Future extreme events in European climate: an exploration of regional climate model projections

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.     

Uncertainties in solar radiation assessment in the United States using climate models

Climate Dynamics - Solar energy is abundant and offers significant potential for future climate change mitigation. This study investigates the impacts of climate change on surface solar radiation...     

Multimodel ensemble projections of future climate extreme changes in the Haihe River Basin,China

Exploring the characteristic of the extreme climatic events, especially future projection is considerably important in assessing the impacts of climatic change on hydrology and water resources system. We investigate the future patterns of climate extremes (2001–2099) in the Haihe River Basin (HRB) derived from Coupled General Circulation Model (CGCM) multimodel ensemble projections using the Bayesian Model Average (BMA) approach, under a range of emission scenarios. The extremes are depicted by three extreme temperature indices (i.e., frost days (FD), growing season length (GSL), and T _min >90th percentile (TN90)) and five extreme precipitation indices (i.e., consecutive dry days (CDD), precipitation ≥10 mm (R10), maximum 5-day precipitation total (R5D), precipitation >95th percentile (R95T), and simple daily intensity index (SDII)). The results indicate frost days display negative trend over the HRB in the 21st century, particularly in the southern basin. Moreover, a greater season length and more frequent warm nights are also projected in the basin. The decreasing CDD, together with the increasing R10, R5D, R95T, and SDII in the 21st century indicate that the extreme precipitation events will increase in their intensity and frequency in the basin. Meanwhile, the changes of all eight extremes climate indices under A2 and A1B scenarios are more pronounced than in B1. The results will be of practical significance in mitigation of the detrimental effects of variations of climatic extremes and improve the regional strategy for water resource and eco-environment management, particularly for the HRB characterized by the severe water shortages and fragile ecological environment.     

Changing climate extremes in the Northeast United States: observations and projections from CMIP5

Climate extremes indices are evaluated for the northeast United States and adjacent Canada (Northeast) using gridded observations and twenty-three CMIP5 coupled models. Previous results have demonstrated observed increases in warm and wet extremes and decreases in cold extremes, consistent with changes expected in a warming world. Here, a significant shift is found in the distribution of observed total annual precipitation over 1981-2010. In addition, significant positive trends are seen in all observed wet precipitation indices over 1951-2010. For the Northeast region, CMIP5 models project significant shifts in the distributions of most temperature and precipitation indices by 2041-2070. By the late century, the coldest (driest) future extremes are projected to be warmer (wetter) than the warmest (wettest) extremes at present. The multimodel interquartile range compares well with observations, providing a measure of confidence in the projections in this region. Spatial analysis suggests that the largest increases in heavy precipitation extremes are projected for northern, coastal, and mountainous areas. Results suggest that the projected increase in total annual precipitation is strongly influenced by increases in winter wet extremes. The largest decreases in cold extremes are projected for northern and interior portions of the Northeast, while the largest increases in summer warm extremes are projected for densely populated southern, central, and coastal areas. This study provides a regional analysis and verification of the latest generation of CMIP global models specifically for the Northeast, useful to stakeholders focused on understanding and adapting to climate change and its impacts in the region.     

印江极端降水和极端强降水日数统计特征

利用印江气象站的逐日降水资料,在对该站点极端降水和极端强降水过程阈值进行科学界定的基础上,对50 a来极端降水和极端强降水过程进行了常规统计。结果表明:印江极端降水和极端强降水过程有弱的增加趋势,极端(强)降水天数与降水量成正相关,20世纪60年代极端降水天数和极端强降水的离散程度最大,21世纪前8 a极端强降水的离散程度最小。极端降水和极端强降水日数变化均达不到气候突变的标准。     

Analysis of rainfall seasonality from observations and climate models

Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere–ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring a distinct wet and dry season, it is directly related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. Models participating to the Coupled Model Intercomparison Project platform, Phase 5, consistently overestimate the RE over tropical Latin America and underestimate it in West Africa, western Mexico and East Asia. It is demonstrated that positive RE biases in a general circulation model are associated with excessively peaked monthly precipitation fractions, too large during the wet months and too small in the months preceding and following the wet season; negative biases are instead due, in most cases, to an excess of rainfall during the premonsoonal months.     

