IRI@AGU: Schedule of Events 2018
A range of IRI’s areas of expertise will be represented at this year’s annual meeting of the American Geophysical Union (AGU). Below is the schedule of IRI’s posters and presentations in sequential order.
Climate Services Research and Development: Adapting to Climate Today
Lisa M Goddard is the primary convener for both a presentation and poster session on this topic.
The United Nations estimates that by 2030, between $60 and $182 billion will be needed each year for adaptation globally. However, counties of all economic status are not adapted to current climate variability. Society is impacted by climate now and must better adapt and manage climate today, not just focus on climate expectations at the end of the century. The Global Framework for Climate Services (GFCS) was established to address climate-related risks across timescales, for specific socio-economic sectors. These efforts can be informed and advanced by focused collaboration across disciplines and national borders. For this session, we invite presentations on the challenges and opportunities associated with the design, implementation, and evaluation of projects that bring climate information into real world decisions. We particularly welcome presentations that highlight methods and processes by which the needs, opportunities, and gaps in climate information and knowledge are identified and incorporated in climate services development.
The ENACTS Approach: Transforming Climate Services in Africa, One Country at a Time
Almost all of the development sector requires climate data and information systems. Effective understanding of the climate system and management of climate variability and change requires that climate information be used effectively in planning and that climate knowledge and risk associated to it be incorporated routinely into development decisions. As a result, availability of and access to climate data and information products is critical to achieving climate resilient development. Unfortunately, climate information is not widely used in Africa, and many other parts of the world, to make development decisions. This is mainly because useful information is often not available or, if it does exist, is inaccessible to those that need it most. There are different efforts under way to alleviate the problem of data availability and use. One of these efforts is the ENACTS (Enhancing National Climate Services) initiative lead by the International Research Institute for Climate and Society (IRI), The Earth Institute at Columbia University. The ENACTS initiative delivers robust climate data, targeted information products and training specifically relevant to the needs of decision makers at multiple levels, empowering a diverse range of actors to use past, present and future climate information. ENACTS has so far been implemented 12 countries and at two Regional Climate Centers in Africa.
The development of climate-informed decision-support tools for the prevention and control of Aedes-borne diseases in the US and transboundary regions
Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50-100 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth and more frequent travels…Historical, current and forecasted climate data can be combined with disease models to improve climate-sensitive health planning and targeting of resources. For infectious disease models, the goal has frequently been to explore different interventions scenarios in order to help priority-setting for policy makers (Heesterbeek et al. 2015). However, in recent years there is increasing interest in using models for real-time forecasting (Yang et al. 2014), although there remains a significant gap in the operational readiness of the numerous forecasting systems presented in the literature (Corley et al. 2014). We propose to co-develop a monitoring and forecasting system for environmental suitability of transmission of Aedes-borne diseases for the US and the Caribbean, using innovative state-of-the-art ento-epidemiological models, climate observations, and seasonal and sub-seasonal forecasts.
Building Resilience to Extreme Weather and Climate Impacts in Developing Countries
Extreme weather and climate are part of our climate system. However, societies worldwide are not adapted to these events. The potential for climate change to fuel more frequent or severe weather and climate extremes provides additional motivation to adapt and build resilience…Most developing countries lack useable observations of the past and present. Thus, they also lack timely monitoring of fast-onset extremes like tropical cyclones or slow-onset extremes like drought, and they lack meaningful weather and climate predictions. As a result food insecurity and other meteorological crises devastate those countries, and the humanitarian community expends tremendous human and financial resources in response…Two examples will be presented. In these, enhanced observational datasets, subseasonal-to-seasonal forecasts with reliable, quantified uncertainty, and cost-benefit analysis have been applied through collaboration with communities and decision makers to lessen climate shocks to the agricultural sector and make humanitarian aid go further.
Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance for Agriculture in the Developing World: an Ethiopia Case Study
New insurance products use remote sensing to trigger weather insurance payments for growing numbers of low-income farmers in developing countries. Because projects typically target data-poor regions with little validation information available, and are built upon participatory farmer approaches, data collected from farmers are increasingly relied upon to design accurate products. However, there are well known limitations in farmer recollections that could lead to problematic insurance if used inappropriately…Our approach is to test the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts in independent satellite data sources…These findings are important to understanding the quality of and strategies for utilizing this information, and for verifying the appropriate remote sensing approaches as index insurance continues to scale.
