Characteristics of sub-daily precipitation extremes in observed data and regional climate model simulations

Theoretical and Applied Climatology - The study compares characteristics of observed sub-daily precipitation extremes in the Czech Republic with those simulated by Hadley Centre Regional Model...     

South Australian rainfall variability and climate extremes

Rainfall extremes over South Australia are connected with broad-scale atmospheric rearrangements associated with strong meridional sea surface temperature (SST) gradients in the eastern Indian Ocean. Thirty-seven years of winter radiosonde data is used to calculate a time series of precipitable water (PW) and convective available potential energy (CAPE) in the atmosphere. Principle component analysis on the parameters of CAPE and PW identify key modes of variability that are spatially and seasonally consistent with tropospheric processes over Australia. The correlation of the leading principle component of winter PW to winter rainfall anomalies reveal the spatial structure of the northwest cloudband and fronts that cross the southern half of the continent during winter. Similarly the second and third principle components, respectively, reveal the structures of the less frequent northern and continental cloudbands with remarkable consistency. 850 hPa-level wind analysis shows that during dry seasons, anomalous offshore flow over the northwest of Australia inhibits advection of moisture into the northwest, while enhanced subsidence from stronger anticyclonic circulation over the southern half of the continent reduces CAPE. This coincides with a southward shift of the subtropical ridge resulting in frontal systems passing well to the south of the continent, thus producing less frequent interaction with moist air advected from the tropics. Wet winters are the reverse, where a weaker meridional pressure gradient to the south of the continent allows rain-bearing fronts to reach lower latitudes. The analysis of SSTs in the Indian Ocean indicate that anomalous warm (cool) waters in the southeast Indian Ocean coincide with a southward (northward) shift in the subtropical ridge during dry (wet) seasons.     

An evaluation of the CORDEX regional climate models in simulating future rainfall and extreme events over Mzingwane catchment,Zimbabwe

The study evaluated CORDEX RCMs’ ability to project future rainfall and extreme events in the Mzingwane catchment using an ensemble average of three RCMs (RCA4, REMO2009 and CRCM5). Model validation employed the statistical mean and Pearson correlation, while trends in projected rainfall and number of rainy days were computed using the Mann-Kendall trend test and the magnitudes of trends were determined by Sen’s slope estimator. Temporal and spatial distribution of future extreme dryness and wetness was established by using the Standard Precipitation Index (SPI). The results show that RCMs adequately represented annual and inter-annual rainfall variability and the ensemble average outperformed individual models. Trend results for the projected rainfall suggest a significant decreasing trend in future rainfall (2016–2100) for all stations at p < 0.05. In addition, a general decreasing trend in the number of rainy days is projected for future climate, although the significance and magnitude varied with station location. Model results suggest an increased occurrence of future extreme events, particularly towards the end of the century. The findings are important for developing proactive sustainable strategies for future climate change adaption and mitigation.     

Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.     

Zonal winds and southeast Australian rainfall in global and regional climate models

Southeast Australia is a region of high rainfall variability related to major climate drivers, with a long-term declining trend in cool-season rainfall. Projections of future rainfall trends are uncertain in this region, despite projected southward shifts in the subtropical ridge and mid-latitude westerlies. This appears to be related to a poor representation of the spatial relationships between rainfall variability and zonal wind patterns across southeast Australia in the latest Coupled Model Intercomparison Project ensemble, particularly in the areas where weather systems embedded in the mid-latitude westerlies are the main source of cool-season rainfall. Downscaling with regional climate models offers improvements in the mean rainfall climatology, and shows some ability to correct for poor modelled relationships between rainfall and zonal winds along the east coast of Australia. However, it provides only minor improvements to these relationships in southeast Australia, despite the improved representation of topographic features. These results suggest that both global and regional climate models may fail to translate projected circulation changes into their likely rainfall impacts in southeast Australia.     

Interannual variability of rainfall over the Sahel based on multiple regional climate models simulations

We analyse the interannual variability of the averaged summer monsoon rainfall over the Sahel from multiple regional climate models driven by the ERA-interim reanalysis and seek to provide effective information for future modelling work. We find that the majority of the models are able to reproduce the rainfall variability with correlation coefficient exceeding 0.5 compared with observations. This is due to a good representation of the dynamics of the main monsoon features of the West African climate such as the monsoon flux, African Easterly Jet (AEJ) and Tropical Easterly Jet (TEJ). Among the models, only HIRHAM fails to reproduce the rainfall variability exhibiting hence a correlation coefficient of ?0.2. This deficiency originates from the fact that HIRHAM does not properly capture the variability of monsoon flow and the relationship between rainfall and the AEJ dynamic. We conclude that a good performance of a regional climate model in simulating the monsoon dynamical features variability is of primary importance for a better representation of the interannual variability of rainfall over the Sahel.     

