Institute For Climate And Society Research Articles

From research to practice: Adapting agriculture to climate today for tomorrow in Ethiopia

Eighty percent of the world's agriculture is rainfed, making it highly vulnerable to climate fluctuations and stresses, such as those brought about by climate variability and change. Sub-Saharan Africa and Ethiopia in particular have experienced a significant increase in climate variability over the past decade, which has led to more frequent weather extremes such as floods and droughts. Because 85% of Ethiopia depends upon agriculture for its livelihoods, such rainfall shortages or excesses can impede food production, access to financial and natural assets, and the ability to recover in subsequent crop seasons. This means that climate variability in agriculture not only affects the availability of the food Ethiopians consume, but also the income of its smallholder farmers. Variability in rainfall and temperature can also have adverse effects on livestock and the pastoralists whose livelihoods depend upon it. Thus, all development planning and practice in the agriculture and related sectors need to take climate variability and long-term climate change into account. Climate services can contribute to the alleviation of a range of climate-sensitive development challenges, including agricultural production and food security. The Adapting Agriculture to Climate Today for Tomorrow (ACToday) approach of the International Research Institute for Climate and Society (IRI), Columbia University, USA, aims to develop climate service solutions through enhancement of the availability and effectiveness of climate information in national policy, planning, management, and other decision-making processes in countries that are particularly dependent on agriculture and vulnerable to the effects of climate variability and change. It targets improved food security, nutrition, environmental sustainability and economic outcomes in these countries by promoting the use of climate information and services to manage current climate risks, while laying the foundation for adaptation to future climatic conditions. In this Perspective, we share experiences from the implementation of the ACToday project and approach in Ethiopia, outlining its accomplishments and challenges. In doing so, we characterize best practices and pitfalls to avoid to ensure climate knowledge and information truly meet the needs of climate-informed decision making and climate-smart policy and planning. We also outline pragmatic guidance to ensure activities designed to evolve climate research into services are done so appropriately, responsibly, and sustainably to bridge the gap between those who produce climate information and those who ultimately use it.

Analysis of Climate Change Indices in Musanze District, Rwanda

This study used daily rainfall, minimum temperature, and maximum temperature data from the Rwanda Meteorology Agency (Meteo Rwanda) for the years 1983 - 2016 to analyze climate change indices in the Musanze district of Rwanda. Rainfall and temperature extreme indices were determined using the methodology provided by Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Climate Data Tool (CDT), which is R-based package developed by International Research Institute for Climate and Society (IRI) was used to compute the indices. Sen's slope estimator and the modified Mann-Kendall (m-MK) test were both applied to identify trends and determine the degree of change. The Monthly Temperature (TXx, TXn, TNx, and TNn) indices generally showed a warming in the eastern and a cooling in the western parts of the district, according to the trend results; however, the Minimum tail of temperature indices showed a slightly reduced cooling which is a sign of warm nights and hot days in highland areas of volcanic region. For the same period, the district's eastern regions have higher precipitation intensity and wetness, which gradually declines toward the middle and western regions. In addition, a considerable upward trend in the annual total precipitation for days above the 95th percentile (very wet days) and the annual count for precipitation equal or greater than 20 mm (very heavy precipitation days). Additionally, temperature indices showed a large rise in the minimum and maximum values of daily minimum temperatures, annual minimum and maximum values of daily maximum temperatures, and the percentage of days with daily maximum temperatures above the 90th percentile (warm days). In view of these evolving trends in weather and climate patterns, the study recommends local farmers and other stakeholders involved in socioeconomic activities in the district to implement the necessary climate change mitigation and adaptation measures.