Forecast-based Financing for Flash Floods: Challenges and Opportunities
In recent years, forecasts have been used to reduce impact from ‘flood’, however, not all floods are created equal. Flash floods are one of the most deadly types of flood on a global scale, with distinct temporal and spatial characteristic relative to other flood types. While Early Warning Early Action systems for slow-onset floods (riverine, for example) have significantly improved over the past 50 years, efforts to create a comparable system for flash floods has lagged behind. Forecast-based Financing (FbF) is a programme that enables access to humanitarian funding for early action based on in-depth forecast information and risk analysis. The goal of FbF is to anticipate disasters, prevent their impact, if possible, and reduce human suffering and losses…While FbF has been applied to various climate extremes such as riverine floods, cyclones and heat waves, it has not yet been done for flash floods. This talk will explore challenges and opportunities for implementing FbF for flash floods.
How Science Influences Action: Responding to Climate Change in Developing Countries
Research program in the IRI at The Earth Institute, Columbia University. During 2016-2017 he was also the Acting Director of the Agriculture and Food Security Center, also at Columbia University. He has established regional programs that aim to improve climate risk assessment and risk management in agriculture, health, water resources, and natural ecosystems. He acted as Distinguished Lead Scholar of the NEXUS Program (Fulbright Foundation) between 2011 and 2013. Before joining the IRI, Baethgen was a Senior Scientist in the Research and Development Division of IFDC (International Soil Fertility and Agricultural Development Center) where he worked mainly in Information and Decision Support Systems for the Agricultural Sector (1987-2003). He acted as a consultant for the World Bank, IADB, United Nations (UNDP, UNIDO, FAO, IAEA), and the Inter-American Institute for Cooperation in Agriculture. He has also has acted as a consultant to governments and the private sector of several countries throughout Latin America…
Predictability of wintertime weather regimes over North America from submonthly reforecasts
Large-scale weather regimes refer to geographically fixed modes of low-frequency variability persisting beyond the lifetime of individual weather disturbances (i.e., beyond about a week). While these large-scale circulation or weather regimes are often used as a reference to express forecasts over the Euro-North Atlantic sector, the low-dimensional weather regime view has been less commonly used over North America. Consistent with earlier studies based on upper-tropospheric circulation patterns, a 4-regime wintertime classification is identified for the Pacific-North America sector by means of a k-means cluster analysis of daily 500hPa geopotential height reanalyses fields. The regimes resemble Rossby wavetrain patterns, except one regime related to a NAO-like meridional pressure gradient, and are all associated with distinct rainfall anomalies over the United States. This study examines the extent to which the observed 4-cluster partition is reproduced by submonthly reforecasts from the subseasonal-to-seasonal (S2S) database in terms of spatial structures, daily regime occurrences and seasonal regime counts. The skill in forecasting observed daily regime sequences and weekly regime counts is investigated from week-1 to -4 leads, alongside relationships with ENSO and the MJO to provide further insights into potential opportunities for skillful winter rainfall predictions based on large-scale weather regimes.
Communication of ENSO-Related Precipitation Anomalies to Resource Constrained Decision-Makers
Seasonal precipitation forecasts are a critical tool in climate services informing decision making in agriculture, public health, and food security. While seasonal forecasting centers have developed sophisticated methods for issuing optimal probabilistic forecasts, many users lack the resources and expertise to interpret and assimilate these forecasts into their decision making…In our study, we present updated ENSO teleconnection maps that have been translated into two simple impact maps (one for El Niño, one for La Niña) so that information can be quickly accessed, understood, and incorporated by lower resource decision-makers.
The method for creating the simplified maps is driven by the question, “What information do the decision-makers need?” We consider the problems the maps will be used to address as well as how sensitive the maps should be to under- and over-prediction of anomalous precipitation. Specifically, how users react to false-alarm and missed-alarm errors and whether they are more threatened by wetter or drier seasons…Assessment of the skill of these maps over 1997-2016 shows they outperform reference forecasts in the tropics. Finally, we compare this skill to the IRI’s dynamical seasonal forecast over the same period to demonstrate the additional value in the dynamical forecasts.
Comparison of the Tercile and Probability Distribution Formats of Seasonal Forecast Information for Climate Services Applications
Historically, seasonal regional Climate Outlook Forums around the world have had a tendency to present their central findings in the format of consensus based maps of tercile probabilities of rainfall and temperature for the coming season. Tools developed at Columbia University’s International Research Institute for Climate and Society (IRI) can enable a more refined understanding of the full forecast probability distribution function. This study will explore, in a heuristic way, the advantages and disadvantages of these two approaches from technical and user perspectives, drawing on several examples at the regional level and from Rwanda and Ethiopia. More complex probabilistic information has been developed by IRI staff in interactive online maproom formats.
