Statistical prediction of 20th century European summer temperatures based on ERA20c reanalysis data

2020 ◽  
Author(s):  
Maria Pyrina ◽  
Sebastian Wagner ◽  
Eduardo Zorita
Keyword(s):  
Statistical Modeling ◽  
North Atlantic ◽  
Regression Models ◽  
Reanalysis Data ◽  
Statistical Prediction ◽  
Predictor Variables ◽  
Ensemble Prediction ◽  
Linear Regression Models ◽  
Seasonal Forecasts ◽  
Study Region

<p>An alternative to dynamical seasonal prediction of European climate is statistical modeling. Statistical modeling is an appealing and computationally effective approach for producing seasonal forecasts by exploiting the physical connections between the predictand variable and the predictors. We assess the seasonal predictability of summer European 2m temperature (T2m) using canonical correlation analysis. Seasonal means of spring Soil Moisture (SM), Sea Level Pressure (SLP) and Sea Surface Temperature (SST) are used as predictors of mean summer T2m. For SSTs, we test the potential predictability of T2m using three different regions. These regions include what we define as: Extratropical North Atlantic (ENA), Tropical North Atlantic (TNA), and North Atlantic (NA). The predictability is explored in the ERA20c reanalysis and in comprehensive Earth System Model (ESM) fields. The results are provided for the European domain on a horizontal grid of 1°x1° degrees.</p><p>In order to identify the local T2m predictability related to the different predictor variables, we first built Univariate Linear Regression models, one for every predictor. The regression models are calibrated and validated during 1902-1950 and a prediction is provided for the periods 1951-1998, 1951-2004, and 1951-2008, respectively. The resulting correlation maps between the original and the predicted T2m anomalies showed that for the predictor variables SLP, SM, and SST<sub>ENA</sub> the results of the experiments using ESM data share similar T2m predictability patterns with the results of the experiments using reanalysis data. Most prominent disagreements between the predictability patterns resulting from ESMs and from ERA20c refers to the T2m prediction that utilizes tropical SSTs. SM is identified as the most important predictor for the summer European temperature predictability.</p><p>The ERA20c data show that the SM predictor field can be used for the T2m prediction over most of our study region west of 15° E and that the ENA SSTs can be used for the prediction over Europe east of 15° E. The resulting gridded correlation coefficients vary between 0.3 and 0.5. These results are not sensitive to the prediction period and to the number of Canonical Coefficients used in the regression model. Our approach complements existing numerical seasonal forecast frameworks and can be implemented for ensemble prediction studies.</p>

scholarly journals Impact of Eurasian autumn snow on the winter North Atlantic Oscillation in seasonal forecasts of the 20<sup>th</sup> century

2021 ◽  
Author(s):  
Martin Wegmann ◽  
Yvan Orsolini ◽  
Antje Weisheimer ◽  
Bart van den Hurk ◽  
Gerrit Lohmann
Keyword(s):  
North Atlantic Oscillation ◽  
Snow Cover ◽  
North Atlantic ◽  
Climate Model ◽  
Winter Season ◽  
Reanalysis Data ◽  
Causal Connection ◽  
Seasonal Forecasts ◽  
The North ◽  
The North Atlantic Oscillation

