scholarly journals Spatial pattern recognition of extreme temperature climatology: assessing HadCM3 simulations via NCEP reanalyses over Europe

2007 ◽  
Vol 22 (2) ◽  
pp. 204-217 ◽  
Author(s):  
P. S. Lucio ◽  
F. C. Conde ◽  
A. M. Ramos
Keyword(s):  
Pattern Recognition ◽  
Spatial Pattern ◽  
Model Building ◽  
Climate Model ◽  
Extreme Values ◽  
Extreme Temperature ◽  
Spatial Models ◽  
Climate Extremes ◽  
The Usa ◽  
Hadley Centre

This article attempts to quantify the spatial uncertainties associated with extreme temperature’s response, by assessing data derived from climate model. This is undertaken by a comparison of the spatial pattern of a long-term time-series aggregation (1960/61-1989/90) for extreme temperatures simulated by a particular GCM (the UK Met Office - Hadley Centre climate model, HadCM3) to that of the USA NCAR NCEP Reanalyses, which are considered as ‘truth’, over the MICE (Modelling the Impacts of Climate Extremes - EU Project) spatial domain. Since evaluation of models is crucial to assessing future scenarios, the aim of this study is to investigate whether the extreme values predicted by the HadCM3 climate model can simulate those produced by NCEP Reanalyses, assuming that the extremes of both models are realizations of the same spatial stochastic process. To get more useful information about the uncertainties surrounding spatial climate projection, one also has to analyze the pattern of temperature extremes in terms of their anomalies. A common technical issue in the assessment of numerical spatial models is based on the Principal Components Analysis and Bayesian Classification for spatial pattern recognition. These methodologies are very important and useful for guiding an evolutionary statistical model-building process. This study leads to the conclusion that the HadCM3 Simulations do not realistically reproduce the NCEP Reanalyses, despite the fact that the climatology of extremes has demonstrated very similar spatial patterns. It is likely therefore that such instability may persist in the future.

scholarly journals Evaluation of a Regional Climate Modeling Effort for the Western United States Using a Superensemble from Weather@home*

2015 ◽  
Vol 28 (19) ◽  
pp. 7470-7488 ◽  
Author(s):  
Sihan Li ◽  
Philip W. Mote ◽  
David E. Rupp ◽  
Dean Vickers ◽  
Roberto Mera ◽  
...  
Keyword(s):  
United States ◽  
Spatial Pattern ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Western United States ◽  
Mean Annual Precipitation ◽  
Seasonal Temperature ◽  
Model Version ◽  
Hadley Centre

Abstract Simulations from a regional climate model (RCM) as part of a superensemble experiment were compared with observations of surface meteorological variables over the western United States. The RCM is the Hadley Centre Regional Climate Model, version 3, with improved physics parameterizations (HadRM3P) run at 25-km resolution and nested within the Hadley Centre Atmosphere Model, version 3 (HadAM3P). Overall, the means of seasonal temperature were well represented in the simulations; 95% of grid points were within 2.7°, 2.4°, and 3.6°C of observations in winter, spring, and summer, respectively. The model was too warm over most of the domain in summer except central California and southern Nevada. HadRM3P produced more extreme temperatures than observed. The overall magnitude and spatial pattern of precipitation were well characterized, though HadRM3P exaggerated the orographic enhancement along the coastal mountains, Cascade Range, and Sierra Nevada. HadRM3P produced warm/dry northwest, cool/wet southwest U.S. patterns associated with El Niño. However, there were notable differences, including the locations of the transition from warm (dry) to cool (wet) in the anomaly fields when compared with observations, though there was disagreement among observations. HadRM3P simulated the observed spatial pattern of mean annual temperature more faithfully than any of the RCM–GCM pairings in the North American Regional Climate Change Assessment Program (NARCCAP). Errors in mean annual precipitation from HadRM3P fell within the range of errors of the NARCCAP models. Last, this paper provided examples of the size of an ensemble required to detect changes at the local level and demonstrated the effect of parameter perturbation on regional precipitation.

scholarly journals The UK-China Climate Science to Service Partnership

2021 ◽  
pp. 1-37
Author(s):  
Adam A. Scaife ◽  
Elizabeth Good ◽  
Ying Sun ◽  
Zhongwei Yan ◽  
Nick Dunstone ◽  
...  
Keyword(s):  
Climate Model ◽  
Asian Summer Monsoon ◽  
Climate Extremes ◽  
Climate Science ◽  
Summer Monsoon Rainfall ◽  
Strategic Partnership ◽  
China Meteorological Administration ◽  
Climate Services ◽  
Hadley Centre ◽  
Model Computer