The Climate Data Tool: Enhancing Climate Services Across Africa

Climate services can contribute to alleviating a range of climate-sensitive development challenges, including those of agricultural production and food security. However, the use of climate data for research and applications in Africa has been scanty, mainly due to poor availability of and access to quality climate data. Weather stations are sparse, and their number has been declining. Access to existing climate data is a challenge mainly because of national data policies, low financial investment, lack of dissemination capacity and tools, and high access costs. The ENACTS (Enhancing National Climate Services) initiative led by the International Research Institute for Climate and Society (IRI) at Columbia University has been tackling this problem by working with National Meteorological Services (NMS) in Africa and in other developing countries. This initiative helps NMS to improve data availability and quality, by combining quality-controlled data from national observation networks with satellite estimates for rainfall and climate model reanalysis products for temperature. This requires access to an easy-to-use and freely available tool for preforming the tasks required to generate the data, as well as using the generated data. Most NMS in Africa do not have access to such a tool. To meet this significant need, the IRI developed such a tool in-house. This is the Climate Data Tool (CDT), which is an open-source, R-based software with an easy-to-use a graphical user interface (GUI). It can be used for data organization, quality control, combining station data with satellite and reanalysis data, evaluating merged and inputs datasets, performing an array of analyses, and visualization. The CDT software has been evolving over that last seven years with inputs from the NMS themselves. Now, it has become a powerful and user-friendly tool, and has been installed in over 20 countries in Africa alone.

ENACTS: Advancing Climate Services Across Africa

Despite recent and mostly global efforts to promote climate services in developing countries, Africa still faces significant limitations in its institutional infrastructure and capacity to develop, access, and use decision-relevant climate data and information products at multiple levels of governance. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University's International Research Institute for Climate and Society (IRI), strives to overcome these challenges by co-developing tailored, actionable, and decision-relevant climate information with and for a wide variety of users at the local, regional, and national levels. This is accomplished through an approach emphasizing direct engagement with the National Meteorological and Hydrological Services (NMHS) and users of their products, and investments in both technological and human capacities for improving the availability, access, and use of quality climate data and information products at decision-relevant spatial and temporal scales. In doing so, the ENACTS approach has been shown to be an effective means of transforming decision-making surrounding vulnerabilities and risks at multiple scales, through implementation in over a dozen countries at national level as well as at the regional levels in both East and West Africa. Through the ENACTS approach, challenges to availability of climate data are alleviated by combining quality-controlled station observations with global proxies to generate spatially and temporally complete climate datasets. Access to climate information is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products. Use of the generated climate data and the derived information products is promoted through raising awareness in relevant communities, training users, and co-production processes.

Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast

CONTEXTGlobal climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. OBJECTIVEWe aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. METHODSWe used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620–668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e., sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). RESULTS AND CONCLUSIONSThe IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the total predictions developed for this scenario considering all weather stations during the study period (2003–2016), while it was less accurate in forecasting above-normal precipitation scenarios (60% of success). We found that earlier sowing dates were a better strategy for years when an above-normal precipitation forecast was released for the December–January-February period (4.7 Mg ha−1 average seed yield). In contrast, delayed sowing dates were more appropriate for below-normal precipitation forecasts (3.7 Mg ha−1 average seed yield). Applying season-specific management practices farmers could potentially increase their soybean yields by up to 0.6 and 1.6 Mg ha−1, in years with below- or above-normal forecasted precipitations, respectively. The benefit of season-specific management will depend on the interaction among all management practices, the effective capacity of farmers to implement it, and the risk profile the farmer adopts and it is exposed to. SIGNIFICANCEHere we built a novel approach to assess the impact of considering seasonal precipitation forecasts for optimizing crop production. This assessment provided insights on how farmers can use seasonal precipitation forecasts to optimize rainfed soybean yield for a specific cropping season.

