scholarly journals A global empirical system for probabilistic seasonal climate prediction

2015 ◽  
Vol 8 (5) ◽  
pp. 3941-3970 ◽  
Author(s):  
J. M. Eden ◽  
G. J. van Oldenborgh ◽  
E. Hawkins ◽  
E. B. Suckling
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Surface Air Temperature ◽  
Climate Change Signal ◽  
Seasonal Forecasts ◽  
Monthly Basis ◽  
Physical Relationship ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Empirical System

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, NGOs and companies and relies on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated using correlation and skill scores. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known ENSO teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

scholarly journals A global empirical system for probabilistic seasonal climate prediction

2015 ◽  
Vol 8 (12) ◽  
pp. 3947-3973 ◽  
Author(s):  
J. M. Eden ◽  
G. J. van Oldenborgh ◽  
E. Hawkins ◽  
E. B. Suckling
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Climate Change Signal ◽  
Seasonal Forecasts ◽  
Physical Relationship ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Empirical System

Abstract. Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Skillful Wintertime North American Temperature Forecasts out to 4 Weeks Based on the State of ENSO and the MJO*

2014 ◽  
Vol 29 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Nathaniel C. Johnson ◽  
Dan C. Collins ◽  
Steven B. Feldstein ◽  
Michelle L. L’Heureux ◽  
Emily E. Riddle
Keyword(s):  
North America ◽  
Large Scale ◽  
Southern Oscillation ◽  
Linear Trend ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Initial States ◽  
Statistical Relationships

Abstract Previous work has shown that the combined influence of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) significantly impacts the wintertime circulation over North America for lead times up to at least 4 weeks. These findings suggest that both the MJO and ENSO may prove beneficial for generating a seamless prediction link between short-range deterministic forecasts and longer-range seasonal forecasts. To test the feasibility of this link, wintertime (December–March) probabilistic 2-m temperature (T2m) forecasts over North America are generated solely on the basis of the linear trend and statistical relationships with the initial state of the MJO and ENSO. Overall, such forecasts exhibit substantial skill for some regions and some initial states of the MJO and ENSO out to a lead time of approximately 4 weeks. In addition, the primary ENSO T2m regions of influence are nearly orthogonal to those of the MJO, which suggests that the MJO and ENSO generally excite different patterns within the continuum of large-scale atmospheric teleconnections. The strong forecast skill scores for some regions and initial states confirm the promise that information from the MJO and ENSO may offer forecasts of opportunity in weeks 3 and 4, which extend beyond the current 2-week extended-range outlooks of the National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Center (CPC), and an intraseasonal link to longer-range probabilistic forecasts.

scholarly journals Influence of Forced Large-Scale Atmospheric Patterns on Surface Air Temperature in China

2011 ◽  
Vol 139 (3) ◽  
pp. 830-852 ◽  
Author(s):  
Xiaojing Jia ◽  
Hai Lin
Keyword(s):  
Air Temperature ◽  
General Circulation ◽  
Large Scale ◽  
Statistical Approach ◽  
Surface Air Temperature ◽  
Tropical Pacific ◽  
Second Phase ◽  
Seasonal Forecasts ◽  
The Relationship ◽  
The Tropical Pacific

Abstract The seasonality of the influence of the tropical Pacific sea surface temperature (SST)-forced large-scale atmospheric patterns on the surface air temperature (SAT) over China is investigated for the period from 1969 to 2001. Both observations and output from four atmospheric general circulation models (GCMs) involved in the second phase of the Canadian Historical Forecasting Project (HFP) are used. The large-scale atmospheric patterns are obtained by applying a singular value decomposition (SVD) analysis between 500-hPa geopotential height (Z500) in the Northern Hemisphere and SST in the tropical Pacific Ocean. Temporal correlations between the SAT over China and the expansion coefficients of the leading SVD modes show that SAT over China can be significantly influenced by these large-scale atmospheric patterns, especially by the second SVD mode. The relationship between the SAT over China and the leading atmospheric patterns in the observations is partly captured by the HFP models. Furthermore, seasonal forecasts of SAT over China are postprocessed using a statistical approach. This statistical approach is designed based on the relationship between the forecast Z500 and the observed SST to calibrate the SAT forecasts. Results show that the forecast skill of the postprocessed SAT over China can be improved in all seasons to some extent, with that in fall having the most significant improvement. Possible mechanisms behind the improvement of the forecast are investigated.

scholarly journals Seasonal Forecasts of the Twentieth Century

2020 ◽  
Vol 101 (8) ◽  
pp. E1413-E1426 ◽  
Author(s):  
Antje Weisheimer ◽  
Daniel J. Befort ◽  
Dave MacLeod ◽  
Tim Palmer ◽  
Chris O’Reilly ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Climate Anomalies ◽  
Global Circulation Models ◽  
Skill Scores ◽  
Physically Based ◽  
Forecast Models

Abstract Forecasts of seasonal climate anomalies using physically based global circulation models are routinely made at operational meteorological centers around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years that are used to estimate skill and to calibrate the forecasts. Hindcasts are usually produced over a period of around 20–30 years. However, recent studies have demonstrated that seasonal forecast skill can undergo pronounced multidecadal variations. These results imply that relatively short hindcasts are not adequate for reliably testing seasonal forecasts and that small hindcast sample sizes can potentially lead to skill estimates that are not robust. Here we present new and unprecedented 110-year-long coupled hindcasts of the next season over the period 1901–2010. Their performance for the recent period is in good agreement with those of operational forecast models. While skill for ENSO is very high during recent decades, it is markedly reduced during the 1930s–1950s. Skill at the beginning of the twentieth century is, however, as high as for recent high-skill periods. Consistent with findings in atmosphere-only hindcasts, a midcentury drop in forecast skill is found for a range of atmospheric fields, including large-scale indices such as the NAO and the PNA patterns. As with ENSO, skill scores for these indices recover in the early twentieth century, suggesting that the midcentury drop in skill is not due to a lack of good observational data. A public dissemination platform for our hindcast data is available, and we invite the scientific community to explore them.

