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This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 33 models from the CMIP phase 6 (CMIP6) to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m. The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth. However, this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean, with a global average of −0.44 g kg−1 for the sea surface salinity (SSS) and −0.26 g kg−1 for the 0–1000-m averaged salinity (S1000) compared with the CMIP5 multimodel mean (−0.25 g kg−1 for the SSS and −0.07 g kg−1 for the S1000). In terms of the seasonal variation, both CMIP6 and CMIP5 models show positive (negative) anomalies in the first (second) half of the year in the global average SSS and S1000. The model-simulated variation in SSS is consistent with the observations, but not for S1000, suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity. The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S–20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans. These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle. [ABSTRACT FROM AUTHOR]

This paper has examined the relative significance of uncertainty in future climate projections from a subset of the coupled model intercomparison project phase 5 (CMIP5) global climate models for the Prairie Provinces of western Canada. This was undertaken by determining: (a) the contribution of model and scenario uncertainty and natural variability to the total variance of these future projections, and (b) the timing of climate signal emergence from the background noise of natural climate variability. We examined future projections of mean temperature, precipitation and summer climate moisture index (CMI). In this region, natural climate variability plays an important role in future uncertainty until the end of this century, particularly for precipitation and to a lesser extent, summer CMI. Model uncertainty also contributes to total uncertainty for these variables throughout this century, while scenario uncertainty becomes more important towards the end of the century. For the region as a whole, significant climate change (i.e., signal/noise >2) occurs earliest for summer mean temperature, with median time of emergence around 2035 for the RCP8.5 radiative forcing scenario. Although the median precipitation signal emerges from the noise (i.e., signal/noise >1) around the 2070s in winter and the 2080s in spring, significant values do not occur in any season for this variable before 2100. For summer CMI, the median time of emergence for significant change is around 2085. At the grid scale, signal‐to‐noise ratios are significant for all seasons for mean surface air temperature, with earliest times of emergence occurring in summer. In contrast, the summer precipitation signal is not significant this century; for summer CMI, significant values are obtained in the eastern half of the region, occurring from about 2065 onwards. Median times of emergence are towards the end of the century for summer CMI in western Saskatchewan and in Alberta, although some areas of Alberta do not exhibit significant signals this century. [ABSTRACT FROM AUTHOR]

On the basis of climate zones classified by the number of days of the daily average temperature ≥ 10 °C (DT10) over China, the performance of the 9 CMIP5 climate models is evaluated in this paper. The results indicate that the CMCC-CMS and MPI-ESM-MR show higher skill than the other 7 models in simulating spatial pattern and its decadal change of climate zones over China. The simulation results for FGOALS-g2 and INM-CM4 both show relatively lower skill than the other 7 models. Meanwhile, the performance of multi-model ensemble in simulating climate zones over China is obviously better than the simulated result of any single model. So, it is a good way to simulate climate zones by multi-model ensemble to reduce some uncertainty of climate models. However, it is crucial to select appropriate ensemble members. Compared with 1960-2005, the climatic zones in China have an obvious trend of northward shift in 2021-2100. The range of southern sub-tropical belt expands to the most areas in the south of Yangtze River under RCP4.5 emission scenarios, and further extends to the north areas of Yangtze River with a maximum of 2-6° of latitude under RCP8.5 emission scenarios. Middle sub-tropical belt shifts gradually to the areas between Yellow River and north areas of the middle and lower reaches of the Yangtze River. Northern sub-tropical belt shifts northward to southeastern North China. Warm extra-tropical belt extends to the most of Northeast China, most of central Inner Mongolia, and northern Xinjiang under RCP8.5 emission scenarios. [ABSTRACT FROM AUTHOR]

