Supplementary material to "The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information"

Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sense of certainty. This article reports the findings of a user-needs survey, conducted with 50 representatives of organizations in Europe from a variety of sectors (e.g. water management, forestry, energy, tourism, health) interested in seasonal and interannual climate forecasts. We find that while many participating organizations perform their own ‘in house’ risk analysis most require some form of processing and interpretation by forecast providers. However, we also find that while users tend to perceive seasonal and interannual forecasts to be useful, they often find them difficult to understand, highlighting the need for communication formats suitable for both expert and non-expert users. In addition, our results show that people tend to prefer familiar formats for receiving information about uncertainty. The implications of these findings for both the providers and users of climate information are discussed.

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Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Geoscientific Model Development ◽

10.5194/gmd-11-351-2018 ◽

2018 ◽

Vol 11 (1) ◽

pp. 351-368 ◽

Cited By ~ 7

Author(s):

Alexander Pasternack ◽

Jonas Bhend ◽

Mark A. Liniger ◽

Henning W. Rust ◽

Wolfgang A. Müller ◽

...

Keyword(s):

Lead Time ◽

Systematic Errors ◽

Time Dependent ◽

Climate Forecast ◽

Climate Forecasts ◽

Probabilistic Forecasts ◽

Short Period ◽

Near Term ◽

Decadal Climate ◽

Forecast Quality

Abstract. Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead-time-dependent systematic errors (drift) and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of reforecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip prototype system and find consistent, and sometimes considerable, improvements in forecast quality compared with a simple calibration of the lead-time-dependent systematic errors.

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Reliability of Regression-Corrected Climate Forecasts

Journal of Climate ◽

10.1175/jcli-d-13-00565.1 ◽

2014 ◽

Vol 27 (9) ◽

pp. 3393-3404 ◽

Cited By ~ 10

Author(s):

Michael K. Tippett ◽

Timothy DelSole ◽

Anthony G. Barnston

Keyword(s):

Sample Size ◽

Climate Model ◽

Sampling Error ◽

Small Sample Size ◽

Small Sample ◽

Climate Forecast ◽

Regression Parameters ◽

Climate Forecasts ◽

Probability Forecasts ◽

The Impact

Abstract Regression is often used to calibrate climate model forecasts with observations. Reliability is an aspect of forecast quality that refers to the degree of correspondence between forecast probabilities and observed frequencies of occurrence. While regression-corrected climate forecasts are reliable in principle, the estimated regression parameters used in practice are affected by sampling error. The low skill and small sample sizes typically encountered in climate prediction imply substantial sampling error in the estimated regression parameters. Here the reliability of regression-corrected climate forecasts is analyzed for the case of joint-Gaussian distributed ensemble forecasts and observations with regression parameters estimated by least squares. Hypothesis testing of the regression parameters provides direct information about the skill and reliability of the uncorrected ensemble-based probability forecasts. However, the regression-corrected probability forecasts with estimated parameters are systematically “overconfident” because sampling error causes a positive bias in the regression forecast signal variance, despite the fact that the estimates of the regression parameters are themselves unbiased. An analytical description of the reliability diagram of a generic regression-corrected climate forecast is derived and is shown to depend on sample size and population correlation skill, with small sample size and low skill being factors that increase overconfidence. The analytical reliability estimate is shown to capture the effect of sampling error in synthetic data experiments and in a 29-yr dataset of NOAA Climate Forecast System version 2 predictions of seasonal precipitation totals over the Americas. The impact of sampling error on the reliability of regression-corrected forecast has been previously unrecognized and affects all regression-based forecasts. The use of regression parameters estimated by shrinkage methods such as ridge regression substantially reduces overconfidence.

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Assessment of Knowledge Co-production in Adaptation to Rainfall Variability in Kitui South Sub-county, Kenya

Asian Journal of Agricultural Extension Economics & Sociology ◽

10.9734/ajaees/2020/v38i530350 ◽

2020 ◽

pp. 87-97

Author(s):

Morris M. Mwatu ◽

Charles W. Recha ◽

Kennedy N. Ondimu

Keyword(s):