Winter surface air temperature prediction over Japan using artificial neural networks

2021 ◽  
Author(s):  
J. V. Ratnam ◽  
Masami Nonaka ◽  
Swadhin K. Behera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Correlation Coefficients ◽  
Ann Model ◽  
Anomaly Correlation ◽  
The North ◽  
Skill Scores ◽  
Artificial Neural

AbstractThe machine learning technique, namely Artificial Neural Networks (ANN), is used to predict the surface air temperature (SAT) anomalies over Japan in the winter months of December, January and February for the period 1949/50 to 2019/20. The predictions are made for the four regions Hokkaido, North, Central and West of Japan. The inputs to the ANN model are derived from the anomaly correlation coefficients among the SAT anomalies over the regions of Japan and the global SAT and sea surface temperature anomalies. The results are validated using anomaly correlation coefficient (ACC) skill scores with the observation. It is found that the ANN predictions over Hokkaido have higher ACC skill scores compared to the ACC scores over the other three regions. The ANN predicted SAT anomalies are compared with that of ensemble mean of 8 of the North American Multi-Model Ensemble (NMME) models besides comparing them with the persistent anomalies. The ANN predictions over all the four regions have higher ACC skill scores compared to the NMME model skill scores in the common period of 1982/83 to 2018/19. The ANN predicted SAT anomalies also have higher Hit rate and lower False alarm rate compared to the NMME predicted SAT anomalies. All these indicate that the ANN model is a promising tool for predicting the winter SAT anomalies over Japan.

scholarly journals An Evaluation of Model Output Statistics for Subseasonal Streamflow Forecasting in European Catchments

2019 ◽  
Vol 20 (7) ◽  
pp. 1399-1416
Author(s):  
Simon Schick ◽  
Ole Rössler ◽  
Rolf Weingartner
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Linear Models ◽  
Surface Air Temperature ◽  
Grid Cell ◽  
Model Output ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Total Runoff ◽  
Model Output Statistics

AbstractSubseasonal and seasonal forecasts of the atmosphere, oceans, sea ice, or land surfaces often rely on Earth system model (ESM) simulations. While the most recent generation of ESMs simulates runoff per land surface grid cell operationally, it does not typically simulate river streamflow directly. Here, we apply the model output statistics (MOS) method to the hindcast archive of the European Centre for Medium-Range Weather Forecasts (ECMWF). Linear models are tested that regress observed river streamflow on surface runoff, subsurface runoff, total runoff, precipitation, and surface air temperature simulated by ECMWF’s forecast systems S4 and SEAS5. In addition, the pool of candidate predictors contains observed precipitation and surface air temperature preceding the date of prediction. The experiment is conducted for 16 European catchments in the period 1981–2006 and focuses on monthly average streamflow at lead times of 0 and 20 days. The results show that skill against the streamflow climatology is frequently absent and varies considerably between predictor combinations, catchments, and seasons. Using streamflow persistence as a benchmark model further deteriorates skill. This is most pronounced for a catchment that features lakes, which extend to about 14% of the catchment area. On average, however, the predictor combinations using the ESM runoff simulations tend to perform best.

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 Urbanization effects on surface air temperature trends in Thailand during 1970-2019

2020 ◽  
Vol 26 (5) ◽  
pp. 200378-0
Author(s):  
Boonlue Kachenchart ◽  
Chaiyanan Kamlangkla ◽  
Nattapong Puttanapong ◽  
Atsamon Limsakul
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Urban Expansion ◽  
Great Influence ◽  
Surface Air Temperature ◽  
Urban Setting ◽  
Temperature Trends ◽  
Urban Warming ◽  
Wide Range ◽  
Increasing Trend

Continued urban expansion undergone in the last decades has converted many weather stations in Thailand into suburban and urban setting. Based on homogenized data during 1970-2019, therefore, this study examines urbanization effects on mean surface air temperature (Tmean) trends in Thailand. Analysis shows that urban-type stations register the strongest warming trends while rural-type stations exhibit the smallest trends. Across Thailand, annual urban-warming contribution exhibits a wide range (< 5% to 77%), probably manifesting the Urban Heat Island (UHI) differences from city to city resulting from the varied urban characteristics and climatic background. Country-wide average urban warming contribution shows a significant increasing trend of 0.15 <sup>o</sup>C per decade, accounting for 40.5% of the overall warming. This evidence indicates that urban expansion has great influence on surface warming, and the urban-warming bias contributes large fraction of rising temperature trends in Thailand. The increasing trend of annual Tmean for Thailand as a whole after adjusting urban-warming bias is brought down to the same rate as the annual global mean temperature trend, reflecting a national baseline signal driven by large-scale anthropogenic-induced climate change. Our results provide a scientific reference for policy makers and urban planners to mitigate substantial fraction of the UHI warming.

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