Grand Multi-Model Seasonal Forecasts in the SECLI-FIRM project

2020 ◽  
Author(s):  
Andrea Alessandri ◽  
Franco Catalano ◽  
Matteo De Felice ◽  
Kristian Nielsen ◽  
Alberto Troccoli ◽  
...  
Keyword(s):  
Seasonal Prediction ◽  
Added Value ◽  
Japan Meteorological Agency ◽  
Brier Score ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Prediction Systems ◽  
Seasonal Climate Forecasts ◽  
Selection Of

<p>A key objective of the Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, www.secli-firm.eu) project is the optimisation of the performance of seasonal climate forecasts provided by many producing centers, in a Grand Multi-Model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM.</p><p>The Grand Multi-Model Ensemble (MME) consists of the five Seasonal Prediction Systems (SPSs) provided by the European Copernicus C3S and a selection of other five SPSs independently developed by centres outside Europe, four by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).</p><p>All the possible multi-model combinations have been evaluated showing that, in general, only a limited number of SPSs is required to obtain the maximum attainable performance. Although the selection of models that perform better is usually different depending on the region/phenomenon under consideration, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs, i.e. by mixing European SPSs with those from NMME-JMA.</p><p>Starting from the definition of the Brier score a novel metric has been developed, named the Brier score covariance (BScov), which estimates the relative independence of the prediction systems. BScov is used to quantify independence among the SPSs and, together with probabilistic skill metrics, used to develop a strategy for the identification of the combinations that optimize the probabilistic performance of seasonal predictions for the study cases.</p>

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2018 ◽  
Vol 22 (4) ◽  
pp. 2057-2072 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Added Value ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow ◽  
Seasonal Climate Forecasts

Abstract. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

A novel model independence methodology to improve multi-model seasonal forecasts combination

2020 ◽  
Author(s):  
Franco Catalano ◽  
Andrea Alessandri ◽  
Kristian Nielsen ◽  
Irene Cionni ◽  
Matteo De Felice
Keyword(s):  
Seasonal Prediction ◽  
Boreal Summer ◽  
Brier Score ◽  
Boreal Winter ◽  
Seasonal Forecasts ◽  
Surface Solar Radiation ◽  
Model Ensembles ◽  
Prediction Systems ◽  
June July ◽  
Forecasts Combination

<p align="justify">Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single model ensembles. The potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. To this aim, a novel methodology has been developed to assess the relative independence of the prediction systems in the probabilistic information they provide.</p><p align="justify"><span>We considered the Copernicus C3S seasonal forecasts product considering the one-month lead retrospective seasonal predictions for boreal summer and boreal winter seasons (1</span><sup><span>st</span></sup><span> May and 1</span><sup><span>st</span></sup><span> November start dates, i.e. June-July-August, JJA and December-January-February, DJF). We analysed the seasonal hindcasts in terms of deterministic and probabilistic scores with a particular focus on </span><span>continental areas</span><span>, since little evaluation has been performed so far over land domains that is where most of the applications of seasonal forecasts are based. The most relevant target variables of interest for the energy users have been considered and skill differences between the prediction systems have been analysed together with related possible sources of predictability. The analysis evidenced the importance of snow-albedo processes for temperature predictions in DJF and the effect of the atmospheric dynamics through moisture convergence for the prediction of surface solar radiation in JJA. </span><span>A</span><span> new metric, the Brier Score Covariance, designed to quantify the probabilistic independence among the models, has been </span><span>developed and </span><span>applied to optimize model selection and combination strategies with a particular focus on the most relevant variables for energy applications.</span></p>

scholarly journals On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs

2020 ◽  
Vol 148 (1) ◽  
pp. 437-456 ◽  
Author(s):  
Andrew Schepen ◽  
Yvette Everingham ◽  
Quan J. Wang
Keyword(s):  
Multivariate Calibration ◽  
Joint Probability ◽  
Superior Performance ◽  
Maximum Temperature ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Calibration Methods ◽  
Multivariate Dependence ◽  
Seasonal Climate Forecasts

Abstract Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach; 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle); and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. Direct multivariate calibration is the second-best method, with its far superior performance in in-sample testing vanishing in cross validation, likely because of insufficient data relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology.

Proactive Drought and Extreme Event Preparedness: Seasonal Climate Forecasts offer Benefit for Decision Making in Water Management in Semi-arid Regions

2020 ◽  
Author(s):  
Tanja Portele ◽  
Christof Lorenz ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Water Management ◽  
Arid Regions ◽  
Extreme Event ◽  
Seasonal Forecast ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Early Action ◽  
Climate Forecasts ◽  
Semi Arid ◽  
Seasonal Climate Forecasts

