scholarly journals Changes in temperature-precipitation correlations over Europe: Are climate models reliable?

2021 ◽  
Author(s):  
Mathieu Vrac ◽  
Soulivanh Thao ◽  
Pascal Yiou
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Historical Period ◽  
Model Ensemble ◽  
Reanalysis Dataset ◽  
The Past ◽  
Temperature And Precipitation ◽  
Compound Events ◽  
Changes Over Time ◽  
Climate Events

Abstract Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterise probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts. Therefore, this study first evaluates the capabilities of one 11-member multi-model ensemble (CMIP6) and one 40-member multi-run single model ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature-precipitation correlations and their historical changes. Next, the ensembles’ correlations for the end of the 21st century are compared to assess the robustness of the future correlation changes. Over historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalyses display significant correlation changes, none of the ensembles is able to reproduce them. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes. In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation. These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations.

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

scholarly journals Global hotspots for the occurrence of compound events

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nina N. Ridder ◽  
Andy J. Pitman ◽  
Seth Westra ◽  
Anna Ukkola ◽  
Hong X. Do ◽  
...  
Keyword(s):  
Western Europe ◽  
Climate Models ◽  
Global Scale ◽  
Reanalysis Data ◽  
Economic Losses ◽  
Multiple Hazards ◽  
Weather And Climate ◽  
Spatial Estimates ◽  
Compound Events ◽  
Climate Events

AbstractCompound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

scholarly journals Extreme Events Representation in CMCC-CM2 High and Very-High Resolution General Circulation Models

2021 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Daniele Peano ◽  
Silvio Gualdi ◽  
Alessio Bellucci ◽  
Tomas Lovato ◽  
...  
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
General Circulation ◽  
Climate Models ◽  
General Circulation Models ◽  
Horizontal Resolution ◽  
Historical Period ◽  
Climate Modelling ◽  
Temperature And Precipitation ◽  
Very High

Abstract. The recent advancements in climate modelling partially build on the improvement of horizontal resolution in different components of the simulating system. A higher resolution is expected to provide a better representation of the climate variability, and in this work we are particularly interested in the potential improvements in representing extreme events of high temperature and precipitation. The two versions of the CMCC-CM2 model used here, adopt the highest horizontal resolutions available within the last family of the global coupled climate models de¬veloped at CMCC to participate in the CMIP6 effort. The main aim of this study is to document the ability of the CMCC-CM2 models in representing the spatial distribution of extreme events of temperature and precipitation, under the historical period, comparing model results to observations (ERA5 Reanalysis and CHIRPS observations). For a more detailed evaluation we investigate both 6 hourly and daily time series for the definition of the extreme conditions. In terms of mean climate, the two models are able to realistically reproduce the main patterns of temperature and precipitation. The very-high resolution version (¼ degree horizontal resolution) of the atmospheric model provides better results than the high resolution one (one degree), not only in terms of means but also in terms of extreme events of temperature defined at daily and 6-hourly frequency. This is also the case of average precipitation. On the other hand the extreme precipitation is not improved by the adoption of a higher horizontal resolution.

scholarly journals Simulation of factors affecting <i>Emiliania huxleyi</i> blooms in Arctic and sub-Arctic seas by CMIP5 climate models: model validation and selection

2020 ◽  
Vol 17 (4) ◽  
pp. 1199-1212
Author(s):  
Natalia Gnatiuk ◽  
Iuliia Radchenko ◽  
Richard Davy ◽  
Evgeny Morozov ◽  
Leonid Bobylev
Keyword(s):  
Model Selection ◽  
Climate Models ◽  
Selection Procedure ◽  
Reanalysis Data ◽  
The Arctic ◽  
Historical Period ◽  
Full Model ◽  
Model Ensemble ◽  
Arctic Seas ◽  
Biochemical Variables

