Spatial association of anomaly correlation for GCM seasonal forecasts of global precipitation

2020 ◽  
Vol 55 (7-8) ◽  
pp. 2273-2286
Author(s):  
Tongtiegang Zhao ◽  
Haoling Chen ◽  
Weixin Xu ◽  
Huayang Cai ◽  
Denghua Yan ◽  
...  
Keyword(s):  
Spatial Association ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Global Precipitation

scholarly journals Attributing correlation skill of dynamical global precipitation forecasts to statistical ENSO teleconnection using a set theory based approach

2021 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Haoling Chen ◽  
Quanxi Shao
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Models ◽  
Coefficient Of Determination ◽  
Positive Anomaly ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Global Precipitation ◽  
Correlation Skill

Abstract. Climate teleconnections are essential for the verification of valuable precipitation forecasts generated by global climate models (GCMs). This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño-Southern Oscillation (ENSO) teleconnection by using the coefficient of determination (R2). Specifically, observed precipitation is respectively regressed against GCM forecasts, Niño3.4 and both of them and then the intersection operation is implemented to quantify the overlapping R2 for GCM forecasts and Niño3.4. The significance of overlapping R2 and the sign of ENSO teleconnection facilitate three cases of attribution, i.e., significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts of global precipitation. For grid cells around the world, the ratio of significantly positive anomaly correlation attributable to positive (negative) ENSO teleconnection is respectively 10.8 % (11.7 %) in December-January-February (DJF), 7.1 % (7.3 %) in March-April-May (MAM), 6.3 % (7.4 %) in June-July-August (JJA) and 7.0 % (14.3 %) in September-October-November (SON). The results not only confirm the prominent contributions of ENSO teleconnection to GCM forecasts, but also present spatial plots of regions where significantly positive anomaly correlation is subject to positive ENSO teleconnection, negative ENSO teleconnection and teleconnections other than ENSO. Overall, the proposed attribution approach can serve as an effective tool to investigate the source of predictability for GCM seasonal forecasts of global precipitation.

scholarly journals Significant spatial patterns from the GCM seasonal forecasts of global precipitation

2019 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Wei Zhang ◽  
Yongyong Zhang ◽  
Xiaohong Chen
Keyword(s):  
Spatial Patterns ◽  
Global Climate ◽  
Predictive Performance ◽  
Global Climate Models ◽  
Moran’S I ◽  
Grid Cells ◽  
Seasonal Forecasts ◽  
Moran's I ◽  
Anomaly Correlation ◽  
Global Precipitation

Abstract. Fully-coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate center exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least one of the ten sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

scholarly journals Significant spatial patterns from the GCM seasonal forecasts of global precipitation

2020 ◽  
Vol 24 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Tongtiegang Zhao ◽  
Wei Zhang ◽  
Yongyong Zhang ◽  
Zhiyong Liu ◽  
Xiaohong Chen
Keyword(s):  
Spatial Patterns ◽  
Global Climate ◽  
Grid Cell ◽  
Predictive Performance ◽  
Moran’S I ◽  
Grid Cells ◽  
Seasonal Forecasts ◽  
Moran's I ◽  
Anomaly Correlation ◽  
Global Precipitation

Abstract. Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80 % of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.

NMME-based Assessment of Prediction Skills of US Summertime Droughts and Pluvials: Role of Near-surface Temperature Prediction Skill

2021 ◽  
Author(s):  
Jonghun Kam ◽  
Sungyoon Kim ◽  
Joshua Roundy
Keyword(s):  
Surface Temperature ◽  
Climate Extremes ◽  
Prediction Skill ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Anomaly Correlation ◽  
The North ◽  
Anomaly Correlation Coefficient ◽  
Precipitation Anomalies

<p>This study used the North American Multi-Model Ensemble (NMME) system to understand the role of near surface temperature in the prediction skill for US climate extremes. In this study, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC) or 2-meter air temperature (T2m) over the contiguous United States (CONUS) during 1982–2012. The strength of the PREC-T2m coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. This study also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (-0.65), and 2012 (-0.60) when the PREC-T2m coupling is weaker or stronger than the 1982–2012 climatology (ACC:-0.34). This study found that most of the NMME models show stronger (negative) PREC-T2m coupling than the observed coupling, indicating that they fail to reproduce interannual variability of the observed PREC-T2m coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong PREC-T2m coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Lastly, we explored how the forecasting skill for SSTs over north Pacific and Atlantic Oceans affects the forecasting skill for T2m and PREC over the US. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.</p>

scholarly journals Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods

2021 ◽  
Author(s):  
Filippo Calì Quaglia ◽  
Silvia Terzago ◽  
Jost von Hardenberg
Keyword(s):  
Lead Time ◽  
Mediterranean Region ◽  
Added Value ◽  
Lead Times ◽  
Model Ensemble ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Temperature And Precipitation ◽  
Precipitation Anomalies ◽  
The Mediterranean

