Current and future atmospheric circulation at 500 hPa over Greenland simulated by the CMIP3 and CMIP5 global models

The Greenland ice sheet is projected to be strongly affected by global warming. These projections are either issued from downscaling methods (such as Regional Climate Models) or they come directly from General Circulation Models (GCMs). In this context, it is necessary to evaluate the accuracy of the daily atmospheric circulation simulated by the GCMs, since it is used as forcing for downscaling methods. Thus, we use an automatic circulation type classification based on two indices (Euclidean distance and Spearman rank correlation using the daily 500 hPa geopotential height) to evaluate the ability of the GCMs from both CMIP3 and CMIP5 databases to simulate the main circulation types over Greenland during summer. For each circulation type, the GCMs are compared to three reanalysis datasets on the basis of their frequency and persistence differences. For the current climate (1961–1990), we show that most of the GCMs do not reproduce the expected frequency and the persistence of the circulation types and that they simulate poorly the observed daily variability of the general circulation. Only a few GCMs can be used as reliable forcings for downscaling methods over Greenland. Finally, when applying the same approach to the future projections of the GCMs, no significant change in the atmospheric circulation over Greenland is detected, besides a generalised increase of the geopotential height due to a uniform warming of the atmosphere.     

Drivers of mean climate change around the Netherlands derived from CMIP5

For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.     

Improved atmospheric circulation over Europe by the new generation of CMIP6 earth system models

Global Climate Models (GCMs) generally exhibit significant biases in the representation of large-scale atmospheric circulation. Even after a sensible bias adjustment these errors remain and are inherited to some extent by the derived downscaling products, impairing the credibility of future regional projections. In this study we perform a process-based evaluation of state-of-the-art GCMs from CMIP5 and CMIP6, with a focus on the simulation of the synoptic climatological patterns having a most prominent effect on the European climate. To this aim, we use the Lamb Weather Type Classification (LWT, Lamb British isles weather types and a register of the daily sequence 736 of circulation patterns 1861-1971. METEOROL OFF, GEOPHYS MEM; 737 GB; DA 1972; NO 116; PP 1-85; BIBL 2P1/2, 1972), a subjective classification of circulation weather types constructed upon historical simulations of daily mean sea level pressure. Observational uncertainty has been taken into account by considering four different reanalysis products of varying characteristics. Our evaluation unveils an overall improvement of salient atmospheric circulation features consistent across observational references, although this is uneven across models and large frequency biases still remain for the main LWTs. Some CMIP6 models attain similar or even worse results than their CMIP5 counterparts, although in most cases consistent improvements have been found, demonstrating the ability of the new models to better capture key synoptic conditions. In light of the large differences found across models, we advocate for a careful selection of driving GCMs in downscaling experiments with a special focus on large-scale atmospheric circulation aspects.

     

Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.     

Evaluation of CMIP5 dynamic sea surface height multi-model simulations against satellite observations

We evaluate the representation of dynamic sea surface height (SSH) fields of 33 global coupled models (GCMs) contributed to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use observations from satellite altimetry and basic performance metrics to quantify the ability of the GCMs to replicate observed SSH of the time-mean, seasonal cycle, and inter-annual variability patterns. The time-mean SSH representation has markedly improved from CMIP3 to CMIP5, both in terms of overall reduction in root-mean square differences, and in terms of reduced GCM ensemble spread. Biases of the time-mean SSH field in the Indian and Pacific Ocean equatorial regions are consistent with biases in the zonal surface wind stress fields identified with independent measurements. In the Southern Ocean, the latitude of the maximum meridional gradient of the zonal mean SSH CMIP5 models is shifted equatorward, consistent with the GCMs’ spatial biases in the maximum of the zonal mean westerly surface wind stress fields. However, while the Southern Ocean SSH gradients correlate well with the maximum Antarctic circumpolar current transports, there is no significant correlation with the maximum zonal mean wind stress amplitudes, consistent with recent findings that the eddy parameterisations in GCMs dominate over wind stress amplitudes in this region. There is considerable spread across the CMIP5 ensemble for the seasonal and interannual SSH variability patterns. Because of the short observational period, the interannual variability patterns depend on the time-period over which they are derived, while no such dependency is found for the time-mean patterns. The model performance metrics for SSH presented here provide insight into GCM shortcoming due to inadequate model physics or processes. While the diagnostics of CMIP5 GCM performance relative to observations reveal that some models are clearly better than others, model performance is sensitive to the spatio-temporal scales chosen.     

FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model Intercomparison Project

The Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP) is an endorsed Model Intercomparison Project in phase 6 of the Coupled Model Intercomparison Project(CMIP6). The goal of FAFMIP is to investigate the spread in the atmosphere–ocean general circulation model projections of ocean climate change forced by increased CO₂, including the uncertainties in the simulations of ocean heat uptake, global mean sea level rise due to ocean thermal expansion and dynamic sea level change due...     

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.     

中国CMIP5模式对未来北极海冰的模拟偏差

本文对中国参加CMIP5的6个气候模式对未来北极海冰的模拟情况进行了评估。通过与1979-2005年海冰的观测值以及2050年代的多模式集合平均值对比发现,中国的气候模式对海冰范围的模拟结果与CMIP5模式的平均水平存在一定差距,具体表现为：BNU-ESM和FGOALS-s2对当前海冰范围估计很好,但对温度敏感性略偏高;FIO-ESM对当前海冰范围估计很好,但由于海冰对温度的敏感性偏低,导致其模拟的未来海冰在各种RCP情景中都融化缓慢;FGOALS-g2（BCC-CSM1-1和BCC-CSM1-1-m）对当前海冰范围的模拟存在显著偏多（显著偏少）的问题,这导致其对未来海冰融化的估计也持续偏多（偏少）。中国模式对北极海冰的模拟偏差导致它们对极区地表大气温度和湿度的模拟出现偏差,并且这些极区气象要素的偏差会进一步通过动力过程传导到对秋、冬季西风带、极涡的模拟中去。研究表明：从对海冰本身的模拟以及海冰偏差带来的气候影响这两个角度看,BNU-ESM在中国模式中水平较高,但总体上中国6个气候模式在海冰分量的模拟上仍与世界平均水平存在差距,这需要中国各模式中心的持续改进。     

SST and ENSO variability and change simulated in historical experiments of CMIP5 models

This work documents the diversity in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models in simulating different aspects of sea surface temperature (SST) variability, particularly those associated with the El Niño–Southern Oscillation (ENSO), as well as the impact of low-frequency variations on the ENSO variability and its global teleconnection. The historical simulations (1870–2005) include 10 models with ensemble member ranging from 3 to 10 that are forced with observed atmospheric composition changes reflecting both natural and anthropogenic forcings. It is shown that the majority of the CMIP5 models capture the relative large SST anomaly variance in the tropical central and eastern Pacific, as well as in North Pacific and North Atlantic. The frequency of ENSO is not well captured by almost all models, particularly for the period of 5–6 years. The low-frequency variations in SST caused by external forcings affect the SST variability and also modify the global teleconnection of ENSO. The models reproduce the global averaged SST low-frequency variations, particularly since 1970s. However, majority of the models are unable to correctly simulate the spatial pattern of the observed SST trends. These results suggest that it is still a challenge to reproduce the features of global historical SST variations with the state-of-the-art coupled general circulation model.     

Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis

The Mediterranean region is identified as one of the two main hot-spots of climate change and also known to have the highest concentration of cyclones in the world. These atmospheric features contribute significantly to the regional climate but they are not reproduced by the Atmosphere–Ocean General Circulation Models (AOGCM), due to their coarse horizontal resolution, which have recently been run in the frame of the 5th Climate Model Intercomparison Project. This article investigates the benefit of dynamically downscaling the Institut Pierre Simon Laplace (IPSL) AOGCM (IPSL-CM5) historical simulation by the weather and research forecasting (WRF) for the representation of the Mediterranean surface winds and cyclonic activity. Indeed, when considering IPSL-CM5 atmospheric fields, the dramatic underestimation of the cyclonic activity in the most cyclogenetic region of the world jeopardizes our ability to investigate in-depth the Mediterranean regional climate and trend in the context of global change. The WRF model shows remarkable skill to reproduce regional cyclogenesis. Indeed, cyclones occurrence is quasi-absent in IPSL-CM5 data but when applying dynamical downscaling their spatial–temporal variability is very close to the re-analysis. This is a clear benefit of dynamical downscaling in regions of strong topographic forcing. This “steady” source of forcing allows the production of lee cyclogenesis and the development of strong cyclones, whatever the quality of the large-scale circulation provided at the WRF’s boundaries by IPSL-CM5. However, dynamical downscaling still presents disadvantages as for instance the fact that large-scale inaccurate features of the IPSL-CM5 regional circulation are replicated by WRF due to the boundary controlled (small domain) simulation. The advantages and disadvantages of dynamical downscaling are thoroughly discussed in this paper revealing its importance for climate research, especially in the context of future scenarios and wind impacts.     

Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3

The present study aims at evaluating and comparing precipitation over the Amazon in two sets of historical and future climate simulations based on phase 3 (CMIP3) and 5 (CMIP5) of the Coupled Model Intercomparison Project. Thirteen models have been selected in order to discuss (1) potential improvements in the simulation of present-day climate and (2) the potential reduction in the uncertainties of the model response to increasing concentrations of greenhouse gases. While several features of present-day precipitation—including annual cycle, spatial distribution and co variability with tropical sea surface temperature (SST)—have been improved, strong uncertainties remain in the climate projections. A closer comparison between CMIP5 and CMIP3 highlights a weaker consensus on increased precipitation during the wet season, but a stronger consensus on a drying and lengthening of the dry season. The latter response is related to a northward shift of the boreal summer intertropical convergence zone in CMIP5, in line with a more asymmetric warming between the northern and southern hemispheres. The large uncertainties that persist in the rainfall response arise from contrasted anomalies in both moisture convergence and evapotranspiration. They might be related to the diverse response of tropical SST and ENSO (El Niño Southern Oscillation) variability, as well as to spurious behaviours among the models that show the most extreme response. Model improvements of present-day climate do not necessarily translate into more reliable projections and further efforts are needed for constraining the pattern of the SST response and the soil moisture feedback in global climate scenarios.     

Climate change hotspots over South America: from CMIP3 to CMIP5 multi-model datasets

This study identifies possible hotspots of climate change in South America through an examination of the spatial pattern of the Regional Climate Change Index (RCCI) over the region by the end of the twenty-first century. The RCCI is a qualitative index that can synthesize a large number of climate model projections, and it is suitable for identifying those regions where climate change could be more pronounced in a warmer climate. The reliability and uncertainties of the results are evaluated by using numerous state-of-the-art general circulation models (GCMs) and forcing scenarios from the Coupled Model Intercomparison Project phases 3 and 5. The results show that southern Amazonia and the central-western region and western portion of Minas Gerais state in Brazil are persistent climate change hotspots through different forcing scenarios and GCM datasets. In general, as the scenarios vary from low- to high-level forcing, the area of high values of RCCI increase and the magnitude intensify from central-western and southeast Brazil to northwest South America. In general, the climatic hotspots identified in this study are characterized by an increase of mean surface air temperature, mainly in the austral winter; by an increase of interannual temperature variability, predominantly in the austral summer; and by a change in the mean and interannual variability of precipitation during the austral winter.     

An evaluation of the CMIP3 and CMIP5 simulations in their skill of simulating the spatial structure of SST variability