AbstractWe present results from the first 6 years of this major UK government funded project to accelerate and enhance collaborative research and development in climate science, forge a strong strategic partnership between UK and Chinese climate scientists and demonstrate new climate services developed in partnership. The development of novel climate services is described in the context of new modelling and prediction capability, enhanced understanding of climate variability and change, and improved observational datasets. Selected highlights are presented from over three hundred peer reviewed studies generated jointly by UK and Chinese scientists within this project. We illustrate new observational datasets for Asia and enhanced capability through training workshops on the attribution of climate extremes to anthropogenic forcing. Joint studies on the dynamics and predictability of climate have identified new opportunities for skilful predictions of important aspects of Chinese climate such as East Asian Summer Monsoon rainfall. In addition, the development of improved modelling capability has led to profound changes in model computer codes and climate model configurations, with demonstrable increases in performance. We also describe the successes and difficulties in bridging the gap between fundamental climate research and the development of novel real time climate services. Participation of dozens of institutes through sub-projects in this programme, which is governed by the Met Office Hadley Centre, the China Meteorological Administration and the Institute of Atmospheric Physics, is creating an important legacy for future collaboration in climate science and services.

scholarly journals Projecting the Most Likely Annual Urban Heat Extremes in the Central United States

Atmosphere ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 727 ◽  
Author(s):  
David E. Jahn ◽  
William A. Gallus ◽  
Phong T. T. Nguyen ◽  
Qiyun Pan ◽  
Kristen Cetin ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Extreme Values ◽  
Prediction Interval ◽  
Extreme Temperature ◽  
Global Climate Models ◽  
Steady Increase ◽  
Extreme Temperatures ◽  
Des Moines ◽  
Heat Extremes

Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period.

scholarly journals Characterization of Future Caribbean Rainfall and Temperature Extremes across Rainfall Zones

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Natalie Melissa McLean ◽  
Tannecia Sydia Stephenson ◽  
Michael Alexander Taylor ◽  
Jayaka Danaco Campbell
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Climate Extremes ◽  
Extreme Rainfall ◽  
Maximum Temperature ◽  
Temperature Extremes ◽  
Intense Rainfall ◽  
Drying Trend

End-of-century changes in Caribbean climate extremes are derived from the Providing Regional Climate for Impact Studies (PRECIS) regional climate model (RCM) under the A2 and B2 emission scenarios across five rainfall zones. Trends in rainfall, maximum temperature, and minimum temperature extremes from the RCM are validated against meteorological stations over 1979–1989. The model displays greater skill at representing trends in consecutive wet days (CWD) and extreme rainfall (R95P) than consecutive dry days (CDD), wet days (R10), and maximum 5-day precipitation (RX5). Trends in warm nights, cool days, and warm days were generally well reproduced. Projections for 2071–2099 relative to 1961–1989 are obtained from the ECHAM5 driven RCM. Northern and eastern zones are projected to experience more intense rainfall under A2 and B2. There is less consensus across scenarios with respect to changes in the dry and wet spell lengths. However, there is indication that a drying trend may be manifest over zone 5 (Trinidad and northern Guyana). Changes in the extreme temperature indices generally suggest a warmer Caribbean towards the end of century across both scenarios with the strongest changes over zone 4 (eastern Caribbean).

scholarly journals Predicting climate extremes – a complex network approach

2015 ◽  
Vol 2 (5) ◽  
pp. 1481-1505 ◽  
Author(s):  
M. Weimer ◽  
S. Mieruch ◽  
G. Schädler ◽  
C. Kottmeier
Keyword(s):  
Climate Model ◽  
Extreme Values ◽  
Regional Climate ◽  
Low Frequency ◽  
Climate Extremes ◽  
Research Field ◽  
Correlation Threshold ◽  
Application Potential ◽  
Link Strength ◽  
The One

Abstract. Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods is moderate, and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for 8 regions in Europe to construct time evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of the network measure to predict the low frequency dynamics of heat periods is similar to the one of the standard approach, with the potential of being even better in some regions.

scholarly journals Which Temperature and Precipitation Extremes Best Explain the Variation of Warm versus Cold Years and Wet versus Dry Years?

2017 ◽  
Vol 31 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Jian-Sheng Ye ◽  
Yan-Hong Gong ◽  
Feng Zhang ◽  
Jiao Ren ◽  
Xiao-Ke Bai ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Extreme Temperature ◽  
Climate Extremes ◽  
Heavy Precipitation ◽  
Ecosystem Response ◽  
Temperature And Precipitation ◽  
Extreme Cold ◽  
Precipitation Records ◽  
Precipitation Events

Abstract Intensifying climate extremes are one of the major concerns with climate change. Using 100-yr (1911–2010) daily temperature and precipitation records worldwide, 28 indices of extreme temperature and precipitation are calculated. A similarity percentage analysis is used to identify the key indices for distinguishing how extreme warm and cold years (annual temperature above the 90th and below the 10th percentile of the 100-yr distribution, respectively) differ from one another and from average years, and how extreme wet and dry years (annual precipitation above the 90th and below the 10th percentile of the 100-yr distribution, respectively) differ from each other and from average years. The analysis suggests that extreme warm years are primarily distinguished from average and extreme cold years by higher occurrence of warm nights (annual counts when night temperature >90th percentile), which occur about six more counts in extreme warm years compared with average years. Extreme wet years are mainly distinguished from average and extreme dry years by more occurrences of heavy precipitation events (events with ≥10 mm and ≥20 mm precipitation). Compared with average years, heavy events occur 60% more in extreme wet years and 50% less in extreme dry years. These indices consistently differ between extreme and average years across terrestrial ecoregions globally. These key indices need to be considered when analyzing climate model projections and designing climate change experiments that focus on ecosystem response to climate extremes.

