Short-Term Climate Extremes: Prediction Skill and Predictability

2013 ◽  
Vol 26 (2) ◽  
pp. 512-531 ◽  
Author(s):  
Emily J. Becker ◽  
Huug van den Dool ◽  
Malaquias Peña
Keyword(s):  
North America ◽  
South America ◽  
Surface Temperature ◽  
Global Climate Model ◽  
Prediction Skill ◽  
Short Term ◽  
Potential Predictability ◽  
Atlantic Hurricane ◽  
Temperature And Precipitation ◽  
Main Development

Abstract Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables. The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

Assessing Predictive Potential Associated with the MJO during the Boreal Winter

2020 ◽  
Vol 148 (12) ◽  
pp. 4957-4969
Author(s):  
Arun Kumar ◽  
Jieshun Zhu ◽  
Wanqiu Wang
Keyword(s):  
Surface Temperature ◽  
Observational Data ◽  
Prediction Skill ◽  
Meteorological Variables ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Potential Predictability ◽  
Upper Limit ◽  
Prediction Systems ◽  
A Chain

AbstractIn this paper, the question of potential predictability in meteorological variables associated with skillful prediction of the Madden–Julian oscillation (MJO) during boreal winter is analyzed. The analysis is motivated by the fact that dynamical prediction systems are now capable of predicting MJO up to 30 days or earlier (measured in terms of anomaly correlation for RMM indices). Translating recent gains in MJO prediction skill and relating them back to potential for predicting meteorological variables—for example, precipitation and surface temperature—is not straightforward because of a chain of steps that go into the computation and evaluation of RMM indices. This paper assesses potential predictability in meteorological variables that could be attributed to skillful prediction of the MJO. The analysis is based on the observational data alone and assesses the upper limit of MJO-associated predictability that could be achieved.

scholarly journals Detection of non‐climatic biases in land surface temperature records by comparing climatic data and their model simulations

2021 ◽  
Author(s):  
Nicola Scafetta
Keyword(s):  
Global Warming ◽  
North America ◽  
South America ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Temperature ◽  
Sea Surface ◽  
Temperature Record ◽  
The World ◽  
Temperature Records

AbstractThe 0.6 °C warming observed in global temperature datasets from 1940 to 1960 to 2000–2020 can be partially due to urban heat island (UHI) and other non-climatic biases in the underlying data, although several previous studies have argued to the contrary. Here we identify land regions where such biases could be present by locally evaluating their diurnal temperature range (DTR = TMax − TMin trends between the decades 1945–1954 and 2005–2014 and between the decades 1951–1960 and 1991–2000 versus their synthetic hindcasts produced by the CMIP5 models. Vast regions of Asia (in particular Russia and China) and North America, a significant part of Europe, part of Oceania, and relatively small parts of South America (in particular Colombia and Venezuela) and Africa show DTR reductions up to 0.5–1.5 °C larger than the hindcasted ones, mostly where fast urbanization has occurred, such as in central-east China. Besides, it is found: (1) from May to October, TMax globally warmed 40% less than the hindcast; (2) in Greenland, which appears nearly free of any non-climatic contamination, TMean warmed about 50% less than the hindcast; (3) the world macro-regions with, on average, the lowest DTR reductions and with low urbanization (60S-30N:120 W–90 E and 60 S–10 N:90 E–180 E: Central and South America, Africa, and Oceania) warmed about 20–30% less than the models’ hindcast. Yet, the world macro-region with, on average, the largest DTR reductions and with high urbanization (30 N–80 N:180 W–180 E: most of North America, Europe, and Central Asia) warmed just a little bit more (5%) than the hindcast, which indicates that the models well agree only with potentially problematic temperature records. Indeed, also tree-based proxy temperature reconstructions covering the 30°N–70°N land area produce significantly less warming than the correspondent instrumentally-based temperature record since 1980. Finally, we compare land and sea surface temperature data versus their CMIP5 simulations and find that 25–45% of the 1 °C land warming from 1940–1960 to 2000–2020 could be due to non-climatic biases. By merging the sea surface temperature record (assumed to be correct) and an adjusted land temperature record based on the model prediction, the global warming during the same period is found to be 15–25% lower than reported. The corrected warming is compatible with that shown by the satellite UAH MSU v6.0 low troposphere global temperature record since 1979. Implications for climate model evaluation and future global warming estimates are briefly addressed.

scholarly journals Distinct Patterns of Tropical Pacific SST Anomaly and Their Impacts on North American Climate

2017 ◽  
Vol 30 (14) ◽  
pp. 5221-5241 ◽  
Author(s):  
Yuanyuan Guo ◽  
Mingfang Ting ◽  
Zhiping Wen ◽  
Dong Eun Lee
Keyword(s):  
North America ◽  
Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
La Niña ◽  
Cluster Technique ◽  
Temperature And Precipitation ◽  
La Nina ◽  
Sst Anomaly ◽  
The Impact

A neural-network-based cluster technique, the so-called self-organizing map (SOM), was performed to extract distinct sea surface temperature (SST) anomaly patterns during boreal winter. The SOM technique has advantages in nonlinear feature extraction compared to the commonly used empirical orthogonal function analysis and is widely used in meteorology. The eight distinguishable SOM patterns so identified represent three La Niña–like patterns, two near-normal patterns, and three El Niño–like patterns. These patterns show the varied amplitude and location of the SST anomalies associated with El Niño and La Niña, such as the central Pacific (CP) and eastern Pacific (EP) El Niño. The impact of each distinctive SOM pattern on winter-mean surface temperature and precipitation changes over North America was examined. Based on composite maps with observational data, each SOM pattern corresponds to a distinguishable spatial structure of temperature and precipitation anomaly over North America, which seems to result from differing wave train patterns, extending from the tropics to mid–high latitudes induced by longitudinally shifted tropical heating. The corresponding teleconnection as represented by the National Center for Atmospheric Research Community Atmospheric Model, version 4 (CAM4), was compared with the observational results. It was found that the 16-member ensemble average of the CAM4 experiments with prescribed SST can reproduce the observed atmospheric circulation responses to the different SST SOM patterns, which suggests that the circulation differences are largely SST driven rather than due to internal atmospheric variability.