Transforming Access to and Use of Climate Information Products Derived from Remote Sensing and In Situ Observations

Making climate-sensitive economic sectors resilient to climate trends and shocks, through adaptation to climate change and managing uncertainties associated with climate extremes, will require effective use of climate information to help practitioners make climate-informed decisions. The provision of weather and climate information will depend on the availability of climate data and its presentation in formats that are useful for decision making at different levels. However, in many places around the world, including most African countries, the collection of climate data has been seriously inadequate, and even when available, poorly accessible. On the other hand, the availability of climate data by itself may not lead to the uptake and use of such data. These data must be presented in user-friendly formats addressing specific climate information needs in order to be used for decision-making by governments, as well as the public and private sectors. The generated information should also be easily accessible. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society (IRI), has been making efforts to overcome these challenges by supporting countries to improve the available climate data, as well as access to and use of climate information products at relevant spatial and temporal scales. Challenges to the availability of climate data are alleviated by combining data from the national weather observation network with remote sensing and other global proxies to generate spatially and temporally complete climate datasets. Access to climate information products is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products such as maps and graphs.

Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

Pricing rainfall derivatives in the equatorial Pacific

PurposeIn the equatorial Pacific, rainfall is affected by global climate phenomena, such as El Niño Southern Oscillation (ENSO). However, current publicly available methodologies for valuing weather derivatives do not account for the influence of ENSO. The purpose of this paper is to develop a complete framework suitable for valuing rainfall derivatives in the equatorial Pacific.Design/methodology/approachIn this paper, we implement a Markov chain for the occurrence of rain and a gamma model for the conditional quantities using vector generalized linear models (VGLM). The ENSO forecast probabilities reported by the International Research Institute for Climate and Society (IRI) are included as independent variables using different alternatives. We then employ the Esscher transform to price rainfall derivatives.FindingsThe methodology is applied and calibrated using the historical rainfall data collected at the El Dorado airport weather station in Bogotá. All the estimated coefficients turn out to be significant. The results prove more accurate than those of Markovian gamma models based on purely statistical descriptions of the daily rainfall probabilities.Originality/valueThis procedure introduces the novelty of incorporating variables related to the climatic phenomena, which are the forecast probabilities regularly published for the occurrence of El Niño and La Niña.

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Predictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.

Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Predictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI). In conjunction with the GLM (generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

A Geo-Informatics Technique for the Management of Meningitis Epidemic Distributions in Northern Nigeria

This study aims at examining and mapping the spatiotemporal distribution of meningitis epidemic, in relation to climate variability, using GIS and Remote Sensing techniques. Using the northern part of Nigeria as a case study, data on meningitis epidemic were obtained from the archive of National Bureau of Statistics, Nigeria for the periods between 1998 and 2013. The data were updated with collection from Nigeria Demographic and Health Survey (NDHS). Also, Nigerian Ministry of Health has compiled consistent statistics on meningitis incidence for the periods. A meningitis distribution map was derived from an environmentally-driven form of predicted probability of epidemic experience as it is in International Research Institute for Climate and Society (IRI) Database. The results showed that Meningitis Epidemic is very high during months with low rainfall. Thus, seasonality of rainfall and temperature are important determinants of Meningitis Epidemic incidence in the Northern part of Nigeria. Therefore, it can be confirmed, as cited in some literatures, that the distribution of the epidemics has a strong association with the environment, especially climate variability. Although meningitis surveillance systems in Nigeria have improved, they still fall short of the sensitivity required to demonstrate incidence changes in vaccinated and non-vaccinated cohorts and complementary approaches may be needed to demonstrate the impact of the vaccines. There is however, a need for a new technology and innovation like an integrated GIS, and other environmental modeling system, to allow health practitioners as well as policy makers, for better management, productivity and profitability.

