IRI@AGU: Schedule of Events 2019
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.
SUNDAY, DECEMBER 8
World Climate Research Programme 40th Anniversary Symposium
WCRP is celebrating its 40th year of international climate science. We invite you to join us for a Symposium, which will showcase the many successes of our community over the last four decades and highlight some of the challenges and opportunities that climate science faces now and will face in the future. WCRP is entering a new phase of development to ensure that it is prepared for this future, recently launching its Strategic Plan 2019-2028. This Symposium will launch a series of sessions and events that will bring this future focus together in a “Climate Science Week”, a joint effort between WCRP and AGU’s 100th Anniversary Symposium.
MONDAY, DECEMBER 9
Can We Predict “Climate Migrations”? The 2018 Guatemalan Case
Migrations are caused by multiple and entangled factors, making them in general virtually impossible to predict. Nonetheless, we expect that when the socio-economic vulnerability of the population surpasses a certain threshold but are not so high that households have expended all their assets, external drivers like intense or prolonged climate hazards can actually trigger “climate migrations”. Once started, cumulative causation takes place with more and more people following the first wave of emigrants. Although the particular conditions to start a climate migration process are less common than the media suggests and are very context-specific, these events are potentially predictable in places where a good monitoring of the population vulnerability exists and where there is predictive skill for the concrete climate hazard triggering the migration as a demographic response. Here, we explore these ideas using the 2018 Guatemalan mass migration as an example of climate-induced population mobility.
We first contextualize the socioeconomic vulnerability of the exposed Guatemalan population, especially those living in the so called “Dry Corridor”, finding that the confounding role of an increasing infant mortality rate since 2012, a high food inflation rate (the fourth highest since 1996), and an increase in the unemployment rate set the stage for the climate migration that followed. In addition, the multi-year drought that was present during the previous three years acted as the final stressor by heavily increasing household debts via reduced staple crop harvests and limited access to unskilled employment in the agricultural sector. With this and other contextual information on past migrations, we then assess the predictive skill of this type of climate migrations for Guatemala, using an ensemble of realizations built with a set of different migration models.
From publishable to operational: new metrics to more honestly measure the ability of remote sensing algorithms to consistently monitor flooded assets and populations in near real time
Advances in computing and earth observing satellites allow for daily monitoring of floods. Published methods to make flood maps from satellites report high accuracy (often near 90%) when tested on sets of large flood events. However, when replicating these methods to many images over time in monitoring systems (e.g. to find flood events over a whole rainy season), we find accuracies far below 90%, often finding many false alarms and failure to map inundated communities and assets. We propose a new metric to measure how well remote sensing algorithms can consistently identify when objects people care about (crops, infrastructure, their homes, etc) are flooded. We apply this metric to measure the ability of 4 combined satellites to consistently map floods in Sri Lanka and the Eastern Nile. This metric, based on publicly available data, could be used by scientists and flood managers as a more honest assessment of flood monitoring systems, identify their limits, and build towards a better system to enable flood protection.
Seasonal Climatology and Dynamical Mechanisms of Rainfall in the Caribbean
The Caribbean is a complex region that heavily relies on its rainfall cycle for its economic and societal needs. This makes the Caribbean especially susceptible to hydro-meteorological disasters (i.e. droughts and floods). Previous studies have investigated the seasonal cycle of rainfall in the Caribbean with monthly or longer resolutions that often mask the seasonal transitions and regional differences of rainfall. This has resulted in inconsistent findings on the seasonal cycle. In addition, the mechanisms that shape the climatological rainfall cycle in the region are not fully understood. To address these problems, this study conducts: (i) a principal component analysis of the annual cycle of precipitation across 38 Caribbean stations using daily observed precipitation data; and, (ii) a moisture budget analysis for the Caribbean, using the ERA-Interim Reanalysis. This study finds that the seasonal cycle of rainfall in the Caribbean hinges on three main facilitators of moisture convergence: the Eastern Pacific ITCZ, the Atlantic ITCZ, and the western flank of the North Atlantic Subtropical High (NASH). The Atlantic Warm Pool and Caribbean Low-Level Jet modify the extent of moisture provided by these main facilitators. The expansion and contraction of the western flank of NASH generate the bimodal pattern of the precipitation annual cycle in the northwestern Caribbean, central Caribbean, and with the Eastern Pacific ITCZ the western Caribbean. This study identifies the Atlantic ITCZ as the major source of precipitation for the central and southern Lesser Antilles, which is responsible for their unimodal rainfall pattern. Convergence by sub-monthly transients contributes little to Caribbean rainfall.
