scholarly journals Toward a Regional-Scale Seasonal Climate Prediction System over Central Italy based on Dynamical Downscaling

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 120 ◽  
Author(s):  
Sangelantoni ◽  
Ferretti ◽  
Redaelli
Keyword(s):  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Scale ◽  
Central Italy ◽  
Seasonal Forecast ◽  
Added Value ◽  
Seasonal Forecasts ◽  
Adaptation Measures ◽  
Seasonal Climate ◽  
Forecast Lead Time

Anticipating seasonal climate anomalies is essential for defining short-term adaptation measures. To be actionable, many stakeholders require seasonal forecasts at the regional scale to be properly coupled to region-specific vulnerabilities. In this study, we present and preliminarily evaluate a regional-scale Seasonal Forecast System (SFS) over Central Italy. This system relies on a double dynamical downscaling performed through the Regional-scale Climate Model (RCM) RegCM4.1. A twelve-member ensemble of the NCEP-CFSv2 provides driving fields for the RegCM. In the first step, the RegCM dynamically downscales NCEP-CFSv2 predictions from a resolution of 100 to 60 km over Europe (RegCM-d1). This first downscaling drives a second downscaling over Central Italy at 12 km (RegCM-d2). To investigate the added value of the downscaled forecasts compared to the driving NCEP-CFSv2, we evaluate the driving CFS, and the two downscaled SFSs over the same (inner) domain. Evaluation involves winter temperatures and precipitations over a climatological period (1982–2003). Evaluation for mean bias, statistical distribution, inter-annual anomaly variability, and hit-rate of anomalous seasons are shown and discussed. Results highlight temperature physical values reproduction benefiting from the downscaling. Downscaled inter-annual variability and probabilistic metrics show improvement mainly at forecast lead-time 1. Downscaled precipitation shows an improved spatial distribution with an undegraded but not improved seasonal forecast quality.

scholarly journals Dynamical Downscaling in Seasonal Climate Forecasts: Comparison between RegCM- and WRF-Based Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 757
Author(s):  
Lorenzo Sangelantoni ◽  
Antonio Ricchi ◽  
Rossella Ferretti ◽  
Gianluca Redaelli
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Regional Scale ◽  
Central Italy ◽  
Probabilistic Approach ◽  
Regional Climate Models ◽  
Added Value ◽  
Temperature Fields

The purpose of the present study is to assess the large-scale signal modulation produced by two dynamically downscaled Seasonal Forecasting Systems (SFSs) and investigate if additional predictive skill can be achieved, compared to the driving global-scale Climate Forecast System (CFS). The two downscaled SFSs are evaluated and compared in terms of physical values and anomaly interannual variability. Downscaled SFSs consist of two two-step dynamical downscaled ensembles of NCEP-CFSv2 re-forecasts. In the first step, the CFS field is downscaled from 100 km to 60 km over Southern Europe (D01). The second downscaling, driven by the corresponding D01, is performed at 12 km over Central Italy (D02). Downscaling is performed using two different Regional Climate Models (RCMs): RegCM v.4 and WRF 3.9.1.1. SFS skills are assessed over a period of 21 winter seasons (1982–2002), by means of deterministic and probabilistic approach and with a metric specifically designed to isolate downscaling signal over different percentiles of distribution. Considering the temperature fields and both deterministic and probabilistic metrics, regional-scale SFSs consistently improve the original CFS Seasonal Anomaly Signal (SAS). For the precipitation, the added value of downscaled SFSs is mainly limited to the topography driven refinement of precipitation field, whereas the SAS is mainly “inherited” by the driving CFS. The regional-scale SFSs do not seem to benefit from the second downscaling (D01 to D02) in terms of SAS improvement. Finally, WRF and RegCM show substantial differences in both SAS and climatologically averaged fields, highlighting a different impact of the common SST driving field.

scholarly journals REVIEW OF SEASONAL CLIMATE FORECASTING FOR AGRICULTURE IN SUB-SAHARAN AFRICA

2011 ◽  
Vol 47 (2) ◽  
pp. 205-240 ◽  
Author(s):  
JAMES W. HANSEN ◽  
SIMON J. MASON ◽  
LIQIANG SUN ◽  
ARAME TALL
Keyword(s):  
Smallholder Farmers ◽  
Seasonal Forecast ◽  
Sub Saharan Africa ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Climate Forecasting ◽  
Probabilistic Forecasts ◽  
Sub Saharan

