Changes In Extreme Indices Research Articles

Climate change projection over Mainland Southeast Asia and the Lancang‐Mekong River basin based on a set of RegCM4 simulations

AbstractWe investigate the projected climate change over Mainland Southeast Asia (also known as the Indochina Peninsula) and the Lancang‐Mekong River basin, a region with complex topography and unique weather and climate systems, but limited availability of published high‐resolution regional climate model (RCM) studies. The study is based on an unprecedented ensemble of 21st century projections with the RegCM4 RCM driven by five different general circulation models (GCMs) at a grid spacing of 25 km under the representative concentration pathways RCP4.5 and RCP8.5. We focus on mean temperature and precipitation in the dry season November–March (NDJFM), the wet season May–September (MJJAS), and the whole year. Intercomparison between the RegCM4 simulations with the driving GCMs is provided to illustrate the added value of the RegCM4 experiments. RegCM4 reproduces greater and more realistic spatial detail of the present day temperature and precipitation distribution compared to the driving GCMs, but some biases are found, such as an overestimation of precipitation over high topography regions. The spatial pattern of biases shows some consistencies across the GCMs and, for NDJFM the RegCM4, although weak correlation is found between the GCM and nested RegCM4 biases. A generally lower warming is projected in the future by the RegCM4 in different seasons and the whole year. For precipitation, while prevailing increases are found in the GCM projections, large areas of decrease occur in the RegCM ones, in particular during the wet season, possibly due to the more detailed topographical representation. The change patterns of precipitation show consistencies across the GCMs and the RegCM4, especially in MJJAS. The projected changes of extreme indices indicate a general decrease/increase of extreme cold/warm events. Drought events are projected to be more frequent over the southwestern, while a general increase of heavy rain events prevails over most parts of the region.

Projected mean and extreme precipitation based on bias-corrected simulation outputs of CORDEX Southeast Asia

Simulations of seven regional climate models (RCMs) from CORDEX-SEA were bias-corrected using the quantile mapping (QM) method to examine the possible ranges of future mean precipitation and extreme index changes over Southeast Asia (SEA). Eleven rainfall indices including total precipitation (PRCPTOT), maximum one day precipitation (RX1day), the maximum number of consecutive dry days (MCDD), intensity of very wet days (R95pTOT) and the frequency of heavy rainfall days (R20mm) were considered. The QM procedure largely reduced the biases and inter-model variations in the historical period. For future projections, some QM modifications can be found but these were season and location dependent. The projected large changes of mean seasonal precipitation, especially over Indochina, were slightly magnified after the correction. Generally, the projected bias-corrected ensemble mean (ENSMEAN) indicated increased mean rainfall during boreal winters in mainland SEA by as much as 30% depending on the projection periods. During the boreal summer, mean rainfall in the maritime continent was projected to decrease by as much as −30% in some areas in Indonesia. In summary, the possible ranges of projected changes of extreme indices are dependent on the type of indices, scenarios and seasons. For JJA, the possible range over the entire region is between −40% and 60%. For DJF, the possible ranges are between −20% and 60% and −20%–20% over the mainland Indochina and Maritime Continent, respectively.

Changes in Climate Extremes and Their Effect on Maize (Zea mays L.) Suitability Over Southern Africa

Southern Africa has been identified as one of the hotspot areas of climate extremes increasing, at the same time many communities in the region are dependent on rain-fed agriculture, which is vulnerable to these rainfall and temperature extremes. The aim of this study is to understand changes in extreme indices during the agricultural season under climate change and how that affect the modeling of maize suitability in Southern Africa. We analyze the changes in rainfall and its extreme indices (consecutive dry days, heavy rain events and prolonged rainfall events), and temperature and its extreme indices (hot night temperatures, hot day temperatures and frequency of very hot days) from the past (1986–2014) to the future (2036–2064) and integrate these into a maize suitability model. Temperature extremes are projected to increase in both duration and intensity, particularly in the eastern parts of the region. Also, consecutive dry days are projected to increase over larger areas during the agricultural season, while rainfall will be less in sums, heavier in intensity and less prolonged in duration. Including extreme climate indices in maize suitability modeling improves the efficiency of the maize suitability model and shows more severe changes in maize suitability over Southern Africa than using season-long climatic variables. We conclude that changes in climate extremes will increase and complicate the livelihood-climate nexus in Southern Africa in the future, and therefore, a set of comprehensive adaptation options for the agricultural sector are needed. These include the use of heat, drought and high-intensity rainfall tolerant maize varieties, irrigation and/or soil water conservation techniques, and in some cases switching from maize to other crops.

