Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.     

An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC

Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.     

Calibrated multi-model ensemble summer temperature predictions over Italy

A statistical calibration scheme is applied to multi-model global seasonal ensemble reforecasts in order to predict the interannual variability of summer averaged surface maximum temperature over Italy. In some cases, this technique is shown to be able to improve the skill scores of the seasonal predictions during the last 35 years, with respect to the direct model output (DMO), using seasonal predictions initialised 1 month before the beginning of the season. It is shown that the presence of some skill in the DMO multi-model predictions is mostly due to the correct prediction of the observed secular trends in maximum temperature, and, partly, to the correct prediction of outliers, in particular, of the summer of 2003. At the same time, while the removal of trends produces a small reduction of skill in both the raw and calibrated predictions, the removal of outliers improves the performance of the calibration scheme. Once all trends and outliers are removed, the DMO predictions have no skill, while the calibrated predictions still present a detectable skill. The improvement introduced by the calibration are shown to be statistically significant by applying resampling techniques. It is shown that the reason of this partial success is linked to the fact that although the models present several shortcomings, some models can capture the existence of a weak large-scale signal, possibly linked with the presence of a summer teleconnection between the equatorial Pacific and Europe, with a spatial pattern substantially different from that associated with the temperature secular trend. The teleconnection is associated with a modulation of the quasi-stationary barotropic eddies in the Northern Hemisphere extra-tropics.     

European summer climate variability in a heterogeneous multi-model ensemble

Recent results from an enhanced greenhouse-gas scenario over Europe suggest that climate change might not only imply a general mean warming at the surface, but also a pronounced increase in interannual surface temperature variability during the summer season (Schär et al., Nature 427:332–336, 2004). It has been proposed that the underlying physical mechanism is related to land surface-atmosphere interactions. In this study we expand the previous analysis by including results from a heterogeneous ensemble of 11 high-resolution climate models from the PRUDENCE project. All simulations considered comprise 30-year control and enhanced greenhouse-gas scenario periods. While there is considerable spread in the models’ ability to represent the observed summer variability, all models show some increase in variability for the scenario period, confirming the main result of the previous study. Averaged over a large-scale Central European domain, the models simulate an increase in the standard deviation of summer mean temperatures between 20 and 80%. The amplification occurs predominantly over land points and is particularly pronounced for surface temperature, but also evident for precipitation. It is also found that the simulated changes in Central European summer conditions are characterized by an emergence of dry and warm years, with early and intensified depletion of root-zone soil moisture. There is thus some evidence that the change in variability may be linked to the dynamics of soil-moisture storage and the associated feedbacks on the surface energy balance and precipitation.     

A multi-model ensemble approach for assessment of climate change impact on surface winds in France

Statistical downscaling of 14 coupled atmosphere-ocean general circulation models (AOGCM) is presented to assess potential changes of the 10 m wind speeds in France. First, a statistical downscaling method is introduced to estimate daily mean 10 m wind speed at specific sites using general circulation model output. Daily 850 hPa wind field has been selected as the large scale circulation predictor. The method is based on a classification of the daily wind fields into a few synoptic weather types and multiple linear regressions. Years are divided into an extended winter season from October to March and an extended summer season from April to September, and the procedure is conducted separately for each season. ERA40 reanalysis and observed station data have been used to build and validate the downscaling algorithm over France for the period 1974–2002. The method is then applied to 14 AOGCMs of the coupled model intercomparison project phase 3 (CMIP3) multi-model dataset. Three time periods are focused on: a historical period (1971–2000) from the climate of the twentieth century experiment and two climate projection periods (2046–2065 and 2081–2100) from the IPCC SRES A1B experiment. Evolution of the 10 m wind speed in France and associated uncertainties are discussed. Significant changes are depicted, in particular a decrease of the wind speed in the Mediterranean area. Sources of those changes are investigated by quantifying the effects of changes in the weather type occurrences, and modifications of the distribution of the days within the weather types.     

Examination of multi-model ensemble seasonal prediction methods using a simple climate system

A simple climate model was designed as a proxy for the real climate system, and a number of prediction models were generated by slightly perturbing the physical parameters of the simple model. A set of long (240 years) historical hindcast predictions were performed with various prediction models, which are used to examine various issues of multi-model ensemble seasonal prediction, such as the best ways of blending multi-models and the selection of models. Based on these results, we suggest a feasible way of maximizing the benefit of using multi models in seasonal prediction. In particular, three types of multi-model ensemble prediction systems, i.e., the simple composite, superensemble, and the composite after statistically correcting individual predictions (corrected composite), are examined and compared to each other. The superensemble has more of an overfitting problem than the others, especially for the case of small training samples and/or weak external forcing, and the corrected composite produces the best prediction skill among the multi-model systems.     