Over the last decades, general circulation model (GCM) simulations have been regularly evaluated in terms of their ability to reproduce the historical frequency of significantly recurrent weather patterns (WP) observed at the regional scale. Thus, a good simulation of the frequency of these particular WP by the GCM is generally conditioning the good representation of the regional statistics of surface variables such as temperature and precipitation. In this paper, the seasonal frequency of eight particular WP have been calculated using the daily geopotential height fields simulated by an ensemble of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM. These WP are known as significantly influencing the French regional hydro‐climatology in terms of both frequency of low‐flows and high‐precipitation events. Four different bias correction methods have been applied on the simulated geopotential height fields before the calculation of the seasonal WP frequencies. The GCM ensemble showed overall good performances in terms of the simulation of WP seasonal frequencies. The application of a spatially and temporally non‐homogenous correction of simulated geopotential height fields improved significantly the simulation of WP frequencies for the four seasons. Finally, the evolution of the WP frequencies over the next century has been quantified. Three WP (WP2, WP4 and WP8) have pronounced seasonal changes, with WP2 and WP4 being less frequent in summer and autumn seasons, respectively, while WP8 being more frequent over spring, summer and autumn seasons. The strong simulated frequency evolution of WP2 and WP8 is an interesting result, which predicts the climate to be drier with time for France. Thus, WP2 (western oceanic circulation), grouping rainy days over the northern France region, is simulated as less frequent in future summers, while WP8 (anticyclonic situations), which groups non‐rainy days over France, is simulated as more frequent in future summers. [ABSTRACT FROM AUTHOR]

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model's ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty. [ABSTRACT FROM AUTHOR]

Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data. [ABSTRACT FROM AUTHOR]

This paper provides a first overview of the performance of global climate models participating in the Coupled Model Inter-Comparison Project phase 5 (CMIP5) in simulating the upper troposphere and lower stratosphere (UTLS) temperatures. Temperature from CMIP5 models is evaluated with high resolution global positioning system radio occultation (GPSRO) constellation observing system for meteorology, ionosphere, and climate (COSMIC) data during the period of July 2006-December 2013. Future projections of 17 CMIP5 models based on the representative concentration pathway (RCP) 8.5 scenarios are utilized to assess model performance and to identify the biases in the temperature in the UTLS region at eight different pressure levels. The evaluations were carried out vertically, regionally, and globally to understand the temperature uncertainties in CMIP5 models. It is found that the CMIP5 models successfully reproduce the general features of temperature structure in terms of vertical, annual, and inter-annual variation. The ensemble mean of CMIP5 models compares well with the COSMIC GPSRO data with a mean difference of ±1 K. In the tropical region, temperature biases vary from one model to another. The spatial difference between COSMIC and ensemble mean reveals that at 100 hPa, the models show a bias of about ±2 K. With increase in altitude the bias decreases and turns into a cold bias over the tropical and Antarctic regions. The future projections of the CMIP5 models were presented during 2006-2099 under the RCP 8.5 scenarios. Projections show a warming trend at 300, 200, and 100 hPa levels over a wide region of 60°N-45°S. The warming decreases rapidly and becomes cooling with increase in altitudes by the end of twenty-first century. Significant cooling is observed at 30, 20, and 10 hPa levels. At 300/10 hPa, the temperature trend increases/decreases by ~0.82/0.88 K/decade at the end of twenty-first century under RCP 8.5 scenarios. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper studies how the anthropogenic-induced global warming affects the East Asian winter monsoon ( EAWM) by using 26 Coupled Model Intercomparison Project phase 5 ( CMIP5) models. The simulated present-day EAWM is evaluated and future projections under RCP4.5 and RCP8.5 scenarios are presented in terms of the climatology and interannual variability. All 26 models can well reproduce the spatial pattern of EAWM climatology and 16 out of the 26 models can reasonably capture major features of interannual variability. The projection made by 26-model ensemble mean indicate that winter surface air temperature averaged over 20°-60°N, 100°-140°E will increase by 3 °C in RCP4.5 and 5.5 °C in RCP8.5 towards the end of the 21st century. The corresponding regional mean precipitation will increase by 12.3% in RCP4.5 and 21.8% in RCP8.5. The strong warming over high-latitude North Pacific due to melting sea ice in the Bering Sea and Okhotsk Sea leads to significant intensification and a northward shift of the Aleutian Low, resulting in prominent increase in the low-level northerly along the coastal regions of northeastern Asia. At 500 hPa the characteristic East Asian Trough is projected to weaken slightly and tilt more eastward with latitude. The selected 16-model ensemble mean projects future enhanced interannual variability of surface air temperature and sea-level pressure over mid-latitude North Pacific and high-latitude East Asia ( EA) and reduced variability over eastern China, suggesting that the EAWM will be more variable in the high-latitude EA and mid-latitude North Pacific but less variable in East China. Accordingly, the year-to-year precipitation variability will be significantly enhanced over high-latitude EA. Majority of the 26 models project that the leading mode of EAWM interannual variation (the 'northern mode') will become more dominant in the warmer climate. [ABSTRACT FROM AUTHOR]