Projected changes in Antarctic daily temperature in CMIP6 under different warming scenarios during two future periods

Global warming increases the frequency and intensity of climate extremes, but the changes in climate extremes over the Antarctic Ice Sheet (AIS) during different periods are unknown. Changes in surface temperature extreme indices (TN10p, TX10p, TN90p, TX90p, CSDI, WSDI, TNn, TNx, TXn, TXx and DTR) are assessed during 2021–2050 and 2071–2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, based on the multi-model ensemble mean (MMEM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The extreme indices, excluding TXn and DTR, illustrate the opposite trend in the two periods in SSP1-2.6 over the AIS. Generally, the changes in extreme indices reflect the continued warming over AIS in the future, and the warming is projected to intensify in SSP3-7.0 and SSP5-8.5. The variations in the extreme indices exhibit regional differences. The Antarctic Peninsula displays rapid changes in TNn, TXn and DTR. In SSP5-8.5, the magnitudes of all climate index tendencies are greater during 2071–2100 than 2021–2050. The variations in TX10p, TX90p, TN10p, TN90p, WSDI and CSDI are faster in the Antarctic inland than in the other regions over the AIS. However, the decrease in the DTR is concentrated along the AIS coast and extends to the interior region, whereas the increasing trend occurs in the Antarctic inland. In West AIS, TX90p and TN90p rapidly increase during 2021–2050, whereas the rapid changing signals disappear in this region in 2071–2100. The dramatic changes in TNn, TXn and DTR occur at the Ross Ice Shelf during 2071–2100, indicating an increased risk of collapse. For TNx and TXx, the degree of warming in the later part of the 21st century is divided by the transantarctic mountains, and greater changes appear on the eastern side. Generally, Antarctic amplification of TNn, TXn and DTR is observed except under SSP1-2.6. In addition, TNx and TXx amplifications occur in SSP3-7.0 and SSP5-8.5.

Attribution and projections of temperature extreme trends in South America based on CMIP5 models.

Temperature extreme indices were analyzed for five continental regions of southern South America defined according to their climatic characteristics. Gridded observations, reanalysis, and global-coupled climate models from CMIP5 were used with the approach of temperature extreme trend attribution analysis on fixed-threshold and percentile-based temperature extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The largest positive trends are exhibited in the tropical nights index, and a clear anthropogenic signal is evident in the subtropical region. In the subtropical central Andes, there is a decrease in the frost days index and increases in the tropical nights and summer days indices, and an anthropogenic signal is evident. In the Patagonian region, all trends from the historical runs were significant, while the ones from the natural experiment were nonsignificant, showing the marked effect of anthropogenic forcing in this region in the extreme temperature events. Projected changes in extreme indices for the 21st century are consistent with a warming climate, and larger changes are expected in the warm nights index.

Impact of Climate Change on Precipitation Extremes over Ho Chi Minh City, Vietnam

In the context of climate change, the impact of hydro-meteorological extremes, such as floods and droughts, has become one of the most severe issues for the governors of mega-cities. The main purpose of this study is to assess the spatiotemporal changes in extreme precipitation indices over Ho Chi Minh City, Vietnam, between the near (2021–2050) and intermediate (2051–2080) future periods with respect to the baseline period (1980–2009). The historical extreme indices were calculated through observed daily rainfall data at 11 selected meteorological stations across the study area. The future extreme indices were projected based on a stochastic weather generator, the Long Ashton Research Station Weather Generator (LARS-WG), which incorporates climate projections from the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Eight extreme precipitation indices, such as the consecutive dry days (CDDs), consecutive wet days (CWDs), number of very heavy precipitation days (R20mm), number of extremely heavy precipitation days (R25mm), maximum 1 d precipitation amount (RX1day), maximum 5 d precipitation amount (RX5day), very wet days (R95p), and simple daily intensity index (SDII) were selected to evaluate the multi-model ensemble mean changes of extreme indices in terms of intensity, duration, and frequency. The statistical significance, stability, and averaged magnitude of trends in these changes, thereby, were computed by the Mann-Kendall statistical techniques and Sen’s estimator, and applied to each extreme index. The results indicated a general increasing trend in most extreme indices for the future periods. In comparison with the near future period (2021–2050), the extreme intensity and frequency indices in the intermediate future period (2051–2080) present more statistically significant trends and higher growing rates. Furthermore, an increase in most extreme indices mainly occurs in some parts of the central and southern regions, while a decrease in those indices is often projected in the north of the study area.

Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections

Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 1/4 degrees (25 km) over one of the most climatically diversified countries, India. We showcase significant improvement gain against two popular state-of-the-art baselines with a better ability to predict statistics of extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.