The natural sea surface temperature (SST) variability in the global oceans is evaluated in simulations of the Climate Model Intercomparison Project Phase 3 (CMIP3) and CMIP5 models. In this evaluation, we examine how well the spatial structure of the SST variability matches between the observations and simulations on the basis of their leading empirical orthogonal functions-modes. Here we focus on the high-pass filter monthly mean time scales and the longer 5 years running mean time scales. We will compare the models and observations against simple null hypotheses, such as isotropic diffusion (red noise) or a slab ocean model, to illustrate the models skill in simulating realistic patterns of variability. Some models show good skill in simulating the observed spatial structure of the SST variability in the tropical domains and less so in the extra-tropical domains. However, most models show substantial deviations from the observations and from each other in most domains and particularly in the North Atlantic and Southern Ocean on the longer (5 years running mean) time scale. In many cases the simple spatial red noise null hypothesis is closer to the observed structure than most models, despite the fact that the observed SST variability shows significant deviations from this simple spatial red noise null hypothesis. The CMIP models tend to largely overestimate the effective spatial number degrees of freedom and simulate too strongly localized patterns of SST variability at the wrong locations with structures that are different from the observed. However, the CMIP5 ensemble shows some improvement over the CMIP3 ensemble, mostly in the tropical domains. Further, the spatial structure of the SST modes of the CMIP3 and CMIP5 super ensemble is more realistic than any single model, if the relative explained variances of these modes are scaled by the observed eigenvalues.     

Surface Temperature Changes Projected by FGOALS Models under Low Warming Scenarios in CMIP5 and CMIP6

To meet the low warming targets proposed in the 2015 Paris Agreement,substantial reduction in carbon emissions is needed in the future.It is important to know how surface climates respond under low warming targets.The present study investigates the surface temperature changes under the low-forcing scenario of Representative Concentration Pathways(RCP2.6)and its updated version(Shared Socioeconomic Pathways,SSP1-2.6)by the Flexible Global Ocean-Atmosphere-Land System(FGOALS)models participating in phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6,respectively).In both scenarios,radiative forcing(RF)first increases to a peak of 3 W m^?2 around 2045 and then decreases to 2.6 W m^?2 by 2100.Global mean surface air temperature rises in all FGOALS models when RF increases(RF increasing stage)and declines or holds nearly constant when RF decreases(RF decreasing stage).The surface temperature change is distinct in its sign and magnitude between the RF increasing and decreasing stages over the land,Arctic,North Atlantic subpolar region,and Southern Ocean.Besides,the regional surface temperature change pattern displays pronounced model-to-model spread during both the RF increasing and decreasing stages,mainly due to large intermodel differences in climatological surface temperature,ice-albedo feedback,natural variability,and Atlantic Meridional Overturning Circulation change.The pattern of tropical precipitation change is generally anchored by the spatial variations of relative surface temperature change(deviations from the tropical mean value)in the FGOALS models.Moreover,the projected changes in the updated FGOALS models are closer to the multi-model ensemble mean results than their predecessors,suggesting that there are noticeable improvements in the future projections of FGOALS models from CMIP5 to CMIP6.     

Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin,China

Theoretical and Applied Climatology - This study compared precipitation projections of CMIP5 and CMIP6 GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global...     

Interannual variability associated with ENSO: present and future climate projections of RegCM4 for South America-CORDEX domain

Interannual variability over South America (SA) is mainly controlled by the El Niño-Southern Oscillation (ENSO) phenomenon. This study investigates the ENSO precipitation signal during austral spring (September–October–November-SON) over SA. Three global circulation models-GCMs-(MPI, GFDL and HadGEM2) are used for RegCM4 (Regional Climate Model version 4) downscaling of the present (1975–2005) near-future (2020–2050) and far-future (2070–2098) climates using two greenhouse gas stabilization scenarios (RCP4.5 and RCP8.5). For the present climate, only HadGEM2 simulates a frequency of El Niño (EN) and La Niña (LN) years similar to the observations. In terms of ENSO frequency changes, only in the far-future RCP8.5 climate there is greater agreement among GCMs, indicating an increase (decrease) of EN (LN) years. In the present climate, validation indicates that only the RegCM4 ensemble mean provides acceptable precipitation biases (smaller than ±20 %) in the two investigated regions. In this period, the GCMs and RegCM4 agree on the relationship between ENSO and precipitation in SA, i.e., both are able to capture the observed regions of positive/negative rainfall anomalies during EN years, with RegCM4 improving on the GCMs’ signal over southeastern SA. For the near and far future climates, in general, the projections indicate an increase (decrease) of precipitation over southeastern SA (northern-northeastern SA). However, the relationship between ENSO and rainfall in most of RegCM4 and GCM members is weaker in the near and far future climates than in the present day climate. This is likely connected with the GCMs’ projection of the more intense ENSO signal displaced to the central basin of Pacific Ocean in the far future compared to present climate.     