Remote-Sensing Applications for Environmental Health Research

More than 1,000 manmade satellites currently orbit our planet.1 Some are near the edge of the Earth’s atmosphere just a few hundred kilometers up. Others are tens of thousands of kilometers above us.2 They aid in communication, navigation, defense, and science. A small number3,4 play a critical and quickly expanding role: monitoring the Earth’s surface and atmosphere to track environmental conditions that are intimately tied to human health. A number of new Earth-observing missions are planned for the next decade, including Sentinel-5 aboard the European Space Agency’s MetOp Second Generation satellites (pictured).48 In the meantime researchers are finding new uses for the satellite ... Researchers and government agencies worldwide already use satellite data to monitor air pollutants, infectious disease epidemics, harmful algal blooms (HABs), climate change, and more. But as current research indicates, that’s only the beginning of what we can do with the technology, broadly referred to as “remote sensing.” In the coming years, new satellites will offer higher-resolution imagery in conjunction with more robust and precise algorithms to process the data they deliver. As a result, researchers expect to dramatically expand their ability to view and understand Earth’s land, water, and air, from its remotest ocean waters to its largest cities. The National Aeronautics and Space Administration (NASA) launched its first satellite in 1958,5 and TIROS-1, the country’s first meteorological satellite, came 2 years later.6 Within a few decades members of the epidemiological and public health communities began actively looking at satellite data, says John Haynes, program manager of the NASA Applied Sciences Health and Air Quality Applications Program. In recent years interest in remote-sensing data has soared, with newer avenues being developed and fine-tuned, including air-quality measurements and vector-borne disease projections. “There’s really been a paradigm shift in the use of remote sensing for public health issues,” Haynes says. “Every year there seems to be more and more interest.” Indeed, by March 2015 NASA will have launched 6 Earth-observing missions in 12 months,7 more than in any year in at least a decade.8 New launches include a “global precipitation observatory” that will make frequent global measurements of rain and snowfall, plus one satellite designed to measure soil moisture and another that will measure how carbon moves through the Earth’s atmosphere, land, and oceans. In addition, the International Space Station will receive three new instruments, one that will observe how winds behave around the world, one that will measure clouds and aerosols (particles suspended in the atmosphere)—two variables that remain difficult to predict in climate-change models—and one that will take global, long-term measurements of key components of the Earth’s atmosphere, including aerosols and ozone.9 The momentum will carry through at least the next 8 or so years, with NASA and other space agencies in Europe and Asia planning to launch new satellites that will provide even higher-resolution snapshots of the Earth. Along with technological and scientific advances, a third development is leading to new and improved applications of satellite data: NASA and the National Oceanic and Atmospheric Administration (NOAA) have made their satellite data available free of charge, Haynes says, while the European Space Agency (ESA) has reduced prices and promised to provide free access to data from its next generation of instruments. “More people use the data, and you get more out of it than when you try to restrict it,” says Raphael Kudela, an oceanographer at the University of California, Santa Cruz, who uses satellite imagery to study HABs. This free sharing of data has been instrumental in his field, allowing researchers at institutions around the world to study HABs from above and to improve systems to track and predict them.

The East African Long Rains in Observations and Models

Abstract Decadal variability of the East African precipitation during the season of March–May (long rains) is examined and the performance of a series of models in simulating the observed features is assessed. Observational results show that the drying trend of the long rains is associated with decadal natural variability associated with sea surface temperature (SST) variations over the Pacific Ocean. Empirical orthogonal function (EOF), linear regression, and composite analyses all show the spatial pattern of the associated SST field to be La Niña like. The SST-forced International Research Institute for Climate and Society (IRI) forecast models are able to capture the East African precipitation climatology, the decadal variability of the long rains, and the associated SST anomaly pattern but are not consistent with observations from the 1970s. The multimodel mean of the SST-forced models from the Coupled Model Intercomparison Project phase 5 (CMIP5) Atmospheric Model Intercomparison Project (AMIP) experiment captures the climatology and the drying trend in recent decades. The fully coupled models from the CMIP5 historical experiment, however, have systematic errors in simulating the East African precipitation climatology by underestimating the long rains while overestimating the short rains. The multimodel mean of the historical simulations of the long rains anomalies, which is the best estimate of the radiatively forced change, shows a weak wetting trend associated with anthropogenic forcing. The SST anomaly pattern associated with the long rains has large discrepancies with the observations. The results herein suggest caution in projections of East African precipitation from CMIP5 or the relationship between the East African precipitation and the SST spatial pattern found in paleoclimate studies with coupled climate models.