Multi-model projections of precipitation extremes in Southeast Asia based on CORDEX-Southeast Asia simulations

This study examines the projected precipitation extremes for the end of 21st century (2081–2100) over Southeast Asia (SEA) using the output of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (SEACLID/CORDEX-SEA). Eight ensemble members, representing a subset of archived CORDEX-SEA simulations at 25 km spatial resolution, were examined for emission scenarios of RCP4.5 and RCP8.5. The study utilised four different indicators of rainfall extreme, i.e. the annual/seasonal rainfall total (PRCPTOT), consecutive dry days (CDD), frequency of extremely heavy rainfall (R50mm) and annual/seasonal maximum of daily rainfall (RX1day). In general, changes in extreme indices are more pronounced and covering wider area under RCP8.5 than RCP4.5. The decrease in annual PRCPTOT is projected over most of SEA region, except for Myanmar and Northern Thailand, with magnitude as much as 20% (30%) under RCP4.5 (RCP8.5) scenario. The most significant and robust changes were noted in CDD, which is projected to increase by as much as 30% under RCP4.5 and 60% under RCP8.5, particularly over Maritime Continent (MC). The projected decrease in PRCPTOT over MC is significant and robust during June to August (JJA) and September to November (SON). During March to May (MAM) under RCP8.5, significant and robust PRCPTOT decreases are also projected over Indochina. The CDD changes during JJA and SON over MC are even higher, more robust and significant compared to the annual changes. At the same time, a wetting tendency is also projected over Indochina. The R50mm and RX1day are projected to increase, during all seasons with significant and robust signal of RX1day during JJA and SON.

Characterization of future climate extremes over Tamil Nadu, India, using high-resolution regional climate model simulation

Tamil Nadu, an agriculturally important state in India, is recurrently exposed to floods, cyclones, and droughts that have devastating effects on human, agriculture, and economy. This persuades the study to focus on future climate extremities of the state. The future climate extremes are examined using daily temperature and rainfall simulations developed by Hadley Center’s Regional Climate Model termed PRECIS (Providing REgional Climates for Impacts Studies) at a horizontal resolution of 25 km. The study uses the simulations that are driven by the lateral boundary condition of HadCM3Q0 (Hadley Centre Coupled Model-Q0) generated by a Perturbed Physical Ensemble of 17 ensembles under A1B scenario for the period of 2005 to 2095. Extreme indices have been acquired from the simulations using RClimDex. The trends of extreme indices are computed and verified for statistical significance using Mann–Kendall and Sen’s slope test. The changes in extreme indices with respect to baseline (1970–2000) reveal that almost all temperature indices denote a highly significant trend. The minimum temperature indices have shown prominent increase compared with maximum temperature indices, which is also upheld by the significant decrease in the diurnal temperature range trend. The summer days above 40 °C have indicated a substantial increase with a stronger slope of 0.77. However, the rainfall indices depict the insignificant trend. The changes in extreme wet days (R99p) and very wet days (R95p) exhibit a positive shift, and the increase of maximum 1-day rainfall is projected to be higher than maximum 5-day rainfall. Furthermore, the probability of rainfall indices exemplifies the increase of intense rainfall by the end of the century. The overall results of indices intimate that Tamil Nadu will be shifted to the extreme warmer and wetter condition by 2080s (2065–2095). Such information will act as a baseline to study the future impact assessment on different sectors thereby supporting policymakers and stakeholders to formulate suitable adaptation and mitigation strategies.

Impacts of idealized land cover changes on climate extremes in Europe

Abstract Extremes in climate and weather can pose significant challenges to economy, ecosystems and human health. Changes in land cover are one of the drivers for variability in frequency and magnitude of extreme climate at regional and local levels. In this study, a regional climate model (COSMO-CLM v4.8) is used to simulate effects in climate extremes from two different idealized land cover change scenarios in Europe. These two simulations involve abrupt large-scale conversion of today forestland to herbaceous vegetation (deforestation), and of today cropland to evergreen needle-leave forest (afforestation). A control simulation with today land cover distribution is used to identify differences in extreme climate. We find significant changes in extreme climate in both deforestation and afforestation simulations, with seasonal and spatial differences. Deforestation causes a warmer summer (with higher annual maximum temperature) and a colder winter (with lower annual minimum temperature). Afforestation slightly increases the average intensity of the hot extremes, although with high spatial variability (a reduction is common in several locations), and mitigates cold extremes in winter. Changes in extreme indices show that deforestation increases both the frequency and duration of hot and cold extremes, while afforestation causes a lower frequency of extreme cold climate. The two simulations show opposing results in the number of frozen days, as they increase for deforestation and decrease for afforestation. A drier climate is found after deforestation, whereas a wetter climate is observed after afforestation. In general, deforestation and afforestation increase the frequency of hot extreme climate as they reduce the return period and increase the return level. Overall, our findings show the potential critical effects that land cover changes can have on climate extremes, and the possible synergies that land management strategies and planning can have for climate change mitigation and adaptation at a regional scale.

