scholarly journals Variations in the Peczely Macro-Synoptic Types (1881–2020) with Attention to Weather Extremes in the Pannonian Basin

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1071
Author(s):  
János Mika ◽  
Csaba Károssy ◽  
László Lakatos
Keyword(s):  
Climate Models ◽  
Pannonian Basin ◽  
Weather Extremes ◽  
Circulation Types ◽  
Temperature Trends ◽  
Grid Points ◽  
High Precipitation ◽  
Almost All ◽  
Physical Reasons ◽  
Local Weather

Daily Peczely circulation types are investigated over 140 years (1881–2020). After presenting monthly mean frequencies and durations of the 13 circulation types, two further questions are investigated: (i) How do the circulation types influence local weather extremes?; (ii) Are there significant trends in the frequency of the original and the grouped circulation types in the recent monotonically warming 50 year period (1971–2020)? The answers are as follows: (i) Four local weather extremes were investigated in nine grid-points of the Pannonian Basin and analyzed in the central months of the seasons. It was established that high precipitation and wind maxima occur in almost all circulation types and months, whereas for both high temperature maxima and low temperature minima, there are six circulation types, where no extremity occurred in one, two, or three investigated months. (ii) In the last 50 years, 37% of the linear seasonal frequency trends have been significant. However, these trends are rarely significant in the shorter monotonously warming (1911–1940) and cooling (1941–1970) 30-year periods. Therefore, the significant trends of the last 50 years are unlikely to be the direct consequences of the monotonous hemispherical warming. Since these hemispherical temperature trends are most likely caused by different sets of physical reasons, the reality of the presented circulation frequency trends needs to be validated by climate models.

scholarly journals Extreme associated functions: optimally linking local extremes to large-scale atmospheric circulation structures

2007 ◽  
Vol 7 (5) ◽  
pp. 14433-14460 ◽  
Author(s):  
D. Panja ◽  
F. M. Selten
Keyword(s):  
Time Series ◽  
The Netherlands ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Weather Extremes ◽  
Pressure System ◽  
Reanalysis Dataset ◽  
Local Weather ◽  
Large Scale Atmospheric Circulation

Abstract. We present a new statistical method to optimally link local weather extremes to large-scale atmospheric circulation structures. The method is illustrated using July–August daily mean temperature at 2 m height (T2m) time-series over the Netherlands and 500 hPa geopotential height (Z500) time-series over the Euroatlantic region of the ECMWF reanalysis dataset (ERA40). The method identifies patterns in the Z500 time-series that optimally describe, in a precise mathematical sense, the relationship with local warm extremes in the Netherlands. Two patterns are identified; the most important one corresponds to a blocking high pressure system leading to subsidence and calm, dry and sunny conditions over the Netherlands. The second one corresponds to a rare, easterly flow regime bringing warm, dry air into the region. The patterns are robust; they are also identified in shorter subsamples of the total dataset. The method is generally applicable and might prove useful in evaluating the performance of climate models in simulating local weather extremes.

Analog Models for Empirical-Statistical Downscaling

2021 ◽  
Author(s):  
María Laura Bettolli
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Weather Conditions ◽  
Climate Information ◽  
Analog Method ◽  
Weather Typing ◽  
Regional Climates ◽  
Local Weather