Relative performance of CMIP5 and CMIP6 models in simulating rainfall in Peninsular Malaysia

Theoretical and Applied Climatology - This study evaluated the skills of global climate models (GCMs) of the fifth and sixth Coupled Model Intercomparison Project (CMIP5 and CMIP6) in simulating...     

Long-term Regional Dynamic Sea Level Changes from CMIP6 Projections

Anthropogenic climate forcing will cause the global mean sea level to rise over the 21st century.However,regional sea level is expected to vary across ocean basins,superimposed by the influence of natural internal climate variability.Here,we address the detection of dynamic sea level(DSL)changes by combining the perspectives of a single and a multimodel ensemble approach(the 50-member CanESM5 and a 27-model ensemble,respectively,all retrieved from the CMIP6 archive),under three CMIP6 projected scenarios:SSP1-2.6,SSP3-7.0 and SSP5-8.5.The ensemble analysis takes into account four key metrics:signal(S),noise(N),S/N ratio,and time of emergence(ToE).The results from both sets of ensembles agree in the fact that regions with higher S/N(associated with smaller uncertainties)also reflect earlier ToEs.The DSL signal is projected to emerge in the Southern Ocean,Southeast Pacific,Northwest Atlantic,and the Arctic.Results common for both sets of ensemble simulations show that while S progressively increases with increased projected emissions,N,in turn,does not vary substantially among the SSPs,suggesting that uncertainty arising from internal climate variability has little dependence on changes in the magnitude of external forcing.Projected changes are greater and quite similar for the scenarios SSP3-7.0 and SSP5-8.5 and considerably smaller for the SSP1-2.6,highlighting the importance of public policies towards lower emission scenarios and of keeping emissions below a certain threshold.     

Projecting twenty-first century regional sea-level changes

We present regional sea-level projections and associated uncertainty estimates for the end of the 21 ^st century. We show regional projections of sea-level change resulting from changing ocean circulation, increased heat uptake and atmospheric pressure in CMIP5 climate models. These are combined with model- and observation-based regional contributions of land ice, groundwater depletion and glacial isostatic adjustment, including gravitational effects due to mass redistribution. A moderate and a warmer climate change scenario are considered, yielding a global mean sea-level rise of 0.54 ±0.19 m and 0.71 ±0.28 m respectively (mean ±1σ). Regionally however, changes reach up to 30 % higher in coastal regions along the North Atlantic Ocean and along the Antarctic Circumpolar Current, and up to 20 % higher in the subtropical and equatorial regions, confirming patterns found in previous studies. Only 50 % of the global mean value is projected for the subpolar North Atlantic Ocean, the Arctic Ocean and off the western Antarctic coast. Uncertainty estimates for each component demonstrate that the land ice contribution dominates the total uncertainty.     

A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in the Australian region

 The study seeks to describe one method of deriving information about local daily temperature extremes from larger scale atmospheric flow patterns using statistical tools. This is considered to be one step towards downscaling coarsely gridded climate data from global climate models (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch between GCMs and climate impact models which need information on spatial scales that the GCMs cannot provide. The method of statistical downscaling is based on physical interaction between atmospheric processes with different spatial scales, in this case between synoptic scale mean sea level pressure (MSLP) fields and local temperature extremes at several stations in southeast Australia. In this study it was found that most of the day-to-day spatial variability of the synoptic circulation over the Australian region can be captured by six principal components. Using the scores of these PCs as multivariate indicators of the circulation a substantial part of the local daily temperature variability could be explained. The inclusion of temperature persistence noticeably improved the performance of the statistical model. The model established and tested with observations is thought to be finally applied to GCM-simulated pressure fields in order to estimate pressure-related changes in local temperature extremes under altered CO₂ conditions. Received: 26 March 1996 / Accepted: 20 September 1996