Rainfall Variability ◽

Study Data ◽

Climate Forecast ◽

Seasonal Climate Forecast ◽

Scientific Methods ◽

Seasonal Climate ◽

Qualitative And Quantitative ◽

Climate Forecasting ◽

Climate Forecasts ◽

Household Heads

Aims: This study tried to investigate the extent of knowledge co-production between indigenous farmers and agricultural extension in dry lands. Study Design: The study adopted survey research design where both qualitative and quantitative approaches were used. Place and Duration of Study: The study was carried out in Kitui South sub-County in the semi-arid Southeastern Kenya. Data was collected between June 2019 and August 2019. Methodology: An enumerator-administered questionnaire was used to collect data from 311 household heads. Purposive and proportional sampling techniques were used to select households which participated in the study. Data was analyzed with the aid of SPSS Version 20. Percentages and proportions were used to establish instances of knowledge co-production between indigenous and modern scientific methods of farming. Results: The study established that all households used both indigenous and scientific methods of farming except in irrigation and crop harvesting methods. The highest co-production was between use of locally preserved seeds and use of modern seasonal climate forecast (71.4%), use of traditional seasonal climate forecasts and use of modern seasonal climate (64.6%) as well as use of traditional crop storage and use modern seasonal climate forecast (59.2%). Seasonal climate forecasting was the leading corresponding method of knowledge co-production in the study area. Conclusion: The study concludes that use of both indigenous and modern methods of farming can improve adaptation to rainfall variability. The study recommends access to adequate water to promote knowledge co-production on irrigation which was lacking yet very critical in dealing with rainfall variability in the study area.

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The CSTools (v4.0) Toolbox: from Climate Forecasts to Climate Forecast Information

10.5194/gmd-2021-368 ◽

2021 ◽

Author(s):

Núria Pérez-Zanón ◽

Louis-Philippe Caron ◽

Silvia Terzago ◽

Bert Van Schaeybroeck ◽

Llorenç Lledó ◽

...

Keyword(s):

R Package ◽

Climate Forecast ◽

Forecast Combination ◽

Post Processing ◽

Seasonal Forecasts ◽

Climate Forecasts ◽

Extreme Wind ◽

Forecast Data ◽

Extreme Wind Speed

Abstract. Despite the wealth of existing climate forecast data, only a small part is effectively exploited for sectoral applications. A major cause of this is the lack of integrated tools that allow the translation of data into useful and skilful climate information. This barrier is addressed through the development of an R package. CSTools is an easy-to-use toolbox designed and built to assess and improve the quality of climate forecasts for seasonal to multi–annual scales. The package contains process-based state-of-the-art methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Due to the design of the toolbox in individual functions, the users can develop their own post-processing chain of functions as shown in the use cases presented in this manuscript: the analysis of an extreme wind speed event, the generation of seasonal forecasts of snow depth based on the SNOWPACK model and the post-processing of data to be used as input for the SCHEME hydrological model.

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Climate Forecasts for Corn Producer Decision Making

Earth Interactions ◽

10.1175/2013ei000541.1 ◽

2014 ◽

Vol 18 (5) ◽

pp. 1-8 ◽

Cited By ~ 24

Author(s):

Eugene S. Takle ◽

Christopher J. Anderson ◽

Jeffrey Andresen ◽

James Angel ◽

Roger W. Elmore ◽

...

Keyword(s):

United States ◽

Decision Making ◽

Weather Conditions ◽

The United States ◽

Climate Forecast ◽

Climate Information ◽

Corn Production ◽

Decision Tools ◽

Decision Cycle ◽

Climate Forecasts

Abstract Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.

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Application of Postprocessing to Watershed-Scale Subseasonal Climate Forecasts over the Contiguous United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0155.1 ◽

2020 ◽

Vol 21 (5) ◽

pp. 971-987

Author(s):

Sarah A. Baker ◽

Andrew W. Wood ◽

Balaji Rajagopalan

Keyword(s):

Water Management ◽

Spatial Scales ◽

Global Climate Model ◽

Forecast Skill ◽

Central Component ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Climate Services ◽

Climate Forecasts

AbstractSubseasonal to seasonal (S2S) climate forecasting has become a central component of climate services aimed at improving water management. In some cases, operational S2S climate predictions are translated into inputs for follow-on analyses or models, whereas the S2S predictions on their own may provide for qualitative situational awareness. At the spatial scales of water management, however, S2S climate forecasts often suffer from systematic biases, and low skill and reliability. This study assesses the potential to improve S2S forecast skill and salience for watershed applications through the use of postprocessing to harness skills in large-scale fields from the global climate model forecast outputs. To this end, the components-based technique—partial least squares regression (PLSR)—is used to improve the skill of biweekly temperature and precipitation forecasts from the Climate Forecast System version 2 (CFSv2). The PLSR method forms predictor components based on a cross-validated analysis of hindcasts from CFSv2 climate and land surface fields, and the results are benchmarked against raw CFSv2 forecasts, remapped to intermediate-scale watershed areas. We find that postprocessing affords marginal to moderate gains in skill in many watersheds, raising climate forecast skill above a usability threshold over the four seasons analyzed. In other locations, however, postprocessing fails to improve skill, particularly for extreme events, and can lead to unreliably narrow forecast ranges. This work presents evidence that the statistical postprocessing of climate forecast system outputs has potential to improve forecast skill, but that more thorough study of alternative approaches and predictors may be needed to achieve comprehensively positive outcomes.