Global climate models (GCM) are fundamental tools for weather forecasting and climate predictions at different time scales, from intraseasonal prediction to climate change projections. Their design allows GCMs to simulate the global climate adequately, but they are not able to skillfully simulate local/regional climates. Consequently, downscaling and bias correction methods are increasingly needed and applied for generating useful local and regional climate information from the coarse GCM resolution. Empirical-statistical downscaling (ESD) methods generate climate information at the local scale or with a greater resolution than that achieved by GCM by means of empirical or statistical relationships between large-scale atmospheric variables and the local observed climate. As a counterpart approach, dynamical downscaling is based on regional climate models that simulate regional climate processes with a greater spatial resolution, using GCM fields as initial or boundary conditions. Various ESD methods can be classified according to different criteria, depending on their approach, implementation, and application. In general terms, ESD methods can be categorized into subgroups that include transfer functions or regression models (either linear or nonlinear), weather generators, and weather typing methods and analogs. Although these methods can be grouped into different categories, they can also be combined to generate more sophisticated downscaling methods. In the last group, weather typing and analogs, the methods relate the occurrence of particular weather classes to local and regional weather conditions. In particular, the analog method is based on finding atmospheric states in the historical record that are similar to the atmospheric state on a given target day. Then, the corresponding historical local weather conditions are used to estimate local weather conditions on the target day. The analog method is a relatively simple technique that has been extensively used as a benchmark method in statistical downscaling applications. Of easy construction and applicability to any predictand variable, it has shown to perform as well as other more sophisticated methods. These attributes have inspired its application in diverse studies around the world that explore its ability to simulate different characteristics of regional climates.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

2020 ◽  
Author(s):  
Anja Katzenberger ◽  
Jacob Schewe ◽  
Julia Pongratz ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Models ◽  
Monsoon Rainfall ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Global Mean Temperature ◽  
Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

Can climate models capture the high amplitudes circumglobal waves and their surface imprints?  

2021 ◽  
Author(s):  
Fei Luo ◽  
Kai Kornhuber ◽  
Frank Selten ◽  
Dim Coumou
Keyword(s):  
Soil Moisture ◽  
Climate Models ◽  
Phase Locking ◽  
Weather Conditions ◽  
Reanalysis Data ◽  
Circulation Types ◽  
Temperature And Precipitation ◽  
Wave Patterns ◽  
Precipitation Anomalies ◽  
June July

<p>Pronounced circumglobal waves can trigger and maintain persistent summer weather conditions by remaining in their preferred phase-locked positions for several weeks in a row. This phenomenon, especially important for wave numbers 5 and 7, has been observed in recent years, but it is unclear whether climate models can reproduce circulation types and their surface imprints.</p><p>Here we assess three climate models (EC-Earth3, CESM1.2, and MIROC5)  for their representation of amplified circumglobal waves and associated surface imprints in summer (June, July and August) over 1979-2016. ERA5 reanalysis data is used as reference to assess the models’ performance. We run a series of modeling experiments to understand the source of biases in the climate models: free interactive atmosphere and soil moisture runs (AISI), atmospheric nudged runs (AFSI), soil moisture prescribed runs (AISF), and both atmosphere and soil moisture nudged experiments (AFSF).</p><p>We show that all models reasonably well reproduce the climatological wave spectra. Further, both wave 5 and wave 7 are found to exhibit phase-locking behaviors across all models, resulting in similar wave patterns across the hemisphere as compared to reanalysis. The surface imprints are observed in the models as well, but depending on the model, the results vary in strength. We also found the biases in surface temperature and precipitation anomalies mainly come from the atmospheric circulation in the models as these biases reduced considerably from AISI runs to AFSI and AFSF runs where upper atmosphere levels were nudged. Nudging soil moisture also minimizes some biases in the models but not as obvious as nudging the atmosphere. </p><div> <div> <div> </div> </div> </div>

scholarly journals The Response of Precipitation Minus Evapotranspiration to Climate Warming: Why the “Wet-Get-Wetter, Dry-Get-Drier” Scaling Does Not Hold over Land*

2015 ◽  
Vol 28 (20) ◽  
pp. 8078-8092 ◽  
Author(s):  
Michael P. Byrne ◽  
Paul A. O’Gorman
Keyword(s):  
Global Warming ◽  
Relative Humidity ◽  
Climate Warming ◽  
Climate Models ◽  
Atmospheric Moisture ◽  
Horizontal Gradients ◽  
Polar Amplification ◽  
Simple Scaling ◽  
Almost All ◽  
Global Mean