Abstract. As the leading climate mode of wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Eurasian cryosphere as a possible predictor for the wintertime NAO. However, missing correlation between snow cover and wintertime NAO in climate model experiments and strong non-stationarity of this link in reanalysis data is questioning the causality of this relationship. Here we use the large ensemble of Atmospheric Seasonal Forecasts of the 20th Century (ASF-20C) with the European Centre for Medium-Range Weather Forecasts model, focusing on the winter season. Besides the main 110-year ensemble of 51 members, we investigate a second, perturbed ensemble of 21 members where initial (November) land conditions over the Northern Hemisphere are swapped from neighboring years. The Eurasian snow/NAO linkage is examined in terms of a longitudinal snow depth dipole across Eurasia. Subsampling the perturbed forecast ensemble and contrasting members with high and low initial snow dipole conditions, we found that their composite difference indicates more negative NAO states in the following winter (DJF) after positive west to east snow cover gradients at the beginning of November. Surface and atmospheric forecast anomalies through the troposphere and stratosphere associated with the anomalous positive snow dipole consist of colder early winter surface temperatures over Eastern Eurasia, an enhanced Ural ridge and increased vertical energy fluxes into the stratosphere, with a subsequent negative NAO-like signature in the troposphere. We thus confirm the existence of a causal connection between autumn snow patterns and subsequent winter circulation in the ASF-20C forecasting system.

scholarly journals Impact of Eurasian autumn snow on the winter North Atlantic Oscillation in seasonal forecasts of the 20th century

2021 ◽  
Vol 2 (4) ◽  
pp. 1245-1261
Author(s):  
Martin Wegmann ◽  
Yvan Orsolini ◽  
Antje Weisheimer ◽  
Bart van den Hurk ◽  
Gerrit Lohmann
Keyword(s):  
North Atlantic Oscillation ◽  
20Th Century ◽  
North Atlantic ◽  
Snow Depth ◽  
Climate Model ◽  
Winter Season ◽  
Reanalysis Data ◽  
Causal Connection ◽  
Seasonal Forecasts ◽  
The North

Abstract. As the leading climate mode of wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Eurasian cryosphere as a possible predictor for the wintertime NAO. However, missing correlation between snow cover and wintertime NAO in climate model experiments and strong non-stationarity of this link in reanalysis data are questioning the causality of this relationship. Here we use the large ensemble of Atmospheric Seasonal Forecasts of the 20th Century (ASF-20C) with the European Centre for Medium-Range Weather Forecasts model, focusing on the winter season. Besides the main 110-year ensemble of 51 members, we investigate a second, perturbed ensemble of 21 members where initial (November) land conditions over the Northern Hemisphere are swapped from neighboring years. The Eurasian snow–NAO linkage is examined in terms of a longitudinal snow depth dipole across Eurasia. Subsampling the perturbed forecast ensemble and contrasting members with high and low initial snow dipole conditions, we found that their composite difference indicates more negative NAO states in the following winter (DJF) after positive west-to-east snow depth gradients at the beginning of November. Surface and atmospheric forecast anomalies through the troposphere and stratosphere associated with the anomalous positive snow dipole consist of colder early winter surface temperatures over eastern Eurasia, an enhanced Ural ridge and increased vertical energy fluxes into the stratosphere, with a subsequent negative NAO-like signature in the troposphere. We thus confirm the existence of a causal connection between autumn snow patterns and subsequent winter circulation in the ASF-20C forecasting system.

scholarly journals Statistical prediction of Sri Lankan rainfall during October to December

MAUSAM ◽  
2021 ◽  
Vol 71 (3) ◽  
pp. 491-502
Author(s):  
KARUNAPALA PABODINI ◽  
YOO CHANGHYUN
Keyword(s):  
Sri Lanka ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Partial Least Square ◽  
Statistical Prediction ◽  
Lead Times ◽  
Sri Lankan ◽  
Linear Regression Models ◽  
Multiple Linear Regression Models

Sri Lanka receives most rainfall during October to December (OND). Here we construct multiple linear regression models to forecast the OND Sri Lankan rainfall during 1979-2012 for lead times of 1 and 2 months. Correlation analysis was used to examine the relationship between Sri Lankan OND rainfall and global sea surface temperature (SST) anomalies. Three independent predictors were identified through partial least square regression method which includes the southern Atlantic SST tendency, southern Pacific SST tendency and western Pacific and Maritime Continent SST tendency at two different lead times. Three-year-out cross validation concludes that the multiple linear regression models can produce forecast the OND rainfall forecast at correlation coefficient skill of 0.69 and 0.68 for the 1 and 2 month lead times respectively. The physical processes associated with these three predictors show that they contribute to increase in OND rainfall of Sri Lanka.