Effects of insecticides on adults and eggs of Drosophila suzukii (Diptera, Drosophilidae)

2017 ◽  
Vol 43 (2) ◽  
pp. 208 ◽  
Author(s):  
Daniele Cristine Hoffmann Schlesener ◽  
Jutiane Wollmann ◽  
Juliano De Bastos Pazini ◽  
Anderson Dionei Grützmacher ◽  
Flávio Roberto Mello Garcia
Keyword(s):  
North America ◽  
South America ◽  
Chemical Control ◽  
Stone Fruits ◽  
Drosophila Suzukii ◽  
Ovicidal Effect ◽  
Mere Presence ◽  
South Of Brazil ◽  
Different Cultures ◽  
First Time

Drosophila suzukii (Diptera, Drosophilidae) is an exotic species, endemic to Asia and currently a pest to small and stone fruits in several countries of North America and Europe. It was detected in 2013 for the first time in South America, in the south of Brazil. Unlike most drosophilids, this species deserves special attention, because the females are capable of oviposit inside healthy fruits, rendering their sale and export prohibited. Despite the confirmed existence of this species in different states of Brazil, this insect is yet been to be given the pest status. Nevertheless, the mere presence of this species is enough to cause concern to producers of small fruits and to justify further investigation for it’s control, especially chemical control for a possible change in status. Therefore, the goal of this work was to evaluate, in laboratory, mortality of D. suzukii adults and ovicidal effect when exposed to different insecticides registered for species of the Tephritidae and Agromyzidae families in different cultures. The insecticides deltamethrin, dimethoate, spinosad, fenitrothion, phosmet, malathion, methidathion, and zeta-cypermethrin resulted in mortality to 100 % of the subjects three days after the treatment (DAT). Regarding the effects over eggs, it was  established that the insecticides fenitrothion, malathion, and methidathion deemed 100 % of the eggs not viable, followed by phosmet and diflubenzuron, which also caused elevated reduction in the eclosion of larvae two DAT.

scholarly journals Deep learning for bias correction of MJO prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
H. Kim ◽  
Y. G. Ham ◽  
Y. S. Joo ◽  
S. W. Son
Keyword(s):  
Deep Learning ◽  
Bias Correction ◽  
Numerical Models ◽  
Weather Prediction ◽  
Primary Source ◽  
Correction Method ◽  
Prediction Skill ◽  
Convective System ◽  
Forecast Errors ◽  
Potential Predictability

AbstractProducing accurate weather prediction beyond two weeks is an urgent challenge due to its ever-increasing socioeconomic value. The Madden-Julian Oscillation (MJO), a planetary-scale tropical convective system, serves as a primary source of global subseasonal (i.e., targeting three to four weeks) predictability. During the past decades, operational forecasting systems have improved substantially, while the MJO prediction skill has not yet reached its potential predictability, partly due to the systematic errors caused by imperfect numerical models. Here, to improve the MJO prediction skill, we blend the state-of-the-art dynamical forecasts and observations with a Deep Learning bias correction method. With Deep Learning bias correction, multi-model forecast errors in MJO amplitude and phase averaged over four weeks are significantly reduced by about 90% and 77%, respectively. Most models show the greatest improvement for MJO events starting from the Indian Ocean and crossing the Maritime Continent.

scholarly journals Projected Changes in Extreme Temperature and Precipitation Indices Over CORDEX-MENA Domain

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 622
Author(s):  
Tugba Ozturk ◽  
F. Sibel Saygili-Araci ◽  
M. Levent Kurnaz
Keyword(s):  
Regional Climate Model ◽  
Radiative Forcing ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Climate Extreme ◽  
Pronounced Increase ◽  
Temperature And Precipitation ◽  
Projected Changes ◽  
Extreme Indices

In this study, projected changes in climate extreme indices defined by the Expert Team on Climate Change Detection and Indices were investigated over Middle East and North Africa. Changes in the daily maximum and minimum temperature- and precipitation- based extreme indices were analyzed for the end of the 21st century compared to the reference period 1971–2000 using regional climate model simulations. Regional climate model, RegCM4.4 was used to downscale two different global climate model outputs to 50 km resolution under RCP4.5 and RCP8.5 scenarios. Results generally indicate an intensification of temperature- and precipitation- based extreme indices with increasing radiative forcing. In particular, an increase in annual minimum of daily minimum temperatures is more pronounced over the northern part of Mediterranean Basin and tropics. High increase in warm nights and warm spell duration all over the region with a pronounced increase in tropics are projected for the period of 2071–2100 together with decrease or no change in cold extremes. According to the results, a decrease in total wet-day precipitation and increase in dry spells are expected for the end of the century.

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