Communicating Disaster Risk: From Assessments to Informed Decision-Making
Andrew Kruczkiewicz is a co-presenter for this workshop event.
How do we communicate risks of disaster events and climate change to policy makers and the public? How do we move beyond the production of risk assessments into risk informed decision-making? Building off a World Bank framework on communicating risk information, this workshop is designed to familiarize participants with the concepts of risk communication. This field is multi-disciplinary, drawing on risk modeling, psychology, communication, design-thinking, systems of governance, and earth sciences, among others. Using real world case studies, the workshop will examine ten principles of good risk communication and help scientists understand how to profile your audience to support more effective messaging. The workshop organizers welcome and encourage feedback and criticism on the framework in the hopes of improving it.
Subseasonal to Seasonal Prediction of Weather and Climate
Andrew W Robertson is the primary convener for two presentation sessions on this topic on Wednesday.
There is growing interest in the scientific, operational and applications communities in developing sub-seasonal to seasonal (S2S) forecasts (2 weeks to a season ahead) to provide early warning of high-impact meteorological events such as tropical cyclones, floods, droughts, heat and cold waves. An extensive database of S2S forecasts and hindcasts from operational centers has been created by the WWRP/WCRP S2S project that enables sources of sub-seasonal predictability (e.g. sea-ice, soil moisture, MJO, tropics-midlatitude-poles teleconnections, stratosphere-troposphere interactions etc.) to be studied, forecast skill assessed, and early warning systems developed tailored to user needs. Contributions are solicited on all aspects of S2S prediction, with emphasis on studies making use of the S2S project database, YTMIT (Year of Tropics-Midlatitude Interactions and Teleconnections), and on possible influences of the 2015/16 El Nino on sub-seasonal forecasts.
Calibrated Multi-model Probabilistic Sub-seasonal Forecasts Based on SubX Models
Sub-seasonal to seasonal forecasting (lead times between 2 weeks and 2 months) is a new area of climate prediction that occupies the time range between medium range weather forecasts and seasonal climate prediction. Based on experience from probabilistic seasonal climate and medium-range forecasting, calibration of model probabilities is necessary to account for model deficiencies and produce reliable forecasts. Here we apply extended logistic regression to precipitation hindcasts from “SubX” sub-seasonal forecasting experiment models globally, for lead times from 1 to 4 weeks in advance, and construct a multi-member ensemble. We document the hindcast skill and illustrate some example forecasts in tercile category format.
Climate Information for Public Health Action: An Interdisciplinary Approach
To ensure that health workers have the requisite know-how to use climate knowledge and information they need access to authoritative resources, tailored to their specific needs. “Climate Information for Public Health Action” exists as a semester-long graduate level course at the Mailman School of Public Health, Columbia University…The course and associated book (Thomson & Mason, 2019, “Climate Information for Public Health Action”, Routledge) focus on the incorporation of climate information in routine epidemiological surveillance systems, early warning and risk assessment for public health outcomes of hydro-meteorological disasters, infectious disease emergencies and nutrition crises. The course and book leverage learning from a series of professional training courses undertaken in Africa, Latin America and the USA. The approach is highly collaborative, allowing epidemiologists to work alongside climatologists and meteorologists, to produce new analyses of risks and interventions, while emphasizing the need to understand context and temporal and spatial scales at which different data sources can be usefully applied, particularly in resource poor settings…An important premise is that improved management of health risks associated with climate variability increases adaptive capacity of the public health sector to longer-term climate change.
How much can Model Output Statistics improve sub-seasonal predictive skill over the Intra-Americas Region?
Recent research has highlighted the potential for improving predictive skill at the sub-seasonal timescale, which could be the basis for enhanced, actionable forecasts for climate services involving water and disaster management, health, energy and food security. The WMO’s World Weather and World Climate Research Programmes Subseasonal-to-Seasonal Prediction Project (S2S) and NOAA’s Sub-seasonal Experiment (SubX) Project have made available extensive datasets with both hindcasts and forecasts at this timescale. Presently, sub-seasonal skill is still limited, and in general raw uncalibrated forecasts cannot be used to develop climate services. An obvious alternative is to make use of a variety of robust bias-correction and calibration methods –also known as Model Output Statistics, MOS– available for other timescales, such as the seasonal one. We discuss advantages of applying MOS to sub-seasonal forecasts, analyzing the spatio-temporal variability of skill in several models for the Intra-Americas Region.