scholarly journals Prognóstico Sazonal de Precipitação Regionalizada na Amazônia Oriental (Seasonal prognostic of regionalized precipitation in Eastern Amazon)

2017 ◽  
Vol 10 (5) ◽  
pp. 1520
Author(s):  
Franklin Thiago Mota De Azevedo ◽  
Everaldo Barreiros De Souza ◽  
Vania Dos Santos Franco ◽  
Paulo Fernando De Souza Souza
Keyword(s):  
Cluster Analysis ◽  
Linear Regression ◽  
Regression Models ◽  
Sea Surface ◽  
Linear Regression Models ◽  
Seasonal Forecasts ◽  
Multiple Regression Models ◽  
Homogeneous Regions ◽  
Period Of Occurrence ◽  
Better Than

O foco deste trabalho é na variabilidade espacial e temporal da precipitação na região da Amazônia oriental (estados do Pará, Maranhão e Tocantins) no período de 1982 a 2015. A aplicação da análise de agrupamento permitiu a identificação de quatro regiões homogêneas de precipitação sobre a região, sendo que o período mais chuvoso (CHU+) e menos chuvoso (CHU-) ocorrem de forma diferenciada ao longo dos meses do ano, caracterizando a variabilidade pronunciada sobre a região. As áreas-chave dos padrões de temperatura da superfície do mar (TSM) sobre os Oceanos Pacífico e Atlântico foram selecionadas através de correlações significantes, com a finalidade de montar os modelos de regressão simples e múltipla para a geração das previsões sazonais de precipitação nas regiões homogêneas durante os regimes CHU+ e CHU-. Os resultados dos modelos indicaram que as previsões com lag0 (preditores de TSM no mesmo período de ocorrência dos regimes CHU+ e CHU-) são melhores do que as previsões defasadas (preditores de TSM no mês, bimestre e trimestre anterior ao regime CHU+ e CHU-), independente do modelo ser de regressão simples (apenas uma área de TSM) ou múltipla (duas, três ou quatro áreas de TSM).   A B S T R A C TThe focus of the present work is on time and spatial variability of precipitation in eastern Amazon (Pará, Maranhão and Tocantins states) during 1982 to 2015 period. The application of the cluster analysis allowed the identification of four homogeneous regions of precipitation over region, with the rainy (CHU+) and dry (CHU-) periods occuring in different ways throughout the months of the year, featuring the pronounced climate variability of the region. The key-areas of the patterns of sea surface temperature (SST) over Pacific and Atlantic Oceans were selected through significant correlations, in order to build simple and multiple regression models to generate seasonal forecasts of precipitation in regions homogeneous during the CHU + and CHU- regimes. The results of the models indicated that the lag0 predictions (SST predictors in the same period of occurrence of CHU + and CHU-) are better than the lagged forecasts (SST predictors of the month, two-months and three-months preceding the CHU + and CHU-), regardless of the model is simple (only one TSM area) or multiple (two, three or four areas of SST).Keywords: precipitation regionalization, Pacific and Atlantic SST, linear regression models.  

Verification of post-processed seasonal predictions

2020 ◽  
Author(s):  
André Düsterhus
Keyword(s):  
North Atlantic ◽  
Statistical Prediction ◽  
Ensemble Prediction ◽  
Time Step ◽  
Ensemble Forecasts ◽  
Continuous Ranked Probability Score ◽  
Time Period ◽  
Model Predictions ◽  
To Come ◽  
Quadratic Distance

