil, the third most populated state in the country. The state is characterized by complex topography, and diverse climate and land uses. Two formulations for the aridity index were used: Ia, defined by Thornthwaite (1948, Geographical Review, 38(1), 55–94) and Penman (1953, Horticultural Congress, 2, 913–924), and D, a modification of Ia by Hare (1983, Climate and Desertification: A Revised Analysis, Geneva: World Meteorological Organization). Both estimates of the aridity require annual rainfall and potential evapotranspiration, which were calculated from the climatological water balance using times series of rainfall and air temperature (1961–2010) from 98 stations over the SRJ. Annual rainfall, evapotranspiration, water deficit and the aridity indices were then spatially interpolated over a 5 × 5‐km grid using a spline tension method. Both indices agree on the driest regions of the SRJ: Norte and Baixada Litorânea, near the northern coast, both under sub‐humid and semi‐arid climate. The index Ia indicated susceptibility to desertification over 2,305 km2 on the Norte and Baixada Litorânea regions, which correspond to 5% of SRJ area; similarly, the index D classified a smaller area over the Norte region under desertification (431 km2). These subregions, among the others also considered in this study, are of great importance for the agricultural, forestry and tourism industries and contribute immensely to the economic development of the state. Hence, the evaluation of susceptibility to desertification in those areas is essential to help prevent further land degradation and support policies formulation on long‐term sustainable management of land and water resources, especially under regional and global climate change scenarios. The methodology used in this work can be applied to studies of desertification in other regions of the world.

Climate change adaptation and carbon emissions in green urban spaces: Case study of Adelaide

Concentrations of building mass and insufficient greenery in cities are identified to contribute significantly to extended heat stress in the built environment, commonly known as the urban heat island (UHI) effect. This paper presents a scenario-based modelling built on climate change projections and potential alterations of urban greenery in 2030 and 2090 in Adelaide, South Australia. The model is based on regional climate change scenarios, potential urban surface cover alternatives and resulted urban microclimate variations in Adelaide. Projected energy demand variations and corresponding carbon emission are calculated in each scenario. Results indicate that an ideal urban landscape transformation scenario with 30% tree canopy can effectively decrease the surface temperature by 1 °C in winter and 3 °C in summer by 2090. It is estimated that having greener and more heat resilient public spaces could save a total carbon emission of 140,000 tone CO2e in Adelaide annually compared to a business-as-usual scenario. Urban greenery may be used as a mean of increased urban life resilience to climate change by reducing surface temperature, increasing urban resilience and decreasing the energy demand during summer.

Projected climate changes are expected to decrease the suitability and production of olive varieties in southern Spain

World olive production is based on the cultivation of different varieties that respond differently to abiotic factors. Climate change may affect the area of land suitable for olive cultivation and change production levels, thus causing serious damage to this economically-relevant and highly-productive olive grove agroecosystem. In Mediterranean regions such as Andalusia, one of the main areas of olive production, the effect of climate change seems threatening. Thus, our main aims are: (1) to examine the abiotic factors that characterise the current cultivated locations and predict the current and potential distribution of these locations; (2) to evaluate the effect of climate change (based on regional scenarios) on the future environmental suitability of each olive variety; and (3) to analyse the expected alteration in the annual olive production. We used the seven most-productive olive varieties in Andalusia and the wild olive species to develop Species Distribution Models (SDMs), coupled with soil properties, geomorphology, water balance and (bio-)climatic predictors at a fine scale. We also derived future climate projections to assess the effect of climate change on the environmental suitability and productivity of each olive variety. We found that soil pH was the most-important factor for most distribution models, while (bio-)climatic predictors - such as continentality index, summer and autumn precipitation and winter temperature - provided important contributions. In general, projections based on regional climate change scenarios point to a decrease in the area suitable for olive crops in Andalusia, due to an increase in evapotranspiration and a decrease in precipitation. These changes in suitable area are also projected to decrease olive production for almost all the olive-growing provinces investigated. Our findings may anticipate the effects of climate change on olive crops and provide early estimates of fruit production, at local and regional scales, as well as forming the basis of adaptation strategies.