Capacity development through the sharing of climate information with diverse user communities

Abstract The International Research Institute for Climate and Society (IRI) is a premier global research and capacity development institution focused on enhancing society’s capability to understand, use, manage and evaluate climate information. Its goal is to strengthen the technical capacity of individuals and organizations in order to increase the demand for climate information and its application. In the early years of IRI, staff trained local meteorologists and decision makers, often in developing countries, on using information products that IRI’s forecast group judged would be of value. However, in the last decade, IRI’s approach to training has evolved into one that is more user-driven. Today, users are brought on board during the development of training curricula to learn what information and methodologies would be most useful for their local needs. This establishes a sense of trust and ownership in the training process. Approaches are tailored to different contexts and communities, and focus is placed on cultivating long-lasting partnerships. The work presented in this article demonstrates the evolution of IRI’s capacity building and development processes. It highlights how the IRI implements capacity development activities, with a particular emphasis on the early engagement of practitioners and end users. Numerous examples are provided of the successes related to knowledge gains and to the concrete applications of climate information that can occur when the design and implementation of activities are preformed in an integrative manner. This approach builds channels for interaction among practitioners, policymakers, scientists and other societal decision making groups. Though it has seen numerous successes, the IRI is continually evaluating its capacity development methods and looking for ways to improve upon them.

The role of targeted climate research at the IRI

Advances in our fundamental understanding of the physical climate system provided the necessary scientific underpinnings for the routine production of reliable seasonal climate forecasts and ultimately, the birth of the International Research Institute for Climate and Society (IRI). While recognizing that the successful adoption of climate information into various decision-making settings requires an iterative approach between the developers and users of that information, since its inception the IRI has also recognized the critical role of basic climate research in generating new climate knowledge. Given its mission, such basic research targets specific regions and questions that are framed by practical considerations of how climate variations are impacting, or may impact, various sectors of society. Analogous to its role in underpinning the development of seasonal forecasts, an enhanced understanding of relevant aspects of the physical climate is viewed as a critical input to the larger process of developing effective strategies for the management of climate-related risks. Here, four examples of targeted climate research undertaken at the IRI are presented covering a range of time scales, from sub-seasonal variability to long-term climate trends. A diverse set of geographic locations is considered, which includes the Sahel, southeastern South America, the Philippines and Indonesia. These four examples were selected to indicate the broad range of use-inspired basic research questions that have been addressed in regions where the IRI is engaged in a broader set of activities to develop actionable climate information. While many institutions are engaged in basic climate research, having the expertise and capacity to do so within the IRI provides it with the necessary flexibility to target its work towards specific climate-related questions in specific regions of the world.

Bridging critical gaps in climate services and applications in africa

Sound climate risk management requires access to the best available decision-relevant climate information and the ability to use such information effectively. The availability and access of such information and the ability to use it is challenging, particularly throughout rural Africa. A gap analysis published by the International Research Institute for Climate and Society (IRI) and the Global Climate Observing System (GCOS) in 2005 explored these challenges in detail and identified four key gaps: (i) gaps in integration of climate into policy; (ii) gaps in integration of climate into practice at scale; (iii) gaps in climate services; and (iv) gaps in climate data. Though this document was published nearly nine years ago, the gaps it highlighted are still relevant today. In the last decade, IRI has been making efforts to address these critical issues in a systematic way through projects and partnerships in Africa. This paper describes IRI’s efforts in Ethiopia, a country particularly prone to climate related risks. Here we outline a creative solution to bridge the gaps in the availability, access and use of national climate information through the Enhancing National Climate Services (ENACTS) initiative. We then discuss how policy and practice has changed as a result of IRI engagement in the development of climate services in the water, public health and agricultural sectors. The work in Ethiopia is indicative of the efforts IRI is implementing in other countries in Africa and in other parts of the world.

Climate information, outlooks, and understanding–where does the IRI stand?