&lt;p&gt;Traditionally, verification of (ensemble) model predictions is done by comparing them to deterministic observations, e.g. with scores like the Continuous Ranked Probability Score (CRPS). While these approaches allow uncertain predictions basing on ensemble forecasts, it is open how to verify them against observations with non-parametric uncertainties.&lt;/p&gt;&lt;p&gt;This contribution focuses on statistically post-processed seasonal predictions of the Winter North Atlantic Oscillation (WNAO). The post-processing procedure creates in a first step for a dynamical ensemble prediction and for a statistical prediction basing on predictors two separate probability density functions (pdf). Afterwards these two distributions are combined to create a new statistical-dynamical prediction, which has been proven to be advantageous compared to the purely dynamical prediction. It will be demonstrated how this combination and with it the improvement of the prediction can be achieved before the focus will be set on the evaluation of those predictions at the hand of uncertain observations. Two new scores basing on the Earth Mover's Distance (EMD) and the Integrated Quadratic Distance (IQD) will be introduced and compared before it is shown how they can be used to effectively evaluate probabilistic predictions with uncertain observations.&amp;#160;&lt;/p&gt;&lt;p&gt;Furthermore, a common approach (e.g. for correlation measures) is to compare predictions with observations over a longer time period. In this contribution a paradigm shift away from this approach towards comparing predictions for each single time step (like years) will be presented. This view give new insights into the performance of the predictions and allows to come to new understandings of the reasons for advantages or disadvantages of specific predictions.&amp;#160;&lt;/p&gt;

Lagrangian detection of moisture sources for an arid region in Northeast Greenland: relations to the North-Atlantic Oscillation and temporal trends from 1979 to 2017

2020 ◽  
Author(s):  
Lilian Schuster ◽  
Fabien Maussion ◽  
Lukas Langhamer ◽  
Gina E. Moseley
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Arid Region ◽  
North Atlantic Ocean ◽  
Temporal Trends ◽  
Reanalysis Data ◽  
The Arctic ◽  
Study Region ◽  
Moisture Source ◽  
Moisture Sources

&lt;p&gt;Northeast Greenland is predicted to be one of the most sensitive terrestrial areas of the Arctic to anthropogenic climate change, resulting in an increase in temperature that is much greater than the global average. Associated with this temperature rise, precipitation is also expected to increase as a result of increased evaporation from an ice-free Arctic Ocean. In recent years, numerous palaeoclimate projects have begun working in the region with the aim of improving our understanding of how this highly-sensitive region responds to a warmer world. However, a lack of meteorological stations within the area makes it difficult to place the palaeoclimate records in the context of modern climate.&lt;/p&gt;&lt;p&gt;This study aims to improve our understanding of precipitation and moisture source dynamics over a small arid region located at 80 &amp;#176;N in Northeast Greenland. This region hosts many speleothem-containing caves that are being studied in the framework of the Greenland Caves Project (greenlandcavesproject.org). The origin of water vapour for precipitation over the study site is detected by a Lagrangian moisture source diagnostic, which is applied to reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA-Interim) from 1979 to 2017.&lt;/p&gt;&lt;p&gt;While precipitation amounts are relatively constant during the year, the regional moisture sources display a strong seasonality. The most dominant winter moisture sources are the ice-free North Atlantic ocean above 45 &amp;#176;N, while in summer the patterns shift towards more local and North Eurasian continental sources. During positive North-Atlantic Oscillation (NAO) phases evaporation and moisture transport from the Norwegian Sea is stronger, resulting in larger and more variable precipitation amounts. Although the annual mean temperature in the study region has increased by 0.7 &amp;#176;C dec &lt;sup&gt;-1&lt;/sup&gt; (95% confidence interval [0.4, 1.0] &amp;#176;C dec &lt;sup&gt;-1&lt;/sup&gt; ) according to ERA-Interim data, we do not detect any change in the amount of precipitation with the exception of autumn where precipitation increases by 8.2 [0.8, 15.5] mm dec&amp;#160;&lt;sup&gt;-1&lt;/sup&gt; over the period. This increase is consistent with future predicted Arctic precipitation change.&lt;/p&gt;

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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