Intercomparison of projected changes in climate extremes for South Korea: application of trend preserving statistical downscaling methods to the CMIP5 ensemble

ABSTRACTGlobal climate models (GCMs) provide the fundamental information used to assess potential impacts of future climate change. However, the mismatch in spatial resolution between GCMs and the requirements of regional applications has impeded the use of GCM projections for impact studies at a regional scale. This study applied statistical post‐processing methods that preserve long‐term temporal trends, bias‐correction/spatial disaggregation with detrended quantile mapping (SDDQM) and BCSD with quantile delta mapping (SDQDM), to downscale 20 CMIP5 GCM climate projections for daily precipitation, minimum temperature, and maximum temperature over South Korea. Using the downscaled CMIP5 climate projections, we investigated absolute changes in extreme indices between the reference and three 30‐year future periods. In addition, the biases in change signals from GCM projections for different statistical downscaling methods were compared to evaluate how well long‐term trends in indices are preserved. The results showed that the statistical downscaling methods significantly improved the skill in reproducing extreme indices. For temperature‐related extreme indices, we found strong significant trends while trends for precipitation‐related indices varied depending on the index and climate projection horizon. Specifically, more frequent, longer duration, and more intense hot extremes may occur under the CMIP5 climate projections, while corresponding decreases may occur for extreme cold indices. Prominent upward trends are found in extreme precipitation events. Regarding analysis of the bias in change signals, SDQDM, which explicitly preserves changes in all quantiles of the underlying variables, better preserved long‐term trends in extreme indices simulated by GCMs.

Spatial and temporal changes in daily temperature extremes in China during 1960–2011

Based on daily maximum and minimum temperature data from 437 weather stations over China, this study examined the spatiotemporal change of temperature extremes in China from 1960 to 2011. Results showed a general downward trends in the occurrence of cold days (TX10) and nights (TN10) (base period 1961–1990), but upward tendency on the occurrence of warm days (TX90) and nights (TN90), the temperatures of coldest day (TXn), coldest night (TNn), warmest day (TXx), and warmest night (TNx) in China and most climate regions. At the national scale, TX10 and TN10 have significantly decreased by −1.89 and −4.39 days/decade, and TX90 and TN90 have significantly increased by 2.49 and 4.72 days/decade from 1960 to 2011. The national average trends for TXn, TNn, TXx, and TNx were 0.28, 0.54, 0.17, and 0.27 °C/decade, respectively. The temporal changes of extremes indices showed that changes in cold (warm) relative indices may be primarily related to that of corresponding winter (summer) Tmax and Tmin, respectively. Regionally, the magnitudes of changes in extreme indices decreased from the north to south of China. However, we found significant increase of warm extremes, especially warm days and nights in Southeast China. For most climate regions, the trend magnitudes in warm days/nights were larger than that in cold days/nights, but the trend in coldest temperature was much higher than that in warmest temperature. The trend magnitudes in minimum temperature indices were larger than those based on daily maximum temperature, explaining the faster increase of Tmin than Tmax in China.

Changes in extreme precipitation in the Huang-Huai-Hai River basin of China during 1960-2010

With the increasing exposure of populations and economy to the natural hazards, it is of vital importance to study the spatiotemporal characteristics of extreme precipitation. Based on daily precipitation at 154 meteorological stations in the Huang-Huai-Hai River basin of China during 1960–2010, the spatial and temporal changes in extreme precipitation were analyzed using twelve indices. The basin was divided into five climate areas using clustering analysis to detect the spatial changes of the extreme indices, and the temporal changes in the probability distributions of the extreme indices were also examined. The results showed that maximum 5-day precipitation, wet days and consecutive wet days decreased significantly while consecutive dry days showed a weak increasing trend. The other precipitation indices had insignificant decreasing trends. The probability distribution functions of simple daily intensity index and consecutive dry days were positively shifted while the rest of the indices were negatively shifted. The temporal changes of extreme indices implied that the frequency of extreme precipitation was decreasing, but the intensity of extreme precipitation was increasing in the Huang-Huai-Hai River basin. The spatial changes in the aspect of precipitation extreme events showed obvious spatial differences between different climate areas. In addition, the series including maximum 1-day precipitation and maximum 5-day precipitation were fitted by generalized extreme-value distribution for risk analysis and the results showed that the generalized extreme-value distribution could fit the series well. The amounts of extreme precipitation for different return periods were calculated and high risk areas for flooding disaster were presented.