TUESDAY, DECEMBER 10
Experimental Real-time Sub-seasonal to Seasonal (S2S) Forecast for Indian Summer Monsoon 2018 over Bihar: A Forecast Application for Risk Management in Agriculture
Nachiketa Acharya. Andrew Robertson and Ángel Muñoz are conveners and chairs for this poster session.
Experimental calibrated probabilistic sub-seasonal to seasonal (S2S) forecast for precipitation were developed for the state of Bihar, one of the most climate-sensitive states in India, and issued in real time during the June–September 2018 monsoon period under the International Research Applications Project (IRAP) project, funded by NOAA. The forecast maps were displayed through a virtual maproom and discussed each week with India Meteorological Department (IMD), and a text summary sent to Bihar’s State Agricultural Universities, who then forwarded them via a non-governmental organization for dissemination to the farmers to assist climate risk management for decision-making.
Abstract page.
List of all the posters in this session.
NextGen: A Next-Generation System for Calibrating, Ensembling and Verifying Regional Seasonal and Subseasonal Forecasts
Ángel G. Muñoz. Andrew Robertson and Muñoz are conveners and chairs for this poster session.
Successful climate services often involve the use of tailored regional climate forecasts at one or multiple timescales. The way those forecasts are implemented is not always straightforward, and depends on several different factors, like which variables, models and calibration methods to use, how to produce the ensemble and tailoring, or even how to present them to the decision makers. Here, NextGen, a systematic general objective approach for designing, calibrating, building ensembles, and verifying objective climate forecasts is presented and discussed. NextGen involves the identification of decision-relevant variables by the stakeholders, and the analysis of the physical mechanisms, sources of predictability and suitable candidate predictors (in models and observations) for those key relevant variables. In those cases when prediction skill is deemed high enough, NextGen helps select the best dynamical models for the region of interest through a process-based evaluation, and automates the generation and verification of tailored multi-model, statistically calibrated predictions at seasonal and sub-seasonal timescales, at regional, national or sub-national level.
Abstract page.
List of all the posters in this session.
An Online Maproom For Real-Time Subseasonal Probabilistic Forecasts
Andrew Robertson. Robertson and Ángel Muñoz are conveners and chairs for this poster session.
Translation of ensemble subseasonal forecast model output into user-relevant forecast products is an important part of the process of developing climate services that fill the gap between weather forecasts and seasonal climate outlooks. An online maproom for real-time subseasonal probabilistic forecasts of precipitation and temperature will be presented, constructed from a multimodal ensemble of three ensemble prediction systems (CFSv2, GEFS and ESRL FIM) from the Subseasonal Experiment (SubX), calibrated using extended logistic regression. The results shown will include probabilistic skill scores and example real time forecasts at lead times of 1-4 weeks ahead.
Abstract page.
List of all the posters in this session.
World Climate Research Programme: Improved Prediction of Climate Systems on Timescales of Weeks to Decades
The World Climate Research Programme (WCRP) invites you to discuss the latest progress and new challenges in climate prediction on time scales of weeks to several decades. A specific focus will be on evolving risks of extremes within a changing climate. Climate variability will continue to challenge our preparedness and resilience to high impact weather and climate extremes, and skillful and reliable climate predictions offer significant opportunities to manage these risks. The development of next-generation operational systems to predict regional impacts at ever greater lead times will require fundamental research into sources of predictability including important scale interactions and nonlinearities along with their representation in models, and innovations in model-data fusion including coupled data assimilation. Merging predictions with longer-term projections is an important challenge toward seamless climate information.