Previous studies have examined the projected climate types in China by 2100. This study identified the emergence time of climate shifts at a 1◦ scale over China from 1990 to 2100 and investigated the temporal evolution of Köppen-Geiger climate classifications computed from CMIP5 multi-model outputs. Climate shifts were detected in transition regions (7%-8% of China's land area) by 2010, including rapid replacement of mixed forest (Dwb) by deciduous forest (Dwa) over Northeast China, strong shrinkage of alpine climate type (ET) on the Tibetan Plateau, weak northward expansion of subtropical winterdry climate (Cwa) over Southeast China, and contraction of oceanic climate (Cwb) in Southwest China. Under all future RCP (Representative Concentration Pathway) scenarios, the reduction of Dwb in Northeast China and ET on the Tibetan Plateau was projected to accelerate substantially during 2010-30, and half of the total area occupied by ET in 1990 was projected to be redistributed by 2040. Under the most severe scenario (RCP8.5), sub-polar continental winter dry climate over Northeast China would disappear by 2040-50, ET on the Tibetan Plateau would disappear by 2070, and the climate types in 35.9% and 50.8% of China's land area would change by 2050 and 2100, respectively. The results presented in this paper indicate imperative impacts of anthropogenic climate change on China's ecoregions in future decades. [ABSTRACT FROM AUTHOR]

Land-sea surface air temperature (SAT) contrast, an index of tropospheric thermodynamic structure and dynamical circulation, has shown a significant increase in recent decades over East Asia during the boreal summer. In Part I of this two-part paper, observational data and the results of transient warming experiments conducted using coupled atmosphere-ocean general circulation models (GCMs) are analyzed to examine changes in land-sea thermal contrast and the associated atmospheric circulation over East Asia from the past to the future. The interannual variability of the land-sea SAT contrast over the Far East for 1950-2012 was found to be tightly coupled with a characteristic tripolar pattern of tropospheric circulation over East Asia, which manifests as anticyclonic anomalies over the Okhotsk Sea and around the Philippines, and a cyclonic anomaly over Japan during a positive phase, and vice versa. In response to CO increase, the cold northeasterly winds off the east coast of northern Japan and the East Asian rainband were strengthened with the circulation pattern well projected on the observed interannual variability. These results are commonly found in GCMs regardless of future forcing scenarios, indicating the robustness of the East Asian climate response to global warming. The physical mechanisms responsible for the increase of the land-sea contrast are examined in Part II. [ABSTRACT FROM AUTHOR]

The possible change in the characteristics of weather in the future should be considered as important as the mean climate change because the increasing risk of extremes is related to the variability on daily time scales. The weather characteristics can be represented by the climatological mean interdiurnal (day-to-day) variability (MIDV). This paper first assessed the phase five of the Coupled Model Intercomparison Project coupled climate models’ capability to represent MIDV for the surface maximum and minimum temperature, surface wind speed and precipitation under the present climate condition. Based on the assessment, we selected three best models for projecting future change. We found that the future changes in MIDV are characterized by: (a) a marked reduction in surface maximum and minimum temperature over high latitudes during the cold season; (b) a stronger reduction in the surface minimum temperature than in the maximum temperature; (c) a reduction in surface wind speed over large parts of lands in Northern Hemisphere (NH) during NH spring; (d) a noticeable increase in precipitation in NH mid-high latitudes in NH spring and winter, and in particular over East Asia throughout most of the year. [ABSTRACT FROM AUTHOR]