Extreme sea levels in the Baltic Sea under climate change scenarios – Part 1: Model validation and sensitivity

Abstract. We analyze extreme sea levels (ESLs) and related uncertainty in an ensemble of regional climate change scenarios for the Baltic Sea. The ERA-40 reanalysis and five Coupled Model Intercomparison Project phase 5 (CMIP5) global general circulation models (GCMs) have been dynamically downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO (Rossby Centre regional atmospheric model version 4 – Nucleus for European Modelling of the Ocean). The 100-year return levels along the Swedish coast in the ERA-40 hindcast are within the 95 % confidence limits of the observational estimates, except those on the west coast. The ensemble mean of the 100-year return levels averaged over the five GCMs shows biases of less than 10 cm. A series of sensitivity studies explores how the choice of different parameterizations, open boundary conditions and atmospheric forcing affects the estimates of 100-year return levels. A small ensemble of different regional climate models (RCMs) forced with ERA-40 shows the highest uncertainty in ESLs in the southwestern Baltic Sea and in the northeastern part of the Bothnian Bay. Some regions like the Skagerrak, Gulf of Finland and Gulf of Riga are sensitive to the choice of the RCM. A second ensemble of one RCM forced with different GCMs uncovers a lower sensitivity of ESLs against the variance introduced by different GCMs. The uncertainty in the estimates of 100-year return levels introduced by GCMs ranges from 20 to 40 cm at different stations and includes the estimates based on observations. It is of similar size to the 95 % confidence limits of 100-year return levels from tide gauge records.

Climate change alters elevational phenology patterns of the European spruce bark beetle (Ips typographus).

The European spruce bark beetle Ips typographus is the most important insect pest in Central European forests. Under climate change, its phenology is presumed to be changing and mass infestations becoming more likely. While several studies have investigated climate effects across a latitudinal gradient, it remains an open question how phenology will change depending on elevation and topology. Knowing how an altered climate is likely to affect bark beetle populations, particularly across diverse topographies and elevations, is essential for adaptive management. We developed a time-varying distributed delay model to predict the phenology of I. typographus. This approach has the particular advantage of capturing the variability within populations and thus representing its stage structure at any time. The model is applied for three regional climate change scenarios, A1B, A2 and RCP3PD, to the diverse topography of Switzerland, covering a large range of elevations, aspects and slopes. We found a strong negative relationship between voltinism and elevation. Under climate change, the model predicts an increasing number of generations over the whole elevational gradient, which will be more pronounced at low elevations. In contrast, the pre-shift in spring swarming is expected to be greater at higher elevations. In comparison, the general trend of faster beetle development on steep southern slopes is only of minor importance. Overall, the maximum elevation allowing a complete yearly generation will move upwards. Generally, the predicted increase in number of generations, earlier spring swarming, more aggregated swarming, together with a projected increase in drought and storm events, will result in a higher risk of mass infestations. This will increase the pressure on spruce stands particularly in the lowlands and require intensified management efforts. It calls for adapted long-term silvicultural strategies to mitigate the loss of ecosystem services such as timber production protection against rockfall and avalanches and carbon storage.