Utilizing subseasonal-to-seasonal forecasts in agrarian societies to build resilience and security
Every region or nation has a unique combination of interactions between the variability of its geopolitical, socioeconomic, environmental and other variables, on differing spatial and temporal scales. Understanding the appropriate context for environmental change and “shocks” is vital in order to better clarify these relationships and to aid building of security and resilience, particularly in those places that are most vulnerable. Before we can begin to try and predict things such as migration or even conflict we must first be able to better assess real-time vulnerability due to the aforementioned factors.
Climate variability and change operate over a wide range of temporal scales, from subseasonal to much longer. Decisions are made on a number of time horizons, depending on the sector. Here I will provide some examples for several agrarian countries part of the ACToday project lead by Columbia University’s International Research Institute for Climate and Society (IRI). These countries are actively working toward improving their subseasonal-to-seasonal (S2S) forecasting systems and enlisting IRI researchers to fill the knowledge gaps of the key dynamic processes driving predictive skill, in order to reduce risk to food production and build resilience…
The IRI Climate Data Library as a Platform to Archive, Analyze, and Distribute SubX Project Data
The NOAA MAPP-Climate Test Bed Subseasonal Experiment (SubX) is producing a set of subseasonal multi-model ensemble hindcasts and real-time forecasts from seven models to be provided to the NOAA Climate Prediction Center (CPC) and the wider research community. Goals of the project include the evaluation of the prediction skill of individual models, testing multi-model combinations to optimize forecasts and develop and evaluate prediction products, and to encourage communication between operational forecasts and model forecast producers. The IRI Climate Data Library is a powerful online tool that allows users to access, visualize, download, and perform complex analyses on a wide variety of climate and earth science data, including climate model ensemble output. Additional online “maproom” tools built upon Data Library functions provide simplified or customized interface options to display or analyze data from the Data Library.
Season Characteristics and Mechanisms of Rainfall in the Caribbean
Carlos Javier Martinez + Lisa M Goddard + Yochanan Kushnir + Mingfang Ting
The Caribbean is a complex region of various topographies that heavily relies on its rainfall cycle for its economic and societal needs. Its high reliance makes the Caribbean especially susceptible to hydro-meteorological disasters. In addition, the mechanisms that shape the rainfall cycle is not fully understood…This study conducts a principal component (PC) analysis to investigate the characteristics of the climatological Winter Dry Season (WDS), Early Rainy Season (ERS), Mid-Summer Drought (MSD), and Late-Rainy Season (LRS) across 38 Caribbean stations using the Caribbean Institute of Climatology and Hydrology (CIMH) and National Oceanic and Atmospheric Administration Global Historical Climatology Network (GHCN) 1960-2017 datasets.
Climate Variability and Coffee Productivity in Southern Guatemala
Diego Pons + Mariela Melendez + Florencia Pappa + Rosario Gómez
The Samalá River watershed is…known for having one of the highest incidences of natural disasters in [Guatemala], associated to hydrological extremes. Among the diverse agricultural productivity in the region, coffee stands as one of the most important export crops. This area has experienced several coffee crises in recent years as a consequence of an abrupt change in commodity prices in the early 2000’s, as well as the recent coffee leaf rust outbreak in 2013. Concomitantly, little has been investigated regarding the influence of natural climate variability on the productivity of coffee farms in the region. In this study we explore the relationships between several climatic variables and coffee productivity at different altitudinal gradients in the watershed.
Downscaling Probabilistic Seasonal Rainfall and Temperature Forecasts for Climate Risk Management in Agriculture
Although seasonal temperature forecasts have been routinely produced by climate forecast providers, linking seasonal rainfall forecast in agriculture is still the norm. However, climate predictability varies with location and season, and sometimes temperature is more predictable than rainfall. This better predictability may add value to climate risk management. In this paper, we will provide a framework for downscaling probabilistic seasonal temperature forecasts, compare it with downscaling probabilistic seasonal rainfall forecasts, and more importantly, to simultaneously downscale rainfall and temperature forecasts, that produces rainfall and temperature realizations, that preserve the probabilities of both forecasts…We will present an innovative way of producing a union of the forecasts realizations that preserves both the forecasted probabilities of rainfall and temperature for that season. Case studies in Japan will be presented, and strengths and limitations of the methods will be discussed, particularly on the performance of downscaled forecasts in estimating the risk of drought, and low or high temperatures during the rice planting season.