AbstractThis study considers a set of state-of-the-art seasonal forecasting systems (ECMWF, MF, UKMO, CMCC, DWD and the corresponding multi-model ensemble) and quantifies their added value (if any) in predicting seasonal and monthly temperature and precipitation anomalies over the Mediterranean region compared to a simple forecasting method based on the ERA5 climatology (CTRL) or the persistence of the ERA5 anomaly (PERS). This analysis considers two starting dates, May 1st and November 1st and the forecasts at lead times up to 6 months for each year in the period 1993–2014. Both deterministic and probabilistic metrics are employed to derive comprehensive information on the forecast quality in terms of association, reliability/resolution, discrimination, accuracy and sharpness. We find that temperature anomalies are better reproduced than precipitation anomalies with varying spatial patterns across different forecast systems. The Multi-Model Ensemble (MME) shows the best agreement in terms of anomaly correlation with ERA5 precipitation, while PERS provides the best results in terms of anomaly correlation with ERA5 temperature. Individual forecast systems and MME outperform CTRL in terms of accuracy of tercile-based forecasts up to lead time 5 months and in terms of discrimination up to lead time 2 months. All seasonal forecast systems also outperform elementary forecasts based on persistence in terms of accuracy and sharpness.

scholarly journals The verification of seasonal precipitation forecasts for early warning in Zambia and Malawi

2015 ◽  
Vol 12 (1) ◽  
pp. 31-36 ◽  
Author(s):  
O. Hyvärinen ◽  
L. Mtilatila ◽  
K. Pilli-Sihvola ◽  
A. Venäläinen ◽  
H. Gregow
Keyword(s):  
Regional Climate ◽  
Global Precipitation Climatology Project ◽  
Brier Score ◽  
Seasonal Precipitation ◽  
Seasonal Forecasts ◽  
African Countries ◽  
Precipitation Climatology ◽  
Global Precipitation Climatology Centre ◽  
Global Precipitation

Abstract. We assess the probabilistic seasonal precipitation forecasts issued by Regional Climate Outlook Forum (RCOF) for the area of two southern African countries, Malawi and Zambia from 2002 to 2013. The forecasts, issued in August, are of rainy season rainfall accumulations in three categories (above normal, normal, and below normal), for early season (October–December) and late season (January–March). As observations we used in-situ observations and interpolated precipitation products from Global Precipitation Climatology Project (GPCP), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP). Differences between results from different data products are smaller than confidence intervals calculated by bootstrap. We focus on below normal forecasts as they were deemed to be the most important for society. The well-known decomposition of Brier score into three terms (Reliability, Resolution, and Uncertainty) shows that the forecasts are rather reliable or well-calibrated, but have a very low resolution; that is, they are not able to discriminate different events. The forecasts also lack sharpness as forecasts for one category are rarely higher than 40 % or less than 25 %. However, these results might be unnecessarily pessimistic, because seasonal forecasts have gone through much development during the period when the forecasts verified in this paper were issued, and forecasts using current methodology might have performed better.

scholarly journals Assessment of seasonal forecast skills of temperature and precipitation: a comparison of 5 different models over the Mediterranean region

2021 ◽  
Author(s):  
Filippo Calì Quaglia ◽  
Silvia Terzago ◽  
Jost von Hardenberg
Keyword(s):  
Characteristic Curve ◽  
Seasonal Forecast ◽  
Added Value ◽  
Sources Of Information ◽  
Seasonal Forecasts ◽  
Anomaly Correlation ◽  
Probability Score ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
The Mediterranean

<p>Seasonal forecasts are increasingly employed as sources of information on the expected evolution of climate in the few months ahead by various end-users. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly time scale. The focus area is the Mediterranean, a densely populated region identified as a hotspot for climate change, where seasonal forecasts could be useful to a variety of economic sectors, including water management, hydropower production, agriculture.</p><p>In this study, seasonal forecast systems issued by 5 European institutions (ECMWF, Météo-France, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them, have been analysed. The added value of these forecast systems with respect to simpler forecast approaches based on climatology and persistence has been investigated.</p><p>Different deterministic (Anomaly Correlation Coefficient) and probabilistic scores (Ranked Probability Score, Continuous Ranked Probability Score and Receiver Operating Characteristic Curve) have been employed to obtain an overall assessment of the quality of the forecasts (as of Murphy, 1993 and WMO, 2018), using ERA5 dataset as a reference. We performed the analysis using 6-month forecasts starting in May and November to reproduce the following summer and the winter seasons.</p><p>In general, temperature patterns and respective skill scores are better reproduced than those regarding precipitation. The anomaly correlation coefficients for MME reach the best agreement values for each season and variable except for winter temperature. Different behaviours are found for the different skill scores; their high spatial variability suggests that smaller regions could perform better for a single variable or starting date. Seasonal forecast systems, despite some limitations, show an added value with respect to simple forecast approaches based on the climatology or persistence.</p>

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