Struther Arnott. 25 September 1934 — 20 April 2013

2015 ◽  
Vol 61 ◽  
pp. 5-22
Author(s):  
Sir Dai Rees
Keyword(s):  
Biological Sciences ◽  
Model Building ◽  
Molecular Model ◽  
Vice President ◽  
Dna And Rna ◽  
Vice Chancellor ◽  
Refinement Method ◽  
The Usa ◽  
Diffraction Patterns ◽  
Difference Fourier

Struther Arnott worked tirelessly as a researcher, teacher, leader and maker and implementer of policy in universities in Britain and the USA, always carrying his colleagues along with him through his infectious energy and breadth of academic enthusiasms and values. His outlook was shaped by the stimulus of a broad Scottish education that launched wide interests inside and outside science, including the history and literature of classical civilizations. His early research, with John Monteath Robertson FRS, was into structure determination by X-ray diffraction methods for single crystals, at a time when the full power of computers was just becoming realized for solution of the phase problem. With tenacity and originality, he then extended these approaches to materials that were to a greater or lesser extent disordered and even more difficult to solve because their diffraction patterns were poorer in information content. He brought many problems to definitive and detailed conclusion in a field that had been notable for solutions that were partial or vague, especially with oriented fibres of DNA and RNA but also various polysaccharides and synthetic polymers. His first approach was to use molecular model building in combination with difference Fourier analysis. This was followed later, and to even greater effect, by a computer refinement method that he developed himself and called linkedatom least-squares refinement. This has now been adopted as the standard approach by most serious centres of fibre diffraction analysis throughout the world. After the 10 years in which he consolidated his initial reputation at the Medical Research Council Biophysics Unit at King's College, London, in association with Maurice Wilkins FRS, he moved to Purdue University in the USA, first as Professor of Biology then becoming successively Head of the Department of Biological Sciences and Vice-President for Research and Dean of the Graduate School. As well as continuing his research, he contributed to the transformation of biological sciences at that university and to the development of the university's general management. He finally returned to his roots in Scotland as Principal and Vice-Chancellor of the University of St Andrews, to draw on his now formidable experience of international scholarship and institutional management, to reshape the patterns of academic life and mission to sit more happily and successfully within an environment that had become beset with conflict and change. He achieved this without disturbance to the harmony and wisdom embodied in the venerable traditions of that ancient Scottish yet cosmopolitan university.

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

scholarly journals Pan-spectral observing system simulation experiments of shortwave reflectance and long-wave radiance for climate model evaluation

2015 ◽  
Vol 8 (7) ◽  
pp. 1943-1954 ◽  
Author(s):  
D. R. Feldman ◽  
W. D. Collins ◽  
J. L. Paige
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Coupled Model ◽  
Climate System ◽  
Spectral Measurements ◽  
Intergovernmental Panel ◽  
Climate System Model ◽  
Detection And Attribution ◽  
Long Wave ◽  
Hadley Centre

Abstract. Top-of-atmosphere (TOA) spectrally resolved shortwave reflectances and long-wave radiances describe the response of the Earth's surface and atmosphere to feedback processes and human-induced forcings. In order to evaluate proposed long-duration spectral measurements, we have projected 21st Century changes from the Community Climate System Model (CCSM3.0) conducted for the Intergovernmental Panel on Climate Change (IPCC) A2 Emissions Scenario onto shortwave reflectance spectra from 300 to 2500 nm and long-wave radiance spectra from 2000 to 200 cm−1 at 8 nm and 1 cm−1 resolution, respectively. The radiative transfer calculations have been rigorously validated against published standards and produce complementary signals describing the climate system forcings and feedbacks. Additional demonstration experiments were performed with the Model for Interdisciplinary Research on Climate (MIROC5) and Hadley Centre Global Environment Model version 2 Earth System (HadGEM2-ES) models for the Representative Concentration Pathway 8.5 (RCP8.5) scenario. The calculations contain readily distinguishable signatures of low clouds, snow/ice, aerosols, temperature gradients, and water vapour distributions. The goal of this effort is to understand both how climate change alters reflected solar and emitted infrared spectra of the Earth and determine whether spectral measurements enhance our detection and attribution of climate change. This effort also presents a path forward to understand the characteristics of hyperspectral observational records needed to confront models and inline instrument simulation. Such simulation will enable a diverse set of comparisons between model results from coupled model intercomparisons and existing and proposed satellite instrument measurement systems.

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