<p>Semi-arid regions are the regions mostly affected by drought. In these climatically sensitive regions, the frequency and intensity of drought and hot extremes is projected to increase. With increasing precipitation variability in semi-arid regions, sustainable water management is required. Proactive drought and extreme event preparedness, as well as damage mitigation could be provided by the use of seasonal climate forecasts. However, their probabilistic nature, the lack of clear action derivations and institutional conservatism impedes their application in decision making of the water management sector. Using the latest global seasonal climate forecast product (SEAS5) at 35 km resolution and 7 months forecast horizon of the European Centre for Medium-Range Weather Forecasts, we show that seasonal-forecast-based actions offer potential economic benefit and allow for climate proofing in semi-arid regions in the case of drought and extreme events. Our analysis includes 7 semi-arid, in parts highly managed river basins with extents from tens of thousands to millions of square kilometers in Africa, Asia and South America. The value of the forecast-based action is derived from the skill measures of hit (worthy action) and false alarm (action in vain) rate and is related to economic expenses through ratios of associated costs and losses of an early action. For water management policies, forecast probability triggers for early action plans can be offered based on expense minimization and event maximization criteria. Our results show that even high lead times and long accumulation periods attain value for a range of users and cost-loss situations. For example, in the case of extreme wet conditions (monthly precipitation above 90<sup>th</sup> percentile), seasonal-forecast-based action in 5 out of 7 regions can still achieve more than 50 % of saved expenses of a perfect forecast at 6 months in advance. The utility of seasonal forecasts strongly depends on the user, the cost-loss situation, the region and the concrete application. In general, seasonal forecasts allow decision makers to save expenses, and to adapt to and mitigate damages of extreme events related to climate change.</p>

scholarly journals Opportunities for seasonal forecasting to support water management outside the tropics

2021 ◽  
Author(s):  
Leah Amber Jackson-Blake ◽  
François Clayer ◽  
Elvira de Eyto ◽  
Andrew French ◽  
María Dolores Frías ◽  
...  
Keyword(s):  
Water Management ◽  
Decision Making ◽  
Climate Model ◽  
Added Value ◽  
Seasonal Forecasting ◽  
Qualitative Assessment ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Study Sites ◽  
Seasonal Climate Forecasts

Abstract. Advance warning of seasonal conditions has potential to assist water management in planning and risk mitigation, with large potential social, economic and ecological benefits. In this study, we explore the value of seasonal forecasting for decision making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, level, chemistry and ecology) and fish migration timing, and were co-developed together with stakeholders. To explore the decision making value of forecasts, we carried out a qualitative assessment of: (1) how useful forecasts would have been for a problematic past season, and (2) the relevance of any “windows of opportunity” (seasons and variables where forecasts are thought to perform well) for management. Overall, stakeholders were optimistic about the potential for improved decision making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical hands-on experience were identified as key requirements before forecasts would be used in operational decision making. Seasonal climate forecasts provided little added value to freshwater forecasts in the study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows.

scholarly journals Challenges and Opportunities Provided by Seasonal Climate Forecasts: A Literature Review

2002 ◽  
Vol 34 (3) ◽  
pp. 603-632 ◽  
Author(s):  
Harvey S.J. Hill ◽  
James W. Mjelde
Keyword(s):  
Social Sciences ◽  
Literature Review ◽  
Decision Makers ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Challenges And Opportunities ◽  
Close Cooperation ◽  
Successful Collaboration ◽  
Seasonal Climate Forecasts

Use of seasonal climate forecasts is a rapidly evolving area. Effective research and application of climate forecasts require close cooperation between scientists in diverse disciplines and decision makers. Successful collaboration requires all players to at least partially understand each other's perspectives. Issues associated with seasonal forecasts, through a selected review of both physical and social sciences literature, is presented. Our hope is that the review will improve research in this area by stimulating further collaborations.

scholarly journals Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research

2008 ◽  
Vol 47 (5) ◽  
pp. 1269-1286 ◽  
Author(s):  
Francisco J. Meza ◽  
James W. Hansen ◽  
Daniel Osgood
Keyword(s):  
Economic Value ◽  
Basic Research ◽  
Farming Systems ◽  
Future Research ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Published Evidence ◽  
Climate Forecasts ◽  
Ex Ante ◽  
Seasonal Climate Forecasts

Abstract Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits. Interdisciplinary initiatives seeking to understand and exploit the potential benefits of seasonal forecasts for agriculture have produced a number of quantitative ex-ante assessments of the economic value of seasonal climate forecasts. The realism, robustness, and credibility of such assessments become increasingly important as efforts shift from basic research toward applied research and implementation. This paper surveys published evidence about the economic value of seasonal climate forecasts for agriculture, characterizing the agricultural systems, approaches followed, and scales of analysis. The climate forecast valuation literature has contributed insights into the influence of forecast characteristics, risk attitudes, insurance, policy, and the scale of adoption on the value of forecasts. Key innovations in the more recent literature include explicit treatment of the uncertainty of forecast value estimates, incorporation of elicited management responses into bioeconomic modeling, and treatment of environmental impacts, in addition to financial outcomes of forecast response. It is argued that the picture of the value of seasonal forecasts for agriculture is still incomplete and often biased, in part because of significant gaps in published valuation research. Key gaps include sampling of a narrow range of farming systems and locations, incorporation of an overly restricted set of potential management responses, failure to consider forecast responses that could lead to “regime shifts,” and failure to incorporate state-of-the-art developments in seasonal forecasting. This paper concludes with six recommendations to enhance the realism, robustness, and credibility of ex-ante valuation of seasonal climate forecasts.