Abstract. The observed warming in the Arctic is more than double the global average, and this enhanced Arctic warming is projected to continue throughout the 21st century. This rapid warming has a wide range of impacts on polar and sub-polar marine ecosystems. One of the examples of such an impact on ecosystems is that of coccolithophores, particularly Emiliania huxleyi, which have expanded their range poleward during recent decades. The coccolithophore E. huxleyi plays an essential role in the global carbon cycle. Therefore, the assessment of future changes in coccolithophore blooms is very important. Currently, there are a large number of climate models that give projections for various oceanographic, meteorological, and biochemical variables in the Arctic. However, individual climate models can have large biases when compared to historical observations. The main goal of this research was to select an ensemble of climate models that most accurately reproduces the state of environmental variables that influence the coccolithophore E. huxleyi bloom over the historical period when compared to reanalysis data. We developed a novel approach for model selection to include a diverse set of measures of model skill including the spatial pattern of some variables, which had not previously been included in a model selection procedure. We applied this method to each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have been observed. Once we have selected an optimal combination of climate models that most skilfully reproduce the factors which affect E. huxleyi, the projections of the future conditions in the Arctic from these models can be used to predict how E. huxleyi blooms will change in the future. Here, we present the validation of 34 CMIP5 (fifth phase of the Coupled Model Intercomparison Project) atmosphere–ocean general circulation models (GCMs) over the historical period 1979–2005. Furthermore, we propose a procedure of ranking and selecting these models based on the model's skill in reproducing 10 important oceanographic, meteorological, and biochemical variables in the Arctic and sub-Arctic seas. These factors include the concentration of nutrients (NO3, PO4, and SI), dissolved CO2 partial pressure (pCO2), pH, sea surface temperature (SST), salinity averaged over the top 30 m (SS30 m), 10 m wind speed (WS), ocean surface current speed (OCS), and surface downwelling shortwave radiation (SDSR). The validation of the GCMs' outputs against reanalysis data includes analysis of the interannual variability, seasonal cycle, spatial biases, and temporal trends of the simulated variables. In total, 60 combinations of models were selected for 10 variables over six study regions using the selection procedure we present here. The results show that there is neither a combination of models nor one model that has high skill in reproducing the regional climatic-relevant features of all combinations of the considered variables in target seas. Thereby, an individual subset of models was selected according to our model selection procedure for each combination of variable and Arctic or sub-Arctic sea. Following our selection procedure, the number of selected models in the individual subsets varied from 3 to 11. The paper presents a comparison of the selected model subsets and the full-model ensemble of all available CMIP5 models to reanalysis data. The selected subsets of models generally show a better performance than the full-model ensemble. Therefore, we conclude that within the task addressed in this study it is preferable to employ the model subsets determined through application of our procedure than the full-model ensemble.

scholarly journals Projected Seasonal Changes in Large-Scale Global Precipitation and Temperature Extremes Based on the CMIP5 Ensemble

2020 ◽  
Vol 33 (13) ◽  
pp. 5651-5671 ◽  
Author(s):  
Wang Zhan ◽  
Xiaogang He ◽  
Justin Sheffield ◽  
Eric F. Wood
Keyword(s):  
Extreme Events ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Extreme Temperature ◽  
Economic Losses ◽  
Temperature And Precipitation ◽  
Extreme Precipitation Events ◽  
The Future ◽  
Precipitation Events

AbstractOver the past decades, significant changes in temperature and precipitation have been observed, including changes in the mean and extremes. It is critical to understand the trends in hydroclimatic extremes and how they may change in the future as they pose substantial threats to society through impacts on agricultural production, economic losses, and human casualties. In this study, we analyzed projected changes in the characteristics, including frequency, seasonal timing, and maximum spatial and temporal extent, as well as severity, of extreme temperature and precipitation events, using the severity–area–duration (SAD) method and based on a suite of 37 climate models archived in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Comparison between the CMIP5 model estimated extreme events and an observation-based dataset [Princeton Global Forcing (PGF)] indicates that climate models have moderate success in reproducing historical statistics of extreme events. Results from the twenty-first-century projections suggest that, on top of the rapid warming indicated by a significant increase in mean temperature, there is an overall wetting trend in the Northern Hemisphere with increasing wet extremes and decreasing dry extremes, whereas the Southern Hemisphere will have more intense wet extremes. The timing of extreme precipitation events will change at different spatial scales, with the largest change occurring in southern Asia. The probability of concurrent dry/hot and wet/hot extremes is projected to increase under both RCP4.5 and RCP8.5 scenarios, whereas little change is detected in the probability of concurrent dry/cold events and only a slight decrease of the joint probability of wet/cold extremes is expected in the future.

Some Pitfalls in Statistical Downscaling of Future Climate

2018 ◽  
Vol 99 (4) ◽  
pp. 791-803 ◽  
Author(s):  
John R. Lanzante ◽  
Keith W. Dixon ◽  
Mary Jo Nath ◽  
Carolyn E. Whitlock ◽  
Dennis Adams-Smith
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Added Value ◽  
Historical Period ◽  
Perfect Model

AbstractStatistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.

scholarly journals Influence of Bias Correcting Predictors on Statistical Downscaling Models

2017 ◽  
Vol 56 (1) ◽  
pp. 5-26 ◽  
Author(s):  
Mathieu Vrac ◽  
Pradeebane Vaittinada Ayar
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Local Scale ◽  
Statistical Properties ◽  
Historical Period ◽  
Climate Projections ◽  
Spatial Structures ◽  
Temperature And Precipitation ◽  
Time Period

