scholarly journals Supplementary material to "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 ◽  
Seasonal Forecasts ◽  
Supplementary Material ◽  
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.

scholarly journals Three Putative Types of El Niño Revealed by Spatial Variability in Impact on Australian Wheat Yield

2005 ◽  
Vol 18 (10) ◽  
pp. 1566-1574 ◽  
Author(s):  
A. B. Potgieter ◽  
G. L. Hammer ◽  
H. Meinke ◽  
R. C. Stone ◽  
L. Goddard
Keyword(s):  
Spatial Patterns ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Wheat Yield ◽  
Severe Drought ◽  
Seasonal Forecasts ◽  
El Niño Events ◽  
Australian Wheat ◽  
El Nino Events

Abstract The El Niño–Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Niño events are often associated with severe drought conditions. However, El Niño events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Niño are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (“footprints”) found in the El Niño impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean–atmosphere dynamics identifies three types of El Niño differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Niño events on agricultural systems.

scholarly journals Correction of atmospheric dynamical seasonal forecasts using the leading ocean-forced spatial patterns

2005 ◽  
Vol 32 (14) ◽  
pp. n/a-n/a ◽  
Author(s):  
Hai Lin ◽  
Jacques Derome ◽  
Gilbert Brunet
Keyword(s):  
Spatial Patterns ◽  
Seasonal Forecasts

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 Spatial Patterns of Global Precipitation Change and Variability during 1901–2010

2015 ◽  
Vol 28 (11) ◽  
pp. 4431-4453 ◽  
Author(s):  
Guojun Gu ◽  
Robert F. Adler
Keyword(s):  
Time Scales ◽  
Spatial Patterns ◽  
Decadal Variability ◽  
Precipitation Change ◽  
Past Century ◽  
Spatial Features ◽  
Precipitation Analysis ◽  
Precipitation Changes ◽  
Global Precipitation

Abstract This study examines global precipitation changes/variations during 1901–2010 by using the long-record GPCC land precipitation analysis, the NOAA/Cooperative Institute for Climate and Satellites (CICS) reconstructed (RECONS) precipitation analysis, and the CMIP5 outputs. In particular, spatial features of long-term precipitation changes and trends and decadal/interdecadal precipitation variations are explored by focusing on the effects of various physical mechanisms such as the anthropogenic greenhouse gas (GHG) and aerosol forcings and certain internal oscillations including the Pacific decadal variability (PDV) and Atlantic multidecadal oscillation (AMO). Precipitation increases in the Northern Hemisphere (NH) mid- to high-latitude lands observed in GPCC can also be found in RECONS and model simulations. Over tropical/subtropical land areas, precipitation reductions generally appear in all products, but with large discrepancies on regional scales. Over ocean, consistent spatial structures of precipitation change also exist between RECONS and models. It is further found that these long-term changes/trends might be due to both anthropogenic GHG and aerosols. The aerosol effect estimated from CMIP5 historical simulations is then removed from the GPCC, RECONS, and AMIP simulations. These isolated GHG-related changes/trends have many similar spatial features when compared to the CMIP5 GHG-only simulations, especially in the zonal-mean context. Both PDV and AMO have influence on spatial patterns of precipitation variations during the past century. In the NH middle to high latitudes, PDV and AMO have played an important role on interdecadal/multidecadal time scales. In several tropical/subtropical regions, their impacts may even become dominant for certain time spans including the recent past two decades. Therefore, these two internal mechanisms make the estimations of GHG and aerosol effects on precipitation on decadal/interdecadal time scales very challenging, especially on regional scales.

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.

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