SUMMARYWe review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

A systematic analysis of the performance of the IPSL-CM5A-LR model for decadal temperature predictions over Europe

2020 ◽  
Author(s):  
Giovanni Sgubin ◽  
Didier Swingedouw ◽  
Juliette Mignot ◽  
Leonard Borchert ◽  
Thomas Noël ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Impact Analysis ◽  
Climate Model ◽  
Added Value ◽  
Climate Projections ◽  
Climate Modelling ◽  
Adaptation Measures ◽  
Windows Of Opportunity ◽  
Skill Scores

<p>Reliable climate predictions over a time-horizon of 1-10 year are crucial for stakeholders and policymakers, as it is the time span for relevant decisions of public and private for infrastructures and other business planning. This promoted, about a decade ago, the development of a new family of climate model: the Decadal Climate Predictions (DCP). Similarly to climate projections, the DCP consists in forced simulations of climate, but initialised from a specific observed climatic state, which potentially represents an added value. Being a relatively new branch of climate modelling the effective application of DCP to impact analysis supporting operational adaptation measures is still conditional on their evaluation.</p><p>Here we contribute to this evaluation by exploring the performance of the IPSL-CM5A-LR DCP system in predicting the air temperature over Europe.  Our assessment of the potentiality of the DCP system follows two main steps: (1) the comparison between the simulated large-scale air temperature from hindcasts and the observations from mid-1900 to present day, i.e. NOAA-20CR dataset, which defines a prediction skill, calculated through both the Anomaly Correlation Coefficient (ACC) and the Root Mean Square Error (RMSE); (2) the detection of the “windows of opportunity”, i.e. specific conditions under which the DCP performs better. The exploration of the windows of opportunity stems from a systematic detection that evaluates the DCP skills for each combination of periods, lead times and seasons. Our analysis involves both raw simulations and de-biased simulations, i.e. outputs data that have been adjusted through the quantile-quantile method.</p><p>Our results evidence a significant added value over most of Europe with respect to non-initialised historical simulations.  Significant skill scores have been generally found over the Mediterranean sector of Europe and UK, while the performance over the rest of Europe results rather conditional on the season and on the period considered. The best predicted months appear to be those between spring and autumn, while low skills have been found for winter months. Also, the predictions appear to be more performant after the ’80, when a rapid warming signal characterised the temperature over Europe: this shift is well reproduced in the initialised simulations. Finally, skill anomalies between raw and debiased outputs are generally minimal. Nevertheless, debiased data show an overall higher RMSE skill, while ACC skill appears to be slightly higher in winter and slightly lower in summer. These findings may be useful for the exploitation of the IPSL DCP for near-term timescale impact analysis over Europe. Also, our systematic approach for the exploration of the windows of opportunity may be at the base of similar investigations applied to other DCP systems.</p>

scholarly journals Impacts of Dynamical Downscaling on Circulation type Statistics: Potential added Value in the Euro-CORDEX Ensemble

2021 ◽  
Author(s):  
Julie Røste ◽  
Oskar A Landgren
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Circulation Type ◽  
Global Model ◽  
Added Value ◽  
Classification Methods ◽  
Circulation Types ◽  
Climate Model Simulations

Abstract Atmospheric circulation type classification methods were applied to an ensemble of 57 regional climate model simulations from Euro-CORDEX, their 11 boundary models from CMIP5 and the ERA5 reanalysis. We compared frequencies of the different circulation types in the simulations with ERA5 and found that the regional models add value especially in the summer season. We applied three different classification methods (the subjective Grosswettertypes and the two optimisation algorithms SANDRA and distributed k-means clustering) from the cost733class software and found that the results are not particularly sensitive to choice of circulation classification method. There are large differences between models. Simulations based on MIROC-MIROC5 and CNRM-CERFACS-CNRM-CM5 show an over-representation of easterly flow and an under-representation of westerly. The downscaled results retain the large-scale circulation from the global model most days, but especially the regional model IPSL-WRF381P changes the circulation more often, which increases the error relative to ERA5. Simulations based on ICHEC-EC-EARTH and MPI-M-MPI-ESM-LR show consistently smaller errors relative to ERA5 in all seasons. The ensemble spread is largest in the summer and smallest in the winter. Under the future RCP8.5 scenario, more than half of the ensemble shows an increase in frequency of north-easterly flow and decrease in the Central-Eastern European high and south-easterly flow. There is in general a strong agreement in the sign of the change between the regional simulations and the data from the corresponding global model.