Abstract Simulations with climate models show a land–ocean contrast in the response of P − E (precipitation minus evaporation or evapotranspiration) to global warming, with larger changes over ocean than over land. The changes over ocean broadly follow a simple thermodynamic scaling of the atmospheric moisture convergence: the so-called “wet-get-wetter, dry-get-drier” mechanism. Over land, however, the simple scaling fails to give any regions with decreases in P − E, and it overestimates increases in P − E compared to the simulations. Changes in circulation cause deviations from the simple scaling, but they are not sufficient to explain this systematic moist bias. It is shown here that horizontal gradients of changes in temperature and fractional changes in relative humidity, not accounted for in the simple scaling, are important over land and high-latitude oceans. An extended scaling that incorporates these gradients is shown to better capture the response of P − E over land, including a smaller increase in global-mean runoff and several regions with decreases in P − E. In the zonal mean over land, the gradient terms lead to a robust drying tendency at almost all latitudes. This drying tendency is shown to relate, in part, to the polar amplification of warming in the Northern Hemisphere, and to the amplified warming over continental interiors and on the eastern side of midlatitude continents.

scholarly journals Mid-to-late Holocene Temperature Evolution and Atmospheric Dynamics over Europe in Regional Model Simulations

2016 ◽  
Author(s):  
Emmanuele Russo ◽  
Ulrich Cubasch
Keyword(s):  
Late Holocene ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climatic Conditions ◽  
Modes Of Variability ◽  
The North ◽  
Climate Reconstructions ◽  
The North Atlantic Oscillation ◽  
Almost All

Abstract. The improvement in resolution of climate models is always been mentioned as one of the most important factors when investigating past climatic conditions especially in order to evaluate and compare the results against proxy data. In this paper we present for the first time a set of high resolution simulations for different time slices of mid-to-late Holocene performed over Europe using a Regional Climate Model. Through a validation against a new pollen-based climate reconstructions dataset, covering almost all of Europe, we test the model performances for paleoclimatic applications and investigate the response of temperature to variations in the seasonal cycle of insolation, with the aim of clarifying earlier debated uncertainties, giving physically plausible interpretations of both the pollen data and the model results. The results reinforce previous findings showing that summertime temperatures were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. In winter, instead, the model does not reproduce correctly the same amplitude of changes, even if it captures the main pattern of the pollen dataset over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though in some areas the discrepancies between the two datasets are most likely due to high pollen uncertanties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability

scholarly journals Paleobotanical proxies for early Eocene climates and ecosystems in northern North America from middle to high latitudes

2020 ◽  
Vol 16 (4) ◽  
pp. 1387-1410 ◽  
Author(s):  
Christopher K. West ◽  
David R. Greenwood ◽  
Tammo Reichgelt ◽  
Alexander J. Lowe ◽  
Janelle M. Vachon ◽  
...  
Keyword(s):  
North America ◽  
Climate Models ◽  
Hydrological Cycle ◽  
The Arctic ◽  
Early Eocene ◽  
High Latitudes ◽  
Early Eocene Climatic Optimum ◽  
Northern Half ◽  
Thermal Maximum ◽  
High Precipitation

Abstract. Early Eocene climates were globally warm, with ice-free conditions at both poles. Early Eocene polar landmasses supported extensive forest ecosystems of a primarily temperate biota but also with abundant thermophilic elements, such as crocodilians, and mesothermic taxodioid conifers and angiosperms. The globally warm early Eocene was punctuated by geologically brief hyperthermals such as the Paleocene–Eocene Thermal Maximum (PETM), culminating in the Early Eocene Climatic Optimum (EECO), during which the range of thermophilic plants such as palms extended into the Arctic. Climate models have struggled to reproduce early Eocene Arctic warm winters and high precipitation, with models invoking a variety of mechanisms, from atmospheric CO2 levels that are unsupported by proxy evidence to the role of an enhanced hydrological cycle, to reproduce winters that experienced no direct solar energy input yet remained wet and above freezing. Here, we provide new estimates of climate and compile existing paleobotanical proxy data for upland and lowland midlatitude sites in British Columbia, Canada, and northern Washington, USA, and from high-latitude lowland sites in Alaska and the Canadian Arctic to compare climatic regimes between the middle and high latitudes of the early Eocene – spanning the PETM to the EECO – in the northern half of North America. In addition, these data are used to reevaluate the latitudinal temperature gradient in North America during the early Eocene and to provide refined biome interpretations of these ancient forests based on climate and physiognomic data.