The International Research Institute for Climate and Society (IRI) began providing user-oriented climate information, including outlooks, in the late 1990s. Its climate products are intended to meet the needs of decision makers in various sectors of society such as agriculture, water management, health, disaster management, energy, education and others. They try to link the current state of the science in climate diagnostics and prediction to the dynamically evolving practical needs of users worldwide. Because most users are not climate scientists, the manner in which the information is provided is of paramount importance in order for it to be understandable and actionable. Non-technical language that preserves essential content is required, as well as graphics that are intuitive and largely self-explanatory. The climate information products themselves must be in demand by users, rather than ones that the producers believe would be best. These requirements are consistent with IRI’s mission of improving human welfare, particularly in developing countries where decision makers may not initially know what climate information they need, and how best to use it. This lack of initial understanding requires back-and-forth communication between the producers and users to initiate and sustain uptake and beneficial use of the information. Backed by its climate prediction research, the IRI’s climate information products span time-scales of days to decades. Experience on the statistics of daily weather behavior within seasons has been gleaned, as has the benefits of statistical and dynamical spatial downscaling of predictions. By providing views in a progressive sequence of temporal scales, IRI’s products help demonstrate that preparation for interannual climate variability may be the best preparation for decadal variability and trends related to climate change.

US Investments in International Climate Research and Applications: reflections on contributions to interdisciplinary climate science and services, development, and adaptation

Climate variability and change have long posed substantial challenges for social, economic and natural systems throughout the world. Incorporating information about climate fluctuations and their impacts is an increasingly important component of risk management and planning in key socio-economic sectors. Understanding and meeting these challenges through effective risk management strategies that foster preparedness, impact mitigation, and adaptation requires a long-term investment in and commitment to sound science, transition, and translation of information among communities, and the application and evaluation of climate services and decision support resources. This science should be developed, implemented and applied in the context of the practical needs and capabilities of communities, stakeholders, and socio-economic sectors if it is to inform resource management challenges, and contribute to the field of study of climate, climate information services, preparedness and adaptation.During the early 1990s, the United States expressed a desire to capitalize on emerging developments in atmospheric and oceanic research and early climate impact studies, in order to develop the institutional capacity to meet these needs on global and regional scales. As a member of the US Global Change Research Program (USGCRP), and working in partnership with the international scientific and governmental community, NOAA led the development of an international research institute, which is known today as the International Research Institute for Climate and Society (IRI). Over several decades - from the initial conceptual and demonstration phases, to today’s mature institution – IRI had a substantial influence in linking science and action to risk management. This essay briefly reflects upon the initial vision for and evolution of the IRI; the importance of investments in endeavors of this nature to the US climate research and services communities, including model development, climate prediction and integrated research and assessments; and the value of such an institution to a Federal entity concerned with development and risk management in developing countries - the US Agency for International Development (USAID). We conclude with some thoughts about future directions.

The International Research Institute for Climate & Society: why, what and how

A climate-informed and climate-ready world is possible. Large investments are being made toward adaptation and resilience to climate change, but many of those investments are separated from the more immediate climate-related vulnerabilities and opportunities that society faces. Information is increasingly available that could be used to guide action; however, information alone is not sufficient. Research at the International Research Institute for Climate and Society (IRI) since 1996 has led to the identification of the several guiding principles to scope and address climate-related challenges to decision- and policy-makers at local-to-regional scale. These include climate-related information, such as assessment of the main vulnerabilities to climate variability and change in countries or regions, and the provision of climate information, products and tools to support decisions, including financial tools that are appropriate to the climate-related risk and that can mediate residual risk. The guiding principles also include identifying the technologies and practices that optimize results in coming years, demonstration of the usefulness of climate information to support climate-related decisions, training and capacity building, and partnerships for research and implementation. This essay introduces the evolution of the IRI and its work that is then elaborated through a series of articles that constitute a special issue of Earth Perspectives: Transdisciplinarity Enabled. The collection of articles provides insight into the science and process that lead to better climate-informed choices. Part of the collection of articles in the special issue covers specific stories of local-to-regional engagement with partners to address climate-related problems. Other articles represent how we do what we do, in particular highlighting the research, the climate forecast effort, and the IRI Data Library. Finally, there are two papers offered from partners that have long-time engagement with the IRI.