scholarly journals Development of seasonal climate outlooks for agriculture in Finland

2020 ◽  
Vol 17 ◽  
pp. 269-277
Author(s):  
Andrea Vajda ◽  
Otto Hyvärinen
Keyword(s):  
Management Practices ◽  
Seasonal Forecast ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Seasonal Climate ◽  
Crop Choice ◽  
Temperature Forecast ◽  
Dry Conditions

Abstract. Seasonal climate forecast products offer useful information for farmers supporting them in planning and making decisions in their management practices, such as crop choice, planting and harvesting time, and water management. Driven by the need of stakeholders for tailored seasonal forecast products, our goal was to assess the applicability of seasonal forecast outputs in agriculture and to develop and pilot with stakeholders a set of seasonal climate outlooks for this sector in Finland. Finnish end users were involved in both the design and testing of the outlooks during the first pilot season of 2019. The seasonal climate outlooks were developed using the SEAS5 seasonal forecast system provided by ECMWF. To improve the prediction skill of the seasonal forecast data, several bias adjustment approaches were evaluated. The tested methods increased the quality of temperature forecast, but no suitable approach was found for eliminating the biases from precipitation data. Besides the widely applied indices, such as mean temperature, growing degree days, cold spell duration, total precipitation and dry conditions, new sector-oriented indices (such as progress of growing season) have been implemented and issued for various lead times (up to 3 months). The first result of forecast evaluation, the development of seasonal forecast indices and the first pilot season of May–October 2019 are presented. We found that the temperature-based outlooks performed well, with better performance skills for short lead times, providing useful information for the farmers in activity management. Precipitation indices had poor skills for each forecasted month, and further research is needed for improving the quality of forecast for Finland. The farmers who have tested the seasonal climate outlooks considered those beneficial and valuable, helping them in planning their activities. Following the first pilot season, further research and implementation work took place to improve our understanding of the skill of seasonal forecasts and increase the quality of tailored seasonal climate services.

scholarly journals Skilful seasonal forecasts of streamflow over Europe?

2017 ◽  
Author(s):  
Louise Arnal ◽  
Hannah L. Cloke ◽  
Elisabeth Stephens ◽  
Fredrik Wetterhall ◽  
Christel Prudhomme ◽  
...  
Keyword(s):  
Lead Time ◽  
Climate Model ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Streamflow Forecasts ◽  
Meteorological Observations ◽  
Seasonal Streamflow

Abstract. This paper presents a Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts, benchmarked against the Ensemble Streamflow Prediction (ESP) forecasting approach. 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. 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 seven months of lead time, for certain months within a season. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making. 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 most of Europe. Patterns in the EFAS seasonal streamflow hindcasts skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim to improve climate-model based seasonal streamflow forecasting.

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.

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 Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jared H. Bowden ◽  
Kevin D. Talgo ◽  
Tanya L. Spero ◽  
Christopher G. Nolte
Keyword(s):  
Climate Model ◽  
Dynamical Downscaling ◽  
Regional Climate ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Added Value ◽  
Tropical Storms ◽  
The Standardized Precipitation Index ◽  
The Impact ◽  
Resources Planning

In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drought timescales that have implications for agriculture and water resources planning. The regional climate generated by WRF has the largest improvement over reanalysis for SPI correlation with observations as the drought timescale increases. This suggests that dynamically downscaled fields may be more reliable than larger-scale fields for water resource applications (e.g., water storage within reservoirs). WRF improves the timing and intensity of moderate to extreme wet and dry periods, even in regions with homogenous terrain. This study also examines changes in SPI from the extreme drought of 1988 and three “drought busting” tropical storms. Each of those events illustrates the importance of using downscaling to resolve the spatial extent of droughts. The analysis of the “drought busting” tropical storms demonstrates that while the impact of these storms on ending prolonged droughts is improved by the RCM relative to the reanalysis, it remains underestimated. These results illustrate the importance and some limitations of using RCMs to project drought.

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