Gradient in the climate change signal of European discharge predicted by a multi-model ensemble

In order to perform hydrological studies on the PRUDENCE regional climate model (RCM) simulations, a special focus was put on the discharge from large river catchments located in northern and central Europe. The discharge was simulated with a simplified land surface (SL) scheme and the Hydrological Discharge (HD) model. The daily fields of precipitation, 2 m temperature and evapotranspiration from the RCM simulations were used as forcing. Therefore the total catchment water balances are constrained by the hydrological cycle of the different RCMs. The validation of the simulated hydrological cycle from the control simulations shows that the multi-model ensemble mean is closer to the observations than each of the models, especially if different catchments and hydrological variables are considered. Therefore, the multi-model ensemble mean can be used to largely reduce the uncertainty that is introduced by a single RCM. This also provides more confidence in the future projections for the multi-model ensemble means. The scenario simulations predict a gradient in the climate change signal over Northern and Central Europe. Common features are the overall warming and the general increase of evapotranspiration. But while in the northern parts the warming will enhance the hydrological cycle leading to an increased discharge, the large warming, especially in the summer, will slow down the hydrological cycle caused by a drying in the central parts of Europe which is accompanied by a reduction of discharge. The comparison of the changes predicted by the multi-model ensemble mean to the changes predicted by the driving GCM indicates that the RCMs can compensate problems that a driving GCM may have with local scale processes or parameterizations.     

Decadal prediction skill in a multi-model ensemble

Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p?≈?0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5?years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p?≈?0.2). Furthermore, uninitialised simulations that include volcanic aerosols have similar skill. It is therefore still an open question whether initialisation improves predictions of Sahel rainfall. We conclude that the main source of skill in forecasting temperature is the trend forced by rising greenhouse gas concentrations. The ocean initial state contributes to skill in some regions, but variations in boundary forcings such as aerosols are as important in decadal forecasting.     

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

     

Quantitative assessment of the climate components driving the pacific decadal oscillation in climate models

The Pacific decadal oscillation (PDO) is defined as the first empirical orthogonal function (EOF) mode of the North Pacific sea surface temperature anomalies. In this study, we reconstructed the PDO using the first-order autoregressive model from various climate indices representing the El Niño-Southern oscillation (ENSO), Aleutian Low (AL), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio extension (KOE) region. The climate indices were obtained from observation and twentieth-century simulations of the eight coupled general circulation models (CGCMs) participating in the Climate Model Intercomparison Project Phase III (CMIP3). In this manner, we quantitatively assessed the major climate components generating the PDO using observation and models. Based on observations, the PDO pattern in the central to eastern North Pacific was accurately reconstructed by the AL and ENSO indices, and that in the western North Pacific was best reconstructed by the SSH and thermocline indices. In the CMIP3 CGCMs, the relative contribution of each component to the generation of the PDO varied greatly from model to model, and observations, although the PDO patterns from most of the models were similar to the pattern observed. In the models, the PDO pattern in the eastern and western North Pacific were well reconstructed using the AL and SSH indices, respectively. However, the PDO pattern reconstructed by the ENSO index was quite different from the observed pattern, which was possibly due to the model's common deficiency in simulating the amplitude and location of the ENSO. Furthermore, the differences in the contribution of the KOE thermocline index between the observed pattern and most of the models indicated that the PDO pattern associated with ocean wave dynamics is not properly simulated by most models. Therefore, the virtually well simulated PDO pattern by models is a result of physically inconsistent processes.     

An assessment of global and regional climate change based on the EH5OM climate model ensemble

An analysis of climate change for global domain and for the European/Mediterranean region between the two periods, 1961–1990 (representing the twentieth century or “present” climate) and 2041–2070 (representing future climate), from the three-member ensemble of the EH5OM climate model under the IPCC A2 scenario was performed. Ensemble averages for winter and summer seasons were considered, but also intra-ensemble variations and the change of interannual variability between the two periods. First, model systematic errors are assessed because they could be closely related to uncertainties in climate change. A strengthening of westerlies (zonalization) over the northern Europe is associated with an erroneous increase in MSLP over the southern Europe. This increase in MSLP is related to a (partial) suppression of summer convective precipitation. Global warming in future climate is relatively uniform in the upper troposphere and it is associated with a 10% wind increase in the subtropical jet cores. However, spatial irregularities in the low-level temperature signal single out some regions as particularly sensitive to climate change. For Europe, the largest near-surface temperature increase in winter is found over its north-eastern part (more than 3°C), and the largest summer warming (over 3.5°C) is over south Europe. For south Europe, the increase in temperature averages is almost an order of magnitude larger than the increase in interannual variability. The magnitude of the warming is larger than the model systematic error, and the spread among the three model realisations is much smaller than the magnitude of climate change. This further supports the significance of estimated future temperature change. However, this is not the case for precipitation, implying therefore larger uncertainties for precipitation than for temperature in future climate projections.     