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Coupling of a physically based lake model into the climate forecast system to improve winter climate forecasts for the Great Lakes region

Climate Dynamics ◽

10.1007/s00382-019-04939-2 ◽

2019 ◽

Vol 53 (9-10) ◽

pp. 6503-6517

Author(s):

Zhemin Lv ◽

Shaobo Zhang ◽

Jiming Jin ◽

Yihua Wu ◽

Michael B. Ek

Keyword(s):

Great Lakes ◽

Great Lakes Region ◽

Climate Forecast ◽

Forecast System ◽

Climate Forecast System ◽

Winter Climate ◽

Lake Model ◽

Climate Forecasts ◽

Physically Based

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Benchmark, predictability, and forecast skill of terrestrial water storage based on CMIP6 decadal hindcasts and land surface ensemble simulations

10.5194/egusphere-egu2020-6304 ◽

2020 ◽

Author(s):

Enda Zhu ◽

Xing Yuan

Keyword(s):

Initial Conditions ◽

Water Storage ◽

Forecast Skill ◽

Precipitation Forecast ◽

Climate Forecast ◽

Terrestrial Water Storage ◽

Ensemble Simulations ◽

Climate Forecasts ◽

Terrestrial Water ◽

Decadal Climate

<p><span>Terrestrial water storage (TWS), including surface water storage, soil water storage, and groundwater storage, is critical for the global hydrological cycle and freshwater resources. A reliable decadal prediction of TWS can provide valuable information for sustainable managements of water resources and infrastructures in the face of climate change. Generally, the hydrological predictability mainly comes from two sources, i.e., initial conditions and boundary conditions. To date, the dependence of TWS forecast skill on the accuracy of initial hydrological conditions and decadal climate forecasts is not clear, and the benchmark skill remains unknown. In this work, we use decadal climate hindcasts from CMIP and perform hydrological ensemble simulations to estimate a baseline decadal forecast skill containing the two predictability sources information for TWS over global major river basins with an elasticity framework that considers varying skill of initial conditions and climate forecasts. With the incorporation of decadal climate forecast, our benchmark skill for TWS incorporated is significantly higher than initial conditions-based forecast skill over 25% and 31% basins for the leads of 1&#8211;4 and 3&#8211;6 years, especially over mid- and high-latitudes. Although the decadal precipitation forecast skill based on individual model is limited, the ensemble forecasts from multiple climate models are better than individuals. In addition, the standardized precipitation index (SPI) predictability and forecast skill from the latest CMIP6 decadal hindcast data are being investigated. Preliminary results suggest that predictability and forecast skill of SPI are positively correlated in general, and the predictability is higher than forecast skill, indicating the room for improving hydro-climate forecast. Our findings provide a new benchmark for verifying the success of decadal TWS forecasts and imply the possibility of improving decadal hydrological forecasts by using dynamical climate prediction information which still has room for improvement.</span></p>

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Parametric Decadal Climate Forecast Recalibration (DeFoReSt 1.0)

10.5194/gmd-2017-162 ◽

2017 ◽

Author(s):

Alexander Pasternack ◽

Jonas Bhend ◽

Mark A. Liniger ◽

Henning W. Rust ◽

Wolfgang A. Müller ◽

...

Keyword(s):

Lead Time ◽

Systematic Errors ◽

Time Dependent ◽

Climate Forecast ◽

Climate Forecasts ◽

Probabilistic Forecasts ◽

Short Period ◽

Near Term ◽

Decadal Climate ◽

Forecast Quality

Abstract. Near-term climate predictions such as decadal climate forecasts are increasingly being used to guide adaptation measures. For near-term probabilistic predictions to be useful, systematic errors of the forecasting systems have to be corrected. While methods for the calibration of probabilistic forecasts are readily available, these have to be adapted to the specifics of decadal climate forecasts including the long time horizon of decadal climate forecasts, lead time dependent systematic errors (drift), and the errors in the representation of long-term changes and variability. These features are compounded by small ensemble sizes to describe forecast uncertainty and a relatively short period for which typically pairs of re-forecasts and observations are available to estimate calibration parameters. We introduce the Decadal Climate Forecast Recalibration Strategy (DeFoReSt), a parametric approach to recalibrate decadal ensemble forecasts that takes the above specifics into account. DeFoReSt optimizes forecast quality as measured by the continuous ranked probability score (CRPS). Using a toy model to generate synthetic forecast observation pairs, we demonstrate the positive effect on forecast quality in situations with pronounced and limited predictability. Finally, we apply DeFoReSt to decadal surface temperature forecasts from the MiKlip Prototype system and find consistent and sometimes considerable improvements in forecast quality compared with a simple calibration of the lead time dependent systematic errors.

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