Applications of Multi-scale Climate Information for Decision Supports in Agriculture
The value of seasonal climate forecast has been proven for advanced climate risk management in agriculture under increasing inter-annual climate variability. In addition, there have been recent advances in issuing operational sub-seasonal to seasonal (S2S) forecasts at some international weather centers. Seasonal and S2S forecasts are provided in various format for different time scales: tercile probabilities for a coming trimester, monthly or seasonal mean, deterministic daily values for the subsequent several weeks. Therefore, it is critical to optimally integrate weather/climate forecasts released at different time scales in different formats, and to produce daily weather sequences for a whole crop growing season in order to utilize process-based crop models for pre-season yield forecasting or other agricultural decision supports. This study will present possible ways to integrate multi-scale weather/climate information and process-based crop simulation models to better adapt to climate variability and thus improve agricultural production. Case studies will be presented: one in the context of developing dynamic cropping calendar in the Philippines and another in the context of utilizing operational weekly S2S forecast in Bangladesh.
Quantifying the Impacts of Early and Late Growing Season Precipitation on Midwestern Corn Production: A Downscaling and Modeling Approach
Amor V M Ines + William J Baule + Prakash Kumar Jha
Water availability and accessibility is one of the primary drivers of crop growth and production. While corn (Zea maize L.) is particularly sensitive to lengthy dry spells, especially during the reproductive stages, on-farm decision making and flexible management strategies can help reduce risk and mitigate some of the negative effects during dry times. Using 2012 as a recent example of significant drought in the Midwestern United States, in this study, we simulate corn production at seven locations across the Corn Belt under multiple management strategies…Past research and preliminary results indicate that FResampler, in combination with crop models, displays substantial utility in evaluating the effects of variable management on corn production considering weather uncertainty and variability.
Toward a Better Understanding of the Impacts of Climate Variability on Agricultural Decision-Making and Longer-Term Adaptation
Amor V M Ines is the primary convener for this poster session. Eunjin Han is a convener and chair of this session.
Climate variability (CV) is a critical driver of year-to-year impacts on agricultural systems. To properly understand its ecological impacts, it is necessary to quantify how managed ecosystems have historically responded to CV, and to characterize the uncertainty in projected biophysical impacts. In agricultural systems, assessing impacts of CV also requires understanding dynamics of farmer decision-making and adaptation in the face of both biophysical and economic uncertainty. Here we seek new efforts to quantify both historical and future impacts of CV on different types of agricultural systems varying by crop, farm size/intensification and climate/soils, including: i) empirical/process-based methods for estimating CV impacts across multiple spatiotemporal scales; ii) methods for forecasting the impact of CV on agricultural response at relevant scales; iii) translating forecasts into useful decision support for farmers and policy-makers; iv) attributing farmer responses to CV with respect to socioeconomic and ecological circumstances.
Tropical cyclone climatology and hazard estimations in a warming climate
Tropical cyclone (TC) hazard is associated with TC climatology, namely, the pattern of genesis, distributions of intensity and intensification rate, tendency of storm motion, and the frequency and intensity at landfall. These properties are sensitive to a warming climate, with an increasing storm intensity being the most certain change. Changes in other properties, such as genesis and landfall frequency are highly uncertain, especially on a regional scale. To estimate TC hazards in a warming climate is therefore challenging. In this presentation, we will assess the TC hazards in a warming climate using the Columbia HAZard model (CHAZ). The CHAZ is a statistical-dynamical downscaling model which estimates TC hazard by generating synthetic storms using environmental conditions from a global model. CHAZ contains three components representing the complete storm lifetime: tropical cyclone genesis index (TCGI) model, a beta-advection track model and an auto-regressive intensity model.
Climate-forced crop yield variability and synchronous crop failures
Modes of climate variability, particularly the El Niño Southern Oscillation (ENSO), are often presented as a risk to global food security. But what fraction of crop yield variability do they actually account for? Here, for the first time, we estimate the relative contribution of major modes of climate variability to crop yield variability at the global scale. We consider the influence of not only ENSO, but also the Indian Ocean Dipole (IOD), tropical Atlantic variability (TAV) and the North Atlantic Oscillation (NAO). We find that modes of climate variability account for ~18%, 7% and 5% of globally aggregated maize, soy and wheat production variability, respectively. All modes of variability are important in at least one region studied, but only ENSO has a significant influence on global production. …These results demonstrate how the distribution of global cropland in relation to ENSO teleconnections contributes to the presence for maize or absence for wheat and soy of synchronous global crop failures.