Changes in rainfall regime over Burkina Faso under the climate change conditions simulated by 5 regional climate models

Sahelian rainfall has recorded a high variability during the last century with a significant decrease (more than 20 %) in the annual rainfall amount since 1970. Using a linear regression model, the fluctuations of the annual rainfall from the observations over Burkina Faso during 1961–2009 period are described through the changes in the characteristics of the rainy season. The methodology is then applied to simulated rainfall data produced by five regional climate models under A1B scenario over two periods: 1971–2000 as reference period and 2021–2050 as projection period. As found with other climate models, the projected change in annual rainfall for West Africa is very uncertain. However, the present study shows that some features of the impact of climate change on rainfall regime in the region are robust. The number of the low rainfall events (0.1–5 mm/d) is projected to decrease by 3 % and the number of strong rainfall events (>50 mm/d) is expected to increase by 15 % on average. In addition, the rainy season onset is projected by all models to be delayed by one week on average and a consensus exists on the lengthening of the dry spells at about 20 %. Furthermore, the simulated relationship between changed annual rainfall amounts and the number of rain days or their intensity varies strongly from one model to another and some changes do not correspond to what is observed for the rainfall variability over the last 50 years.     

Evaluation of CORDEX regional climate models in simulating temperature and precipitation over the Tibetan Plateau

区域气候模式对研究地形复杂的青藏高原地区气候具有高分辨率的优势。以前的相关研究主要基于单个区域模式,我们评估了CORDEX多区域气候模式对青藏高原气候的模拟能力。结果显示:(1)5个区域气候模式一致模拟出了相似的气温、降水空间模态,但产生了冷偏差和湿偏差。所有区域气候模式未能再现观测的气温、降水趋势空间模态,并且平均高估了气温趋势、低估了降水趋势。综合考虑模拟的气温、降水趋势,多模式集合的结果最优。就单个模式而言,Reg CM4所得趋势最为合理。(2)各区域气候模式结果之间的差异十分显著,表明青藏高原气候模拟具有很大的模式依赖性。这一结果建议当利用单个区域气候模式开展青藏高原气候变化研究时需要谨慎。(3)多区域模式集合预估显示,相对1986–2005年,到2016–2035年气温(降水)将增加1.38±0.09°C(0.8%±4.0%)(RCP4.5)和1.77±0.28°C(7.3%±2.5%)(RCP8.5)。这些结果从多模式角度提高了我们对运用区域气候模式研究青藏高原气候的认识。     

Scale dependency of regional climate modeling of current and future climate extremes in Germany

A 26-year simulation (1980–2005) was performed with the Weather Research and Forecast (WRF) model over the Volta Basin in West Africa. This was to investigate the ability of a climate version of WRF to reproduce present day temperature and precipitation over the Volta Basin. The ERA-Interim reanalysis and one realization of the ECHAM6 global circulation model (GCM) data were dynamically downscaled using two nested domains within the WRF model. The outer domain had a horizontal resolution of 50 km and covered the whole of West Africa while the inner domain had a horizontal resolution of 10 km. It was observed that biases in the respective forcing data were carried over to the RCM, but also the RCM itself contributed to the mean bias of the model. Also, the biases in the 50-km domain were transferred unchanged, especially in the case of temperature, to the 10-km domain, but, for precipitation, the higher-resolution simulations increased the mean bias in some cases. While in general, WRF underestimated temperature in both the outer (mean biases of ?1.6 and ?2.3 K for ERA-Interim and ECHAM6, respectively) and the inner (mean biases of ?0.9 K for the reanalysis and ?1.8 K for the GCM) domains, WRF slightly underestimated precipitation in the coarser domain but overestimated precipitation in the finer domain over the Volta Basin. The performance of the GCM, in general, is good, particularly for temperature with mean bias of ?0.7 K over the outer domain. However, for precipitation, the added value of the RCM cannot be overlooked, especially over the whole West African region on the annual time scale (mean biases of ?3% for WRF and ?8% for ECHAM6). Over the whole Volta Basin and the Soudano-Sahel for the month of April and spring (MAM) rainfall, respectively, mean bias close to 0% was simulated. Biases in the interannual variability in both temperature and precipitation over the basin were smaller in the WRF than the ECHAM6. High spatial pattern correlations between 0.7 and 0.8 were achieved for the autumn precipitation and low spatial correlation in the range of 0.0 and 0.2 for the winter season precipitation over the whole basin and all the three belts over the basin.     