Ethiopian decadal climate variability

Ethiopian decadal climate variability is characterized by application of singular value decomposition to gridded rainfall data over the period 1901–2007. Two distinct modes are revealed with different annual cycles and opposing responses to regional and global forcing. The northern zone that impacts the Nile River and underlies the tropical easterly jet has a unimodal rainy season that is enhanced by Atlantic Multidecadal Oscillation warm phase. This rainfall mode is linked with the Atlantic zonal overturning circulation and exhibits 10–12-year cycles through much of the twentieth century. The southern zone has a bimodal rainy season that is enhanced by Pacific Decadal Oscillation cool phase and the southern meridional overturning circulation. Multiyear wet and dry spells are characterized by sympathetic responses in the near-equatorial trough extending from Central America across the African Sahel to Southeast Asia. The interaction of Walker and Hadley cells over Africa appears to be a key feature that modulates Ethiopian climate at decadal frequency through anomalous north–south displacement of the near-equatorial trough.     

A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

Decadal fingerprints of freshwater discharge around Greenland in a multi-model ensemble

The recent increase in the rate of the Greenland ice sheet melting has raised with urgency the question of the impact of such a melting on the climate. As former model projections, based on a coarse representation of the melting, show very different sensitivity to this melting, it seems necessary to consider a multi-model ensemble to tackle this question. Here we use five coupled climate models and one ocean-only model to evaluate the impact of 0.1 Sv (1 Sv = 10⁶ m³/s) of freshwater equally distributed around the coast of Greenland during the historical era 1965–2004. The ocean-only model helps to discriminate between oceanic and coupled responses. In this idealized framework, we find similar fingerprints in the fourth decade of hosing among the models, with a general weakening of the Atlantic Meridional Overturning Circulation (AMOC). Initially, the additional freshwater spreads along the main currents of the subpolar gyre. Part of the anomaly crosses the Atlantic eastward and enters into the Canary Current constituting a freshwater leakage tapping the subpolar gyre system. As a consequence, we show that the AMOC weakening is smaller if the leakage is larger. We argue that the magnitude of the freshwater leakage is related to the asymmetry between the subpolar-subtropical gyres in the control simulations, which may ultimately be a primary cause for the diversity of AMOC responses to the hosing in the multi-model ensemble. Another important fingerprint concerns a warming in the Nordic Seas in response to the re-emergence of Atlantic subsurface waters capped by the freshwater in the subpolar gyre. This subsurface heat anomaly reaches the Arctic where it emerges and induces a positive upper ocean salinity anomaly by introducing more Atlantic waters. We found similar climatic impacts in all the coupled ocean–atmosphere models with an atmospheric cooling of the North Atlantic except in the region around the Nordic Seas and a slight warming south of the equator in the Atlantic. This meridional gradient of temperature is associated with a southward shift of the tropical rains. The free surface models also show similar sea-level fingerprints notably with a comma-shape of high sea-level rise following the Canary Current.     

Preliminary evaluations of FGOALS-g2 for decadal predictions

The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature (SAT), particularly in that of Nin o3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate (the control) by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 (LICOM2). The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.     

CMIP5多模式集合对江苏省气候变化模拟评估及情景预估

利用中国气象局所属的2 400余个台站观测资料制作的分辨率为0.25°×0.25°数据集中的气温、降水量资料评估了CMIP5中17个模式对于1961—2004年江苏省气温和降水量空间分布特征的模拟能力,筛选出了5个对江苏省气候特征模拟较好的模式。之后基于5个优选模式集合平均的结果预估了3种典型浓度路径(Representative Concentration Pathways,RCPs)下江苏省2006—2100年的气温和降水量变化趋势。结果表明:(1)全球耦合气候模式对江苏省的气温和降水量空间分布特征具有一定的模拟能力,并且模式集合平均的气温和降水量与观测资料的空间相关系数分别为0.85和0.93;(2)在低浓度路径(RCP2.6)、中浓度路径(RCP4.5)和高浓度路径(RCP8.5)3种温室气体排放情景下,江苏省2006—2100年的地表温度均呈现明显的增温趋势,并且苏北的增温幅度要高于苏南;(3)3种温室气体排放情景下,江苏省未来百年降水量均呈现出北方增多南方减少的趋势;(4)未来百年江苏省降水量随气温变化的趋势并不稳定,RCP2.6和RCP4.5情景下降水量随气温的升高而增加,而RCP8.5情景下降水量随气温的增加而减少。