Future projections of synoptic weather types over the Arabian Peninsula during the twenty-first century using an ensemble of CMIP5 models

An assessment of future change in synoptic conditions over the Arabian Peninsula throughout the twenty-first century was performed using 20 climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. We employed the mean sea level pressure (SLP) data from model output together with NCEP/NCAR reanalysis data and compared the relevant circulation types produced by the Lamb classification scheme for the base period 1975–2000. Overall, model results illustrated good agreement with the reanalysis, albeit with a tendency to underestimate cyclonic (C) and southeasterly (SE) patterns and to overestimate anticyclones and directional flows. We also investigated future projections for each circulation-type during the rainy season (December–May) using three Representative Concentration Pathways (RCPs), comprising RCP2.6, RCP4.5, and RCP8.5. Overall, two scenarios (RCP4.5 and RCP 8.5) revealed a statistically significant increase in weather types favoring above normal rainfall in the region (e.g., C and E-types). In contrast, weather types associated with lower amounts of rainfall (e.g., anticyclones) are projected to decrease in winter but increase in spring. For all scenarios, there was consistent agreement on the sign of change (i.e., positive/negative) for the most frequent patterns (e.g., C, SE, E and A-types), whereas the sign was uncertain for less recurrent types (e.g., N, NW, SE, and W). The projected changes in weather type frequencies in the region can be viewed not only as indicators of change in rainfall response but may also be used to inform impact studies pertinent to water resource planning and management, extreme weather analysis, and agricultural production.     

Long time-scale potential predictability in an ensemble of coupled climate models

A diagnostic multi-model ensemble potential predictability study of surface air temperature is performed using data from nine models participating in the Coupled Model Intercomparison Project (CMIP1). The data are considered to be a sample of results from the population of models embodying current abilities to simulate the climate system and represent a range of numerics, resolution and of physical parametrizations. The potential predictability of pentadal, decadal, and 25-year means is analyzed. The multi-model ensemble provides a statistically stable estimate of the potential predictability variance fraction (ppvf) with a narrow confidence interval. This is not the case for individual models with modest lengths of simulation data nor, by implication, for the instrument-based observational record. Potential predictability is found predominately over the high-latitude oceans. There is evidence also for potential predictability at tropical latitudes in the Pacific and Atlantic, but not the Indian oceans, on the shorter of the time scales. The potential predictability variance fraction decreases with increasing time scale but appreciable values exist at all of the time scales considered, especially for the Southern Ocean and for the North Atlantic. Values over land, while statistically non-zero, are small. The autocorrelation structure of the data is investigated to account for its effect on the statistical estimation of the ppvf and to indicate the extent to which the data reflect simple oceanic damping of white noise atmospheric forcing. Ensemble autocorrelation structures differ between tropical and extra-tropical latitudes (at least on the time scales considered) with more oscillatory behaviour implied in tropical regions compared to high latitudes. It appears that the results are inconsistent with simple ocean damping and that higher order autocorrelation structures of temperature cannot be neglected generally or in the determination of the potential predictability. The statistical results suggest that predictability in the extratropics is associated with long ocean time scales while in the tropics it is associated with the coupled atmosphere-ocean system. Physically based analyses are required to understand this long time scale behaviour and an ensemble view is also needed in order to determine the behaviour that is robust across models and the real system.     

Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections

Seasonal extreme daily precipitation is analyzed in the ensemble of NARCAPP regional climate models. Significant variation in these models’ abilities to reproduce observed precipitation extremes over the contiguous United States is found. Model performance metrics are introduced to characterize overall biases, seasonality, spatial extent and the shape of the precipitation distribution. Comparison of the models to gridded observations that include an elevation correction is found to be better than to gridded observations without this correction. A complicated model weighting scheme based on model performance in simulating observations is found to cause significant improvements in ensemble mean skill only if some of the models are poorly performing outliers. The effect of lateral boundary conditions are explored by comparing the integrations driven by reanalysis to those driven by global climate models. Projected mid-century future changes in seasonal precipitation means and extremes are presented and discussions of the sources of uncertainty and the mechanisms causing these changes are presented.     