This is the second of the two-part paper series on the analysis and evaluation of the Fifth phase of Coupled Model Intercomparison Project (CMIP5) simulation of contemporary climate as well as IPCC, AR5 Representative Concentrations Pathways (RCP), 4.5 and 8.5 scenarios projections of the Greater Horn of Africa (GHA) Climate. In the first part (Otieno and Anyah in Clim Dyn, ) we focused on the historical simulations, whereas this second part primarily focuses on future projections based on the two scenarios. Six Earth System Models (ESMs) from CMIP5 archive have been used to characterize projected changes in seasonal and annual mean precipitation, temperature and the hydrological cycle by the middle of twenty-first century over the GHA region, based on IPCC, 5th Assessment Report (AR5) RCP4.5 and RCP8.5 scenarios. Nearly all the models outputs analyzed reproduce the correct mean annual cycle of precipitation, with some biases among the models in capturing the correct peak of precipitation cycle, more so, March-April-May (MAM) seasonal rainfall over the equatorial GHA region. However, there is significant variation among models in projected precipitation anomalies, with some models projecting an average increase as others project a decrease in precipitation during different seasons. The ensemble mean of the ESMs indicates that the GHA region has been experiencing a steady increase in both precipitation and temperature beginning in the early 1980s and 1970s respectively in both RCP4.5 and RCP8.5 scenarios. Going by the ensemble means, temperatures are projected to steadily increase uniformly in all the seasons at a rate of 0.3/0.5 °C/decade under RCP4.5/8.5 scenarios over northern GHA region leading to an approximate temperature increase of 2/3 °C by the middle of the century. On the other hand, temperatures will likely increase at a rate of 0.3/0.4 °C/decade under RCP4.5/8.5 scenarios in both equatorial and southern GHA region leading to an approximate temperature increase of 2/2.5 °C by the middle of twenty-first century. Nonetheless, projected precipitation increase varied across seasons and sub-regions. With the exception of the equatorial region, that is projected to experience precipitation increase during DJF season, most sub-regions are projected to experience precipitation increase within their peak seasons, with the highest rate of increase experienced during DJF and OND seasons over southern and equatorial GHA regions respectively. Notably, as precipitation increases, the deficit (E < P) between evaporation (E) and precipitation (P) increased over the years, with a negatively skewed distribution. This generally implies that there is a high likelihood of an increased deficit in local moisture supply. This remarkable change in the general hydrological cycle (i.e. deficit in local moisture) is projected to be also coincident with intensified westerly anomaly influx from the Congo basin into the region. However, better understanding of the detailed changes in hydrological cycle will require comprehensive water budget analyses that require daily or sub-daily variables, and was not a specific focus of the present study. [ABSTRACT FROM AUTHOR]

The Congo Basin is one of three key convective regions on the planet which, during the transition seasons, dominates global tropical rainfall. There is little agreement as to the distribution and quantity of rainfall across the basin with datasets differing by an order of magnitude in some seasons. The location of maximum rainfall is in the far eastern sector of the basin in some datasets but the far western edge of the basin in others during March to May. There is no consistent pattern to this rainfall distribution in satellite or model datasets. Resolving these differences is difficult without ground-based data. Moisture flux nevertheless emerges as a useful variable with which to study these differences. Climate models with weak (strong) or even divergent moisture flux over the basin are dry (wet). The paper suggests an approach, via a targeted field campaign, for generating useful climate information with which to confront rainfall products and climate models. [ABSTRACT FROM AUTHOR]

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region. [ABSTRACT FROM AUTHOR]