scholarly journals Brief communication: Impact of common ice mask in surface mass balance estimates over the Antarctic ice sheet

2021 ◽  
Author(s):  
Nicolaj Hansen ◽  
Sebastian Bjerregaard Simonsen ◽  
Fredrik Boberg ◽  
Christoph Kittel ◽  
Andrew Orr ◽  
...  
Keyword(s):  
Mass Balance ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Regional Climate Models ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Glacier Ice ◽  
High Precipitation ◽  
The Antarctic

Abstract. Regional climate models compute ice sheet surface mass balance (SMB) over a mask that defines the area covered by glacier ice, but ice masks have not been harmonised between models. Intercomparison studies of modelled SMB therefore use a common ice mask. The SMB in areas outside the common ice mask, which are typically coastal and high precipitation regions, are discarded. Ice mask differences change integrated SMB by between 40.5 to 140.6 Gt yr−1, (1.8 % to 6.0 % of ensemble mean SMB), equivalent to the entire Antarctic mass imbalance. We conclude there is a pressing need for a common ice mask protocol.

scholarly journals Paleobotanical proxies for early Eocene climates and ecosystems in northern North America from mid to high latitudes

2020 ◽  
Author(s):  
Christopher K. West ◽  
David R. Greenwood ◽  
Tammo Reichgelt ◽  
Alex J. Lowe ◽  
Janelle M. Vachon ◽  
...  
Keyword(s):  
North America ◽  
Climate Models ◽  
Hydrological Cycle ◽  
The Arctic ◽  
Early Eocene ◽  
High Latitudes ◽  
Early Eocene Climatic Optimum ◽  
Northern Half ◽  
Thermal Maximum ◽  
High Precipitation

Abstract. Early Eocene climates were globally warm, with ice-free conditions at both poles. Early Eocene polar landmasses supported extensive forest ecosystems of a primarily temperate biota, but also with abundant thermophilic elements such as crocodilians, and mesothermic taxodioid conifers and angiosperms. The globally warm early Eocene was punctuated by geologically brief hyperthermals such as the Paleocene-Eocene Thermal Maximum (PETM), culminating in the Early Eocene Climatic Optimum (EECO), during which the range of thermophilic plants such as palms extended into the Arctic. Climate models have struggled to reproduce early Eocene Arctic warm winters and high precipitation, with models invoking a variety of mechanisms, from atmospheric CO2 levels that are unsupported by proxy evidence, to the role of an enhanced hydrological cycle to reproduce winters that experienced no direct solar energy input yet remained wet and above freezing. Here, we provide new estimates of climate, and compile existing paleobotanical proxy data for upland and lowland mid-latitudes sites in British Columbia, Canada, and northern Washington, USA, and from high-latitude lowland sites in Alaska and the Canadian Arctic to compare climatic regimes between mid- and high latitudes of the early Eocene – spanning the PETM to the EECO – of the northern half of North America. In addition, these data are used to reevaluate the latitudinal temperate gradient in North America during the early Eocene, and to provide refined biome interpretations of these ancient forests based on climate and physiognomic data.

scholarly journals Machine learning prediction of the Madden-Julian oscillation

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Riccardo Silini ◽  
Marcelo Barreiro ◽  
Cristina Masoller
Keyword(s):  
Neural Network ◽  
Extreme Events ◽  
Time Scale ◽  
Climate Models ◽  
Dominant Mode ◽  
Prediction Skill ◽  
Madden Julian Oscillation ◽  
Weather Extremes ◽  
Feed Forward Neural Network ◽  
Good Ability

AbstractThe socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding state-of-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated.

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