Surface Heat Budget over the North Sea in Climate Change Simulations

An ensemble of regional climate change scenarios for the North Sea is validated and analyzed. Five Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs) using three different Representative Concentration Pathways (RCPs) have been downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO. Validation of sea surface temperature (SST) against different datasets suggests that the model results are well within the spread of observational datasets. The ensemble mean SST with a bias of less than 1 ∘ C is the solution that fits the observations best and underlines the importance of ensemble modeling. The exchange of momentum, heat, and freshwater between atmosphere and ocean in the regional, coupled model compares well with available datasets. The climatological seasonal cycles of these fluxes are within the 95% confidence limits of the datasets. Towards the end of the 21st century the projected North Sea SST increases by 1.5 ∘ C (RCP 2.6), 2 ∘ C (RCP 4.5), and 4 ∘ C (RCP 8.5), respectively. Under this change the North Sea develops a specific pattern of the climate change signal for the air–sea temperature difference and latent heat flux in the RCP 4.5 and 8.5 scenarios. In the RCP 8.5 scenario the amplitude of the spatial heat flux anomaly increases to 5 W/m 2 at the end of the century. Different hypotheses are discussed that could contribute to the spatially non-uniform change in air–sea interaction. The most likely cause for an increased latent heat loss in the central western North Sea is a drier atmosphere towards the end of the century. Drier air in the lee of the British Isles affects the balance of the surface heat budget of the North Sea. This effect is an example of how regional characteristics modulate global climate change. For climate change projections on regional scales it is important to resolve processes and feedbacks at regional scales.

Lake or Sea? The Unknown Future of Central Baltic Sea Herring

The understanding of natural variability of harvestable fish stocks is of fundamental interest for their management; however, time series are often biased by the impact of fisheries. Observations show that the mean weight of central Baltic Sea herring (Clupea harengus), a quantity unbiased by fisheries, is indirectly influenced by the Atlantic Multi-decadal Oscillation (AMO) via a complex mediator chain, in which a transition from a marine to a freshwater state plays a major role. This observed trend is also projected for the future in regional climate change scenarios. The consequence of increasing precipitation is a reduction in the marine habitat, a change in the prey community and a decrease in the mean weight of three-year-old central Baltic Sea herring from 50-70 g in the late 1970s to 25-30 g today. The reduction in weight over this period represents a reduction in landings of approximately one million tons of herring and an economic loss of the order of €100 million.

Climate change impact assessment on water security in South Africa: A case study in a semi-arid river basin

The main aim of this paper is to assess the impact of regional climate change scenarios on the availability of water resources in a semi-arid river basin in South Africa using a hydrological model called Soil and Water Assessment Tool (SWAT). In this paper, climate change data was derived from two downscaling approaches, namely statistical downscaling experiment (SDE) and dynamic downscaling (CORDEX). These were derived from the GCM simulations of the Coupled Model Inter-comparison Project Phase-5 (CMIP5) and across two greenhouse gas emission scenarios known as Representative Concentration Pathways (RCP) 4.5 and 8.5. The spatial resolution of the dataset for the SDE method is 25 km × 25 km and 50 km × 50 km for the CORDEX method. Six GCM models were used for SDE set of data and four for the CORDEX set of data. SWAT model was run using these data for a period of up to mid-century (2020 – 2050) for SDE and for a period of up to the end of this century (2020 – 2100) for CORDEX data. The results were then compared with long-term historical data (1975-2005). Comparison of measured data with simulated historical data showed strong correlation (R2 = 0.95 for SDE data and R2 = 0.92 for CORDEX data), which is indicative of the reliability of projected future climate.

Environmental Modelling of Forest Vegetation Zones as A Support Tool for Sustainable Management of Central European Spruce Forests