Observation-based blended projections from ensembles of regional climate models

We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCMs corresponding to present day conditions, and observational records. Discrepancies are then propagated into the future to obtain high resolution blended projections of 21st century climate. In addition to blended projections, the proposed method provides location-dependent comparisons between the different simulations by estimating the different modes of spatial variability, and using the climate model-specific coefficients of the spatial factors for comparisons. The approach has the flexibility to provide projections at customizable scales of potential interest to stakeholders while accounting for the uncertainties associated with projections at these scales based on a comprehensive statistical framework. We demonstrate the methodology with simulations from the Weather Research & Forecasting regional model (WRF) using three different boundary conditions. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the Special Report on Emissions Scenarios (SRES) A2 emissions scenario, covering 2041 to 2070. We investigate and project yearly mean summer and winter temperatures for a domain in the South West of the United States.     

Changes in the onset and length of seasons from an ensemble of regional climate models over Spain for future climate conditions

This study analyses the length and onset of the four seasons based on the annual climatic cycle of maximum and minimum temperatures. Previous studies focused over climatically homogeneous mid-high latitude areas, employing fixed temperature thresholds (related to climatic features such as freezing point) that can be inadequate when different climate conditions are present. We propose a method related to the daily minimum and maximum temperature 25th and 75th point-dependent climatic percentiles. It is applied to an ensemble of regional climate models (RCMs) of 25-km horizontal resolution over the peninsular Spain and Balearic Islands, where a large variety of climatic regimes, from alpine to semi-desertic conditions, are present. First, baseline climate (1961–2000) ERA40-forced RCM simulations are successfully compared with the Spain02 daily observational database, following astronomical season length (around 90 days). This result confirms the validity of the proposed method and capability of the RCMs to describe the seasonal features. Future climate global climate model-forced RCMs (2071–2100) compared with present climate (1961–1990) simulations indicate the disappearance of winter season, a summer enlargement (onset and end) and a slight spring and autumn increase.     

Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections

This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2–Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981–2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.

     

Improved confidence in climate change projections of precipitation evaluated using daily statistics from the PRUDENCE ensemble

An ensemble of regional climate modelling simulations from the European framework project PRUDENCE are compared across European sub-regions with observed daily precipitation from the European Climate Assessment dataset by characterising precipitation in terms of probability density functions (PDFs). Models that robustly describe the observations for the control period (1961–1990) in given regions as well as across regions are identified, based on the overlap of normalised PDFs, and then validated, using a method based on bootstrapping with replacement. We also compare the difference between the scenario period (2071–2100) and the control period precipitation using all available models. By using a metric quantifying the deviation over the entire PDF, we find a clearly marked increase in the contribution to the total precipitation from the more intensive events and a clearly marked decrease for days with light precipitation in the scenario period. This change is tested to be robust and found in all models and in all sub-regions. We find a detectable increase that scales with increased warming, making the increase in the PDF difference a relative indicator of climate change level. Furthermore, the crossover point separating decreasing from increasing contributions to the normalised precipitation spectrum when climate changes does not show any significant change which is in accordance with expectations assuming a simple analytical fit to the precipitation spectrum.     

Indices for extreme events in projections of anthropogenic climate change

Indices for temperature and precipitation extremes are calculated on the basis of the global climate model ECHAM5/MPI-OM simulations of the twentieth century and SRES A1B and B1 emission scenarios for the twenty-first century. For model evaluation, the simulated indices representing the present climate were compared with indices based on observational data. This comparison shows that the model is able to realistically capture the observed climatological large-scale patterns of temperature and precipitation indices, although the quality of the simulations depends on the index and region under consideration. In the climate projections for the twenty-first century, all considered temperature-based indices, minimum Tmin, maximum Tmax, and the frequency of tropical nights, show a significant increase worldwide. Similarly, extreme precipitation, as represented by the maximum 5-day precipitation and the 95th percentile of precipitation, is projected to increase significantly in most regions of the world, especially in those that are relatively wet already under present climate conditions. Analogously, dry spells increase particularly in those regions that are characterized by dry conditions in present-day climate. Future changes in the indices exhibit distinct regional and seasonal patterns as identified exemplarily in three European regions.     

