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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]

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]

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]

The global climate models (GCMs) performances of the recently released Coupled Model Intercomparison Project phase 6 (CMIP6) compared to its predecessor, CMIP5, are evaluated to anticipate the expected changes in climate over Egypt. Thirteen GCMs and their multi‐model ensemble (MME) of both CMIPs were used for this purpose. The future projections were compared for two radiative concentration pathways (RCP 4.5 and 8.5) and two shared socio‐economic pathways (SSP 2–4.5 and 5–8.5). The results revealed improvement in most CMIP6 models in replicating historical rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) climatology over Egypt. The MME of the CMIPs revealed that both could reproduce Egypt's spatial distribution and seasonal climate variability. However, the bias in CMIP5 was higher than that for CMIP6. The uncertainties in simulating seasonal variability of rainfall and temperatures were lower for CMIP6 compared to CMIP5. The future projection of rainfall using CMIP6 MME revealed a higher reduction of precipitation (10–26 mm) in the economically crucial northern region than that estimated using CMIP5 (0–17 mm), compared to 133.5 mm rainfall in the base period. CMIP6 also projected a 0.74–1.63°C more rise in Tmax and Tmin compared to CMIP5 by the end of the century. The study indicates more aggravated scenarios of climate changes in Egypt than anticipated earlier, using the CMIP5 models. Therefore, Egypt needs