In this Town Hall, we will discuss avenues for advancing climate prediction science and services. These include (i) determining limits of predictability and the relative roles of initial conditions and forcing, (ii) assessing the capacities of operational prediction systems to approach those limits, (iii) quantifying uncertainties, and (iv) effectively formulating and communicating forecast information. We will discuss the ability of prediction systems to represent key processes, and to predict risks of extreme events including unprecedented extremes and crossing of thresholds in vulnerable regions. Challenges spanning prediction across different Earth system components will be explored, as will implications of a non-stationary climate for the occurrence of “fast” extremes such as hurricanes, and “slow” extremes such as droughts.
WEDNESDAY, DECEMBER 11
Toward Better Understanding of the Impacts of Climate Variability on Agricultural Decision-Making and Longer-Term Adaptation I
Eunjin Han is a convener for 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 spatio-temporal scales, including emerging Earth-Observation-technologies (e.g., ECOSTRESS) and distributed-sensor-networks; ii) methods for forecasting impacts of CV on agricultural response at relevant scales, including S2S; iii) translating forecasts into useful decision-support for farmers and policymakers; and iv) attributing farmer responses to CV with respect to socioeconomic-ecological circumstances.
List of all presentations in this session.
Advances in the lead time of Sahel rainfall prediction with the North American Multi-Model Ensemble
Alessandra Giannini. Ángel Muñoz is a co-convener of this poster session.
Rainfall in the Sahel, the poleward margin of the West African monsoon, is characterized by high variability, evident in the multi-decadal swings between the anomalously wet 1950s and 1960s and the anomalously dry 1970s and 1980s, and in the year-to-year swings since the mid-1990s.
Variability is shaped by the interplay of independent, North Atlantic and global tropical, sources of predictability, encapsulated in the North Atlantic Relative Index. The potential for competition of warming oceans under the influence of greenhouse gases is one way to interpret the increased interannual variability since the mid-1990s—behavior which makes seasonal prediction all the more valuable.
In this context we assess the deterministic skill in the North American Multi-Model Ensemble (NMME), a dynamical, openly accessible, operational multi-model seasonal prediction system. We find that skill for a regionally averaged rainfall index is essentially the same for forecasts for the July-September target season made as early as February/March and as late as June. The system owes its skill to the correct characterization of oceanic influence on Sahel rainfall, which is achieved by combining output from two models particularly skillful at predicting North Atlantic and tropical Pacific sea surface temperature anomalies respectively, namely NASA-GEOSS2S and CMC2-CanCM4.
Abstract page.
List of all the presentations in this session.
MJO teleconnections to agro-climate extremes and crop yields
Weston Anderson. Andrew Robertson is the primary convener of this session, and Ángel Muñoz is a co-convener.
Understanding what causes abiotic stresses that lead to crop failures is a critical step towards stabilizing crop production locally and globally. While there are many sources of abiotic stresses, the Madden-Julian Oscillation (MJO) is the dominant source of sub-seasonal climate variability in the tropics, making it a potential – but as of yet unexplored – source of crop failures. We use daily precipitation, maximum temperature, and soil moisture data, as well as crop models and observational yield statistics to assess whether the MJO affects crop yields.
Abstract page.
List of all the presentations in this session.
Interannual variability of the Early and Late-Rainy Seasons in the Caribbean
An important understanding of the Caribbean rainfall cycle is its interannual variability. To address this issue, this study conducts: (i) a temporal composite of the annual cycle of precipitation during anomalously wet and dry years across 34 Caribbean stations using daily observed precipitation data; and, (ii) a spatial composite of SST, SLP, and mean flow convergence in the Caribbean using the NCEP/NCAR reanalysis, NOAA v5 Extended Reconstructed SST, and ERA-Interim Reanalysis datasets. This study finds that the Early-Rainy Season (ERS) and Late-Rainy Season (LRS) are predominantly independent of each other, highlighting that most wet or dry years in the rainfall cycle are a result of anomalous precipitation from only one component of the rainfall season. Dry (wet) ERS years are due to a preceding positive (negative) NAO induced cold (warm) SST persistence signal across the Caribbean that produces a wind-evaporation-SST (WES) feedback of enhanced easterlies (westerlies) and moisture divergence (convergence). Dry (wet) LRS years are due to the summertime onset of a positive (negative) ENSO phase which produces a WES feedback of anomalous cool (warm) SSTs, high (low) pressure, easterlies (westerlies), and moisture divergence (convergence) in the Caribbean. The seasonal evolution of the NAO-SST persistence signal and ENSO affect the climatological dynamical mechanisms that shape the Caribbean’s regional rainfall cycle. They also explain why the NW Caribbean has weaker rainfall variability signal than the rest of the Caribbean. Finally, mature ENSO phases in the preceding seasons weakly modify the Caribbean ERS, while a persistent NAO-SST signal can be a secondary influence on the duration of anomalous precipitation in the Caribbean LRS.