The potential of pattern scaling for projecting temperature-related extreme indices

Pattern scaling can be used to linearly relate changes in extreme indices to changes in the annual or seasonal mean temperature. This study demonstrates the skills and limitations of two often used pattern scaling approaches in filling-in gaps in the time series of six temperature-related extreme indices. The extreme indices over Europe are derived from daily temperature output of 12 regional climate models of the multi-model project ENSEMBLES. The response pattern is estimated using one of the two future time periods (2021–2050 or 2070–2099) and the reference period (1961–1990). The simulated values from the remaining future time period are used for evaluating the skills. Both pattern scaling approaches perform reasonably well particularly for percentile-based and over most of the regions also for fixed temperature indices. Uncertainties due to internal variability can be large if the time period used for estimating the response pattern is close to the reference period. Limitations of pattern scaling due to violations of the linearity assumption are related to the shape of the temperature distribution. As a result, differences in the skills among the extreme indices can be related to the magnitude and shift direction of the whole temperature distribution. Therefore, skills for estimated extreme indices derived from the upper tail of the underlying temperature distribution are generally high. Over some areas, linear regression models used in this study are not appropriate statistical models because of the bounded and discrete nature of the data. Alternative pattern scaling methods such as, for instance, the logistic regression model leads to improvements over particular areas but not over the whole integration area. Copyright © 2013 Royal Meteorological Society

Spatially downscaling GCMs outputs to project changes in extreme precipitation and temperature events on the Loess Plateau of China during the 21st Century

A simple transfer function method is used to spatially downscale extreme precipitation and temperature indices of six GCMs (CNRM-CM3, GFDL-CM2.1, INM-CM3.0, IPSL-CM4, MIROC3.2_M and NCAR-PCM) under three emission scenarios (A1B, A2, B1) from grid outputs to target station to project their potential spatiotemporal changes on the Loess Plateau of China during the 21st century. GCMs project that extreme climate events will keep the present change trend, i.e. longer heatwave duration and growing season length, less cold extremes, smaller annual extreme temperature range, more frequent and intense precipitation, and longer drought duration; and climate models suggest that the changes during the 21st century are a further amplification of those at present. Spatial variations exist in the changes of extreme indices, the greatest changes will occur in the southeast and northwest region for extreme precipitation indices while in the north and southeast region for extreme temperature events. The projected changes in extreme climate will possibly bring great impacts on the soil losses and agriculture on the Loess Plateau and countermeasures should be planned.

Changes in severe indices as simulated by two French coupled global climate models

Extremes are assessed here in an attempt to validate the two French models in their representation of the second part of the 20th century, using different sources of gridded observational datasets. Models show some ability to simulate extremal behaviour of the climate even if discrepancies are noticeable between models and observations. These may be partly due to the low resolution used for the present study simulations. Extreme indices, calculated using the STARDEX (STAtistical and Regional dynamical Downscaling of EXtremes for European regions) methodology, are investigated in different IPCC (International Panel on Climate Change) scenarios performed by the French community. Investigation of 21st century severe indices simulated in these simulations shows some interesting features. In some parts of the world, extreme temperatures experience a more rapid increase than the mean, suggesting that the Power Density Function (PDF) may not only be shifted toward higher temperatures but also changed in its shape. Extremes of precipitation also experience a change toward more intense precipitation events in winter and longer dry events in summer. Approaching future changes in extreme indices through their relationship to mean annual temperature may be a useful approach in multi-model studies, since it provides a measure of the sensitivity of extremes to warming conditions in these models.

Changes in Extremes Indices over Japan Due to Global Warming Projected by a Global 20-km-mesh Atmospheric Model

Changes in indices of extremes between the present-day climate and a future warmer climate are projected over Japan using a global 20-km-mesh atmospheric model. Comparisons with observed data show that the indices on temperature extremes are represented well in the model, while less intense precipitation biases are found. In the future climate simulation around 2090, the number of frost days decreases by 20-45 days with larger decrease along the Sea of Japan than the other area. Growing season length increases about a month. Changes in the temperature extremes are not uniform over Japan, showing usefulness of projections using a high-resolution model. Although changes in precipitation extremes are small and not significant over a large part of Japan, statistically significant increase in indices of heavy precipitation is found in western part of Japan and Hokkaido.