AbstractStatistical downscaling models (SDMs) and bias correction (BC) methods are commonly used to provide regional or debiased climate projections. However, most SDMs are utilized in a “perfect prognosis” context, meaning that they are calibrated on reanalysis predictors before being applied to GCM simulations. If the latter are biased, SDMs might suffer from discrepancies with observations and therefore provide unrealistic projections. It is then necessary to study the influence of applying bias correcting to large-scale predictors for SDMs, since it can have impacts on the local-scale simulations: such an investigation for daily temperature and precipitation is the goal of this study. Hence, four temperature and three precipitation SDMs are calibrated over a historical period. First, the SDMs are forced by historical predictors from two GCMs, corrected or not corrected. The two types of simulations are compared with reanalysis-driven SDM outputs to characterize the quality of the simulations. Second, changes in basic statistical properties of the raw GCM projections and those of the SDM simulations—driven by bias-corrected or raw predictors from GCM future projections—are compared. Third, the stationarity of the SDM changes brought by the BC of the predictors is investigated. Changes are computed over a historical (1976–2005) and future (2071–2100) time period and compared to assess the nonstationarity. Overall, BC can have impacts on the SDM simulations, although its influence varies from one SDM to another and from one GCM to another, with different spatial structures, and depends on the considered statistical properties. Nevertheless, corrected predictors generally improve the historical projections and can impact future evolutions with potentially strong nonstationary behaviors.

scholarly journals A standardized index for assessing sub-monthly compound dry and hot conditions with application in China

2021 ◽  
Vol 25 (3) ◽  
pp. 1587-1601
Author(s):  
Jun Li ◽  
Zhaoli Wang ◽  
Xushu Wu ◽  
Jakob Zscheischler ◽  
Shenglian Guo ◽  
...  
Keyword(s):  
Climate Models ◽  
Southern China ◽  
Strong Dependence ◽  
Joint Probability ◽  
Heat Index ◽  
Historical Period ◽  
Joint Probability Distribution ◽  
Distribution Method ◽  
Compound Events ◽  
Daily Scale

Abstract. Compound dry and hot conditions frequently cause large impacts on ecosystems and societies worldwide. A suite of indices is available for the assessment of droughts and heatwaves, yet there is no index available for incorporating the joint variability of dry and hot conditions at the sub-monthly scale. Here we introduce a daily-scale index, called the standardized compound drought and heat index (SCDHI), to assess compound dry-hot conditions. The SCDHI is based on a daily drought index (the standardized antecedent precipitation evapotranspiration index – SAPEI), the daily-scale standardized temperature index (STI), and a joint probability distribution method. The new index is verified against real-world compound dry and hot events and associated observed vegetation impacts in China. The SCDHI can not only capture compound dry and hot events at both monthly and sub-monthly scales, but is also a good indicator for associated vegetation impacts. Using the SCDHI, we quantify the frequency, severity, duration, and intensity of compound dry-hot events during the historical period and assess the ability of climate models to reproduce these characteristics in China. We find that compound events whose severity is at least light and which last longer than 2 weeks generally persisted for 20–35 d in China. Southern China suffered from compound events most frequently, and the most severe compound events were mainly detected in this region. Climate models generally overestimate the frequency, duration, severity, and intensity of compound events in China, especially for western regions, which can be attributed to a too strong dependence between the SAPEI and STI in those models. The SCDHI provides a new tool to quantify sub-monthly characteristics of compound dry and hot events and to monitor their initiation, development, and decay. This is important information for decision-makers and stakeholders to release early and timely warnings.

scholarly journals A synthesis of the Antarctic surface mass balance during the last 800 yr

2013 ◽  
Vol 7 (1) ◽  
pp. 303-319 ◽  
Author(s):  
M. Frezzotti ◽  
C. Scarchilli ◽  
S. Becagli ◽  
M. Proposito ◽  
S. Urbini
Keyword(s):  
Mass Balance ◽  
Large Scale ◽  
Climate Models ◽  
Wave Train ◽  
Global Climate Models ◽  
Surface Mass Balance ◽  
Blowing Snow ◽  
High Accumulation ◽  
Surface Mass ◽  
The Past

Abstract. Global climate models suggest that Antarctic snowfall should increase in a warming climate and mitigate rises in the sea level. Several processes affect surface mass balance (SMB), introducing large uncertainties in past, present and future ice sheet mass balance. To provide an extended perspective on the past SMB of Antarctica, we used 67 firn/ice core records to reconstruct the temporal variability in the SMB over the past 800 yr and, in greater detail, over the last 200 yr. Our SMB reconstructions indicate that the SMB changes over most of Antarctica are statistically negligible and that the current SMB is not exceptionally high compared to the last 800 yr. High-accumulation periods have occurred in the past, specifically during the 1370s and 1610s. However, a clear increase in accumulation of more than 10% has occurred in high SMB coastal regions and over the highest part of the East Antarctic ice divide since the 1960s. To explain the differences in behaviour between the coastal/ice divide sites and the rest of Antarctica, we suggest that a higher frequency of blocking anticyclones increases the precipitation at coastal sites, leading to the advection of moist air in the highest areas, whereas blowing snow and/or erosion have significant negative impacts on the SMB at windy sites. Eight hundred years of stacked records of the SMB mimic the total solar irradiance during the 13th and 18th centuries. The link between those two variables is probably indirect and linked to a teleconnection in atmospheric circulation that forces complex feedback between the tropical Pacific and Antarctica via the generation and propagation of a large-scale atmospheric wave train.

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