The simulation of European heat waves from an ensemble of regional climate models within the EURO-CORDEX project

The ability of a large ensemble of regional climate models to accurately simulate heat waves at the regional scale of Europe was evaluated. Within the EURO-CORDEX project, several state-of-the art models, including non-hydrostatic meso-scale models, were run for an extended time period (20 years) at high resolution (12 km), over a large domain allowing for the first time the simultaneous representation of atmospheric phenomena over a large range of spatial scales. Eight models were run in this configuration, and thirteen models were run at a classical resolution of 50 km. The models were driven with the same boundary conditions, the ERA-Interim re-analysis, and except for one simulation, no observations were assimilated in the inner domain. Results, which are compared with daily temperature and precipitation observations (ECA&D and E-OBS data sets) show that, even forced by the same re-analysis, the ensemble exhibits a large spread. A preliminary analysis of the sources of spread, using in particular simulations of the same model with different parameterizations, shows that the simulation of hot temperature is primarily sensitive to the convection and the microphysics schemes, which affect incoming energy and the Bowen ratio. Further, most models exhibit an overestimation of summertime temperature extremes in Mediterranean regions and an underestimation over Scandinavia. Even after bias removal, the simulated heat wave events were found to be too persistent, but a higher resolution reduced this deficiency. The amplitude of events as well as the variability beyond the 90th percentile threshold were found to be too strong in almost all simulations and increasing resolution did not generally improve this deficiency. Resolution increase was also shown to induce large-scale 90th percentile warming or cooling for some models, with beneficial or detrimental effects on the overall biases. Even though full causality cannot be established on the basis of this evaluation work, the drivers of such regional differences were shown to be linked to changes in precipitation due to resolution changes, affecting the energy partitioning. Finally, the inter-annual sequence of hot summers over central/southern Europe was found to be fairly well simulated in most experiments despite an overestimation of the number of hot days and of the variability. The accurate simulation of inter-annual variability for a few models is independent of the model bias. This indicates that internal variability of high summer temperatures should not play a major role in controlling inter-annual variability. Despite some improvements, especially along coastlines, the analyses conducted here did not allow us to generally conclude that a higher resolution is clearly beneficial for a correct representation of heat waves by regional climate models. Even though local-scale feedbacks should be better represented at high resolution, combinations of parameterizations have to be improved or adapted accordingly.     

A multi-physics ensemble of regional climate change projections over the Iberian Peninsula

This study illustrates the sensitivity of regional climate change projections to the model physics. A single-model (MM5) multi-physics ensemble of regional climate simulations over the Iberian Peninsula for present (1970–1999) and future (2070–2099 under the A2 scenario) periods is assessed. The ensemble comprises eight members resulting from the combination of two options of parameterization schemes for the planetary boundary layer, cumulus and microphysics. All the considered combinations were previously evaluated by comparing hindcasted simulations to observations, none of them providing clearly outlying climates. Thus, the differences among the various ensemble members (spread) in the future projections could be considered as a matter of uncertainty in the change signals (as similarly assumed in multi-model studies). The results highlight the great dependence of the spread on the synoptic conditions driving the regional model. In particular, the spread generally amplifies under the future scenario leading to a large spread accompanying the mean change signals, as large as the magnitude of the mean projected changes and analogous to the spread obtained in multi-model ensembles. Moreover, the sign of the projected change varies depending on the choice of the model physics in many cases. This, together with the fact that the key mechanisms identified for the simulation of the climatology of a given period (either present or future) and those introducing the largest spread in the projected changes differ significantly, make further claims for efforts to better understand and model the parameterized subgrid processes.