Mid-21st century projections in temperature extremes in the southern Colorado Rocky Mountains from regional climate models

This study analyzes mid-21st century projections of daily surface air minimum (T_min) and maximum (T_max) temperatures, by season and elevation, over the southern range of the Colorado Rocky Mountains. The projections are from four regional climate models (RCMs) that are part of the North American Regional Climate Change Assessment Program (NARCCAP). All four RCMs project 2°C or higher increases in T_min and T_max for all seasons. However, there are much greater (>3°C) increases in T_max during summer at higher elevations and in T_min during winter at lower elevations. T_max increases during summer are associated with drying conditions. The models simulate large reductions in latent heat fluxes and increases in sensible heat fluxes that are, in part, caused by decreases in precipitation and soil moisture. T_min increases during winter are found to be associated with decreases in surface snow cover, and increases in soil moisture and atmospheric water vapor. The increased moistening of the soil and atmosphere facilitates a greater diurnal retention of the daytime solar energy in the land surface and amplifies the longwave heating of the land surface at night. We hypothesize that the presence of significant surface moisture fluxes can modify the effects of snow-albedo feedback and results in greater wintertime warming at night than during the day.     

Evaluation of the CORDEX regional climate models performance in simulating climate conditions of two catchments in Upper Blue Nile Basin

This study was targeted at evaluating the performance of six Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX). The evaluation is on the bases of how well the RCMs simulate the seasonal mean climatology, interannual variability and annual cycles of rainfall, maximum and minimum temperature over two catchments in western Ethiopia during the period 1990–2008. Observed data obtained from the Ethiopian National Meteorological Agency was used for performance evaluation of the RCMs outputs. All Regional Climate Models (RCMs) have simulated seasonal mean annual cycles of precipitation with a significant bias shown on individual models; however, the ensemble mean exhibited better the magnitude and seasonal rainfall. Despite the highest biases of RCMs in the wet season, the annual cycle showed the prominent features of precipitation in the two catchments. In many aspects, CRCM5 and RACMO22 T simulate rainfall over most stations better than the other models. The highest biases are associated with the highest error in simulating maximum and minimum temperature with the highest biases in high elevation areas. The rainfall interannual variability is less evident in Finchaa with short rainy season experiencing a larger degree of interannual variability. The differences in performance of the Regional Climate Models in the two catchments show that all the available models are not equally good for particular locations and topographies. In this regard, the right regional climate models have to be used for any climate change impact study for local-scale climate projections.     

Politics eclipses climate extremes for climate change perceptions

Whether or not actual shifts in climate influence public perceptions of climate change remains an open question, one with important implications for societal response to climate change. We use the most comprehensive public opinion survey data on climate change available for the US to examine effects of annual and seasonal climate variation. Our results show that political orientation has the most important effect in shaping public perceptions about the timing and seriousness of climate change. Objective climatic conditions do not influence Americans’ perceptions of the timing of climate change and only have a negligible effect on perceptions about the seriousness of climate change. These results suggest that further changes in climatic conditions are unlikely to produce noticeable shifts in Americans’ climate change perceptions.     

Internal variability of regional climate models

 Two regional climate models have been applied to the task of generating an ensemble of realizations of the year 1982 with observed boundary conditions in areas covering parts of the Mediterranean countries. These realizations were generated by applying boundary conditions from the ECMWF ERA reanalysis project consecutively, carrying over the soil variables from the regional models from one iteration to the next. Monthly mean fields for six iterations of each model have been used as statistical ensembles in order to investigate the internal variability of the regional model dynamics. This internal variability is a necessary consequence of the non-linear physical feedback mechanisms of the RCM being active. A small value of internal variability will give better statistics for climate sensitivity signals, but will make these results less credible. The internal variability is important for the quantitative assessment of a climate sensitivity signal. With the present choice of models and integration domains the internal variabilities of surface fields and precipitation do reach levels that are less than, but in summer of comparable order of magnitude to, corresponding atmospheric variabilities of an atmospheric general circulation model. Received: 26 October 1999 / Accepted: 18 December 2000     

Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model

This study estimates the potential for added value in dynamical downscaling by increasing the spatial resolution of the regional climate model (RCM) over Korea. The Global/Regional Integrated Model System—Regional Model Program with two different resolutions is employed as the RCM. Large-scale forcing is given by a historical simulation of a global climate model, namely the Hadley Center Global Environmental Model version 2. As a standard procedure, the reproducibility of the RCM results for the present climate is evaluated against the reanalysis and observation datasets. It is confirmed that the RCM adequately reproduces the major characteristics of the observed atmospheric conditions and the increased resolution of the RCM contributes to the improvement of simulated surface variables including precipitation and temperature. For the added-value assessment, the interannual and daily variabilities of precipitation, temperature are compared between the different resolution RCM experiments. It is distinctly shown that variabilities are additionally described as the spatial resolution becomes higher. The increased resolution also contributes to capture the extreme weather conditions, such as heavy rainfall events and sweltering days. The enhanced added value is more evident for the precipitation than for the temperature, which stands for a usefulness of the high-resolution RCM especially for diagnosing potential hazard related to heavy rainfall. The results of this study assure the effectiveness of increasing spatial resolution of the RCM for detecting climate extremes and also provide credibility to the current climate simulation for future projection studies.     

Assessing future changes in seasonal climatic extremes in the Ganges river basin using an ensemble of regional climate models

Using an ensemble of four high resolution (~25 km) regional climate models, this study analyses the future (2021–2050) spatial distribution of seasonal temperature and precipitation extremes in the Ganges river basin based on the SRES A1B emissions scenario. The model validation results (1989–2008) show that the models simulate seasonality and spatial distribution of extreme temperature events better than precipitation. The models are able to capture fine topographical detail in the spatial distribution of indices based on their ability to resolve processes at a higher regional resolution. Future simulations of extreme temperature indices generally agree with expected warming in the Ganges basin, with considerable seasonal and spatial variation. Significantly warmer summers in the central part of the basin along with basin-wide increase in night temperature are expected during the summer and monsoon months. An increase in heavy precipitation indices during monsoon, coupled with extended periods without precipitation during the winter months; indicates an increase in the incidence of extreme events.     

Seasonal variability of rainfall extremes

Monthly rainfall extremes have been analyzed for three stations in Southern Ontario. The double exponential probability distribution was fitted to the extreme values for each month considered, each duration selected, and sets of annual extremes. A station‐year approach yielded monthly and annual extreme value distributions for the lumped region of Southern Ontario. The analysis has revealed a pronounced seasonal pattern in the rainfall extremes – the amount of rain expected with a selected probability of occurrence during the summer being considerably greater than the rainfall that might be expected to be exceeded at the same probability level during the spring or fall. The extent of the seasonal variability was found also to vary with duration. The implications of the variability are seen to be significant for the estimation of the magnitude and frequency of floods.     

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.     

Multi-variable error correction of regional climate models

Climate change impact research needs regional climate scenarios of multiple meteorological variables. Those variables are available from regional climate models (RCMs), but affected by considerable biases. We evaluate the application of an empirical-statistical error correction method, quantile mapping (QM), for a small ensemble of RCMs and six meteorological variables. Annual and monthly biases are reduced to close to zero by QM for all variables in most cases. Exceptions are found, if non-stationarity of the model’s error characteristics occur. Even in the worst cases of non-stationarity, QM clearly improves the biases of raw RCMs. In addition, QM successfully adjusts the distributions of the analysed variables. To approach the question whether time series and inter-variable relationships are still plausible after correction, we evaluate the root-mean-square error (RMSE), autocorrelation and inter-variable correlation. We found improvement or no clear effect in RMSE and autocorrelation, and no clear effect on the correlation between meteorological variables. These results demonstrate that QM retains the quality of the temporal structure in time series and the inter-variable dependencies of RCMs. It has to be emphasised that this cannot be interpreted as an improvement and that deficiencies of the RCMs in those features are retained as well. Our results give some indication for the performance of QM applied to future scenarios, since our evaluation relies on independent calibration and evaluation periods, which are affected by climate variability and change. The effect of non-stationarity, however, can be expected to be larger in far future. We demonstrate the retainment of the RCM’s temporal structure and inter-variable dependencies, and large improvements in biases. This qualifies QM as a valuable, though not perfect, method in the interface between climate models and climate change impact research. Nonetheless, in case of no correlation between re-analysis driven RCM and observation, one should consider that QM does not correct this correlation.