scholarly journals On the reliability of seasonal climate forecasts

2014 ◽  
Vol 11 (96) ◽  
pp. 20131162 ◽  
Author(s):  
A. Weisheimer ◽  
T. N. Palmer
Keyword(s):  
Numerical Models ◽  
Forecast Error ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Weather Forecasts ◽  
Temperature And Precipitation ◽  
Climate Forecasts ◽  
Wide Range ◽  
Regional Temperature ◽  
Seasonal Climate Forecasts

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1–5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that ‘goodness’ should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a ‘5’ should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of ‘goodness’ rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching ‘5’ across all regions and variables in 30 years time.

scholarly journals Summative evaluation of climate application activities with pastoralists in western Queensland

2008 ◽  
Vol 30 (3) ◽  
pp. 361 ◽  
Author(s):  
D. H. Cobon ◽  
K. L. Bell ◽  
J. N. Park ◽  
D. U. Keogh
Keyword(s):  
Direct Contact ◽  
Climate Information ◽  
Seasonal Forecasts ◽  
Self Assessment ◽  
Test Question ◽  
Broadband Internet ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Climate Knowledge ◽  
Seasonal Climate Forecasts

Survey methods were engaged to measure the change in use and knowledge of climate information by pastoralists in western Queensland. The initial mail survey was undertaken in 2000–01 (n = 43) and provided a useful benchmark of pastoralists climate knowledge. Two years of climate applications activities were completed and clients were re-surveyed in 2003 (n = 49) to measure the change in knowledge and assess the effectiveness of the climate applications activities. Two methods were used to assess changes in client knowledge, viz., self-assessment and test questions. We found that the use of seasonal climate forecasts in decision making increased from 36% in 2001 (n = 42) to 51% in 2003 (n = 49) (P = 0.07). The self-assessment technique was unsatisfactory as a measure of changing knowledge over short periods (1–3 years), but the test question technique was successful and indicated an improvement in climate knowledge among respondents. The increased levels of use of seasonal climate forecasts in management and improved knowledge was partly attributed to the climate applications activities of the project. Further, those who used seasonal forecasting (n = 25) didn’t understand key components of forecasts (e.g. probability, median) better than those who didn’t use seasonal forecasts (n = 24) (P > 0.05). This identifies the potential for misunderstanding and misinterpretation of forecasts among users and highlights the need for providers of forecasts to understand the difficulties and prepare simply written descriptions of forecasts and disseminate these with the maps showing probabilities. The most preferred means of accessing climate information were internet, email, ‘The Season Ahead’ newsletter and newspaper. The least preferred were direct contact with extension officers and attending field days and group meetings. Eighty-six percent of respondents used the internet and 67% used ADSL broadband internet (April 2003). Despite these findings, extension officers play a key role in preparing and publishing the information on the web, in emails and newsletters. We also believe that direct contact with extension officers trained in climate applications is desirable in workshop-like events to improve knowledge of the difficult concepts underpinning climate forecasts, which may then stimulate further adoption.

scholarly journals Assessing the Skill and Reliability of Seasonal Climate Forecasts in Sahelian West Africa

2020 ◽  
Vol 35 (3) ◽  
pp. 1035-1050 ◽  
Author(s):  
Jennifer S. R. Pirret ◽  
Joseph D. Daron ◽  
Philip E. Bett ◽  
Nicolas Fournier ◽  
Andre Kamga Foamouhoue
Keyword(s):  
West Africa ◽  
Regional Climate ◽  
Model Performance ◽  
Climatic Conditions ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Planning Decisions ◽  
Climate Forecasts ◽  
Forecast Models ◽  
Seasonal Climate Forecasts

Abstract Seasonal climate forecasts have the potential to support planning decisions and provide advanced warning to government, industry, and communities to help reduce the impacts of adverse climatic conditions. Assessing the reliability of seasonal forecasts, generated using different models and methods, is essential to ensure their appropriate interpretation and use. Here we assess the reliability of forecasts for seasonal total precipitation in Sahelian West Africa, a region of high year-to-year climate variability. Through digitizing forecasts issued from the regional climate outlook forum in West Africa known as Prévisions Climatiques Saisonnières en Afrique Soudano-Sahélienne (PRESASS), we assess their reliability by comparing them to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) project observational data over the past 20 years. The PRESASS forecasts show positive skill and reliability, but a bias toward lower forecast probabilities in the below-normal precipitation category. In addition, we assess the reliability of seasonal precipitation forecasts for the same region using available global dynamical forecast models. We find all models have positive skill and reliability, but this varies geographically. On average, NCEP’s CFS and ECMWF’s SEAS5 systems show greater skill and reliability than the Met Office’s GloSea5, and in turn than Météo-France’s Sys5, but one key caveat is that model performance might depend on the meteorological situation. We discuss the potential for improving use of dynamical model forecasts in the regional climate outlook forums, to improve the reliability of seasonal forecasts in the region and the objectivity of the seasonal forecasting process used in the PRESASS regional climate outlook forum.

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