Abstract The impact of climate change on forest ecosystems may manifest itself by a shift in forest vegetation zones in the landscape northward and into higher elevations. Studies of climate change-induced vegetation zone shifts in forest ecosystems have been relatively rare in the context of European temperate zone (apart from Alpine regions). The presented paper outlines the results of a biogeographic model of climatic conditions in forest vegetation zones applied in the Central European landscape. The objective of the study is a prediction of future silvicultural conditions for the Norway spruce (Picea abies L. Karst.), which is one of the principal tree species within European forests. The model is based on a general environmental dependence of forest vegetation zones on the long-term effect of altitudinal and exposure climates defined by the mean and extreme air temperatures and the amount and distribution of atmospheric precipitation. The climatological data for the model were provided by a validated regional climate database for 2010 – 2090 according to the SRES A1B scenario, bound to specific geo-referenced points in the landscape. The geobiocoenological data in the model were provided by the Biogeography Register database which contains ecological data on the landscape bound to individual cadastres of the entire Czech Republic. The biogeographic model applies special programs (the FORTRAN programming language) in the environment of geographic information systems. The model outputs can be clearly graphically visualized as scenarios of predicted future climatic conditions of landscape vegetation zones. Modelling of the regional scenario of changes in the climatic conditions of forest vegetation zones reveals that in the prediction period of 2070 and beyond, good and very good climatic conditions for the cultivation of forests with dominant Norway spruce will be found only in some parts of its today’s native range in forest vegetation zones 5 – 8. Based on the results provided by the regional scenario, the authors of this paper recommend fundamental reassessment of the national strategy of sustainable forest management in the Czech Republic, stipulating that the current practice of spruce cultivation be reduced only to areas specifically defined by the biogeographic model. The paper shows that biogeographic models based on the concept of vegetation zoning can be applied not only in regional scenarios of climate change in the landscape but also as support tools for the creation of strategies of sustainable forest management.

Simplification of shade tree diversity reduces nutrient cycling resilience in coffee agroforestry

Abstract Agroforestry systems are refuges for biodiversity and provide multiple ecosystem functions and services. Diverse multispecies shade tree canopies are increasingly replaced by monospecific shade, often dominated by non‐native tree species. The loss of tree diversity and the nature of the dominating tree can have strong implications for ecosystem functions, for example, nutrient cycling ultimately reducing crop production. To understand direct and indirect impacts of shade trees on nutrient cycling and crop production, we studied coffee agroforestry systems in India along a gradient from native multispecies canopies to Grevillea robusta (Proteaceae) ‐dominated canopy cover. We identified 25 agroforests, across a broad rainfall and management gradient and assessed litter quantity and quality, decomposition, nutrient release, soil fertility and coffee nutrient limitations. Increasing G. robusta dominance affected nutrient cycling predominantly by; (a) changing of litter phenology, (b) reducing phosphorus (P), potassium (K), magnesium (Mg), boron (B) and zinc (Zn) inputs via litterfall, decelerated litter decomposition and immobilization of P and Zn due to low‐quality litter, (c) reducing soil carbon (C) and micronutrients (especially sulphur (S), Mg and B). Coffee plants were deficient in several nutrients (nitrogen (N), calcium (Ca), manganese (Mn), Mg and S in organic and B in conventional management). (d) Overall G. robusta‐dominated agroforests were characterized by a reduction of P cycling due to low inputs, strong immobilization while decomposition and antagonistic effects on its release in litter mixtures with coffee. Synthesis and applications. The conversion of shade cover in coffee agroforestry systems from diverse tree canopies to canopies dominated by Grevillea robusta (Proteaceae) reduces the inputs and cycling of several micro‐ and macronutrients. Soil fertility is therefore expected to decline in Grevillea robusta‐dominated systems, with likely impacts on coffee production. These negative effects might increase under the longer dry periods projected by regional climate change scenarios due to the pronounced litter phenology of Grevillea robusta. Maintaining diverse shade canopies can more effectively sustain micro‐ and macronutrients in a more seasonal climate.

Putting into action the REGCM4.6 regional climate model for the study of climate change, variability and modeling over Central America and Mexico

What: International experts and attendees from several countries of Central America, Mexico, the Caribbean (CAM), and South America (SA) met to discuss regional issues on climate variability and climate change to learn the use of the non-hydrostatic version of the International Center for Theoretical Physics (ICTP) RegCM4.6 model, and to establish a regional modeling scientific community for understanding the physics of climate processes and the generation of regional climate change scenarios. When: 14-18 November 2016. Where: Center for Geophysical Research (CIGEFI in Spanish) and School of Physics, University of Costa Rica (UCR), San Jose, Costa Rica.