THURSDAY, DECEMBER 12
Toward Better Understanding of the Impacts of Climate Variability on Agricultural Decision-Making and Longer-Term Adaptation II
Eunjin Han is a convener and chair for 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 spatio-temporal scales, including emerging Earth-Observation-technologies (e.g., ECOSTRESS) and distributed-sensor-networks; ii) methods for forecasting impacts of CV on agricultural response at relevant scales, including S2S; iii) translating forecasts into useful decision-support for farmers and policymakers; and iv) attributing farmer responses to CV with respect to socioeconomic-ecological circumstances.
List all posters for this session.
Development of a decision support model for the management of fungal ear rot and associated mycotoxin contamination in corn grain
Eunjin Han. Han is also a convener for this poster session.
Corn ear rot disease and associated mycotoxins, such as deoxynivalenol (DON), are annual issues for many Michigan corn producers. The combination of hybrid susceptibility and ideal weather conditions for fungal infection during silking can result in corn ear rot and associated DON contamination. Moreover, feeding by western bean cutworm (WBC) has been observed in hybrids, even with Bt trait for insect control (Cry 1F). Insect damage to the ear provides another pathway for fungal infection and mycotoxin contamination. In 2017 growing season, because of a different weather pattern during silking, there was an apparent decrease in ear rot occurrence in the state, compared with 2016 and 2018. This highlights the impact of weather on this problem. Fungicide application is generally practiced by corn growers and has shown to decrease DON levels and increase corn yields. However, fungicide use is expensive, and timing of application can impact the efficacy of ear rot control. Hence, producers must need accurate climate/weather forecast information to know the potential risk of disease occurrence to improve their chance of better managing this annual menace. Also, it is necessary to predict silking of common corn hybrids to better design agronomic practices that will minimize the occurrence of ear rot and associated mycotoxin contamination. Corn hybrid age groups and geographic diversity in corn-growing regions provide opportunities to design different planting windows in the state that maximize resource utilization and minimize fungal infection at silking. In this paper, we present the development of a decision support model for the prediction of risks and management of fungal ear rot and associated mycotoxins of corn grain in Michigan.
Improving seasonal precipitation forecast for agriculture in the Orinoquia Region of Colombia
Canonical Correlation Analysis (CCA) is used to improve the skill of seasonal forecast in the Orinoquía Region, where over 40% of Colombian rice is produced. Seasonal precipitation and frequency of wet-days are predicted, as rice yields simulated by a calibrated crop-model is better correlated with wet-days frequency than with precipitation amounts in June-August (JJA). Prediction of frequency of wet-days, using as predictors variables from the NCEP Climate Forecast System version 2 (CFSv2), result in forecast with higher skill than models predicting seasonal precipitation amounts. Using wet-days frequency as an alternative climate variable reveals that the distribution of daily rainfall is both more relevant for rice yield variability and more skillfully predicted than seasonal precipitation amounts. Forecast skill can also be improved by using the Climate Hazards Infrared Precipitation with Stations (CHIRPS) merged satellite-station JJA precipitation as predictand in a CCA model, especially if the predictor is CFSv2 vertically integrated meridional moisture flux (VQ). The probabilistic hindcast derived from the CCA model using CHIRPS as predictand, can successfully discriminate above-normal, normal and below-normal terciles of over 80% of the stations in the region. This is particularly relevant for stations that, due to discontinuity in their time series, are not included in station-only CCA models but are still in need of probabilistic seasonal forecast. Finally, CFSv2 VQ performs better than precipitation as predictor in CCA, which we attribute to CFSv2 being more internally consistent in regards to sea surface temperature (SST)-forced VQ variability than to SST-forced precipitation variability in the Orinoquía.
Improvements in the GISTEMP Uncertainty Model
A new and improved uncertainty analysis is presented for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).
World Climate Research Programme: Bridging Science and Society: Decision-Relevant Information About the Evolving Climate System
Ángel G. Muñoz is a convener for this Town Hall.
The World Climate Research Programme (WCRP), together with partners, invites you to review progress and discuss emerging challenges in climate-society interactions and in generating decision-relevant climate information and knowledge in support of policy and services. Climate science is generating a wealth of data from observations all over the globe and model output that requires distillation into information, knowledge and practical advice. The transfer of uncertainties along the generation process, including socio-economic elements, is very complex but a necessary condition to make informed decisions and manage risk about our Earth system. In this Town Hall, we will discuss some innovative approaches providing avenues to sort through this vast amount of information, reconcile and explain outcomes, and extract useful knowledge. We will explore pathways to produce climate services, accurate scientific assessments and public communication strategies, all of which require collaborative efforts with civil society, governments and private industry. We will also discuss how the scientific community can take a more active role in the climate transition, for instance by developing tools and know-how for virtual conferences.
The session will focus on existing examples of good practice within advice for policy, use of updated climate data in education, and general outreach from ongoing research projects to citizens using social media.
FRIDAY, DECEMBER 13
Building Resilience to Extreme Weather and Climate Impacts in Developing Countries
Society will never be climate proofed. Resilience must be about preparedness as well as protective interventions. Preparedness for climate and weather shocks can save money, property, and lives, compared to traditional humanitarian work that begins only once a crisis is identified. While this seems a reasonable statement, its validity depends on the quality of the information that supports the early warning-early action systems. Quality of the early warning/action requires accurate, real time observational data, probabilistically reliable forecasts across a range of timescales, and financial tools that the community can understand and trust – again supported by good observations.
In the presentation I will overview a couple of examples of work with developing countries within a large project called, “Adapting Agriculture to Climate Today, for Tomorrow”. In the project countries, we establish the observational, forecast, and financial instruments methodologies and platforms that can help build resilience to extremes. These elements then serve as the foundation to develop decision support structures in the context of small holder livelihoods and national food security.
Utility of Seasonal Climate Forecasts for Agricultural Decision Supports in Ethiopia
Seasonal climate forecasts (SCF) are regularly provided by various national and international organizations in a tercile probability format for the coming season with different lead times. Although SCFs have been operationally released to the public for recent years, its utility in agricultural sector has not been well evaluated. A new project led by the International Research Institute for Climate and Society (IRI) entitled “Adapting Agriculture to Climate Today, for Tomorrow (ACToday)” aims to improve agricultural productivity and food security, and build resilience and adaptive capacity in six developing countries through integrated climate service and knowledge. Taking advantage of the ACToday project we explored the potential of the available SCFs to improve decision making by highly food insecure smallholder farmers engaged in rain-fed agricultural production system in selected regions of Ethiopia. The Decision Support System for Agrotechnology Transfer (DSSAT) was used for assessing the value of current operational SCFs on crop yield prediction given representative management practices, in comparison to baseline simulations (i.e., crop modeling using historical weather observations). The tercile probabilities of SCFs were stochastically disaggregated into daily weather realizations and used as input for the crop simulation models. We used currently available SCFs from several national and international agencies including the National Meteorology Agency of Ethiopia (NMA), IGAD Climate Prediction and Application Centre (ICPAC), IRI, and the European Centre for Medium-Range Weather Forecasts (ECMWF). Seasonal climate forecasts from these sources were used with the crop simulation models and their utilities were evaluated in terms of crop yield forecasting and agricultural decision support. In this presentation we will discuss some limitations of the current SCFs and the potential for improvement in the context of improving agricultural decision support tools to reduce climate-related risk, enhance adaptation to climate variability and change, and boost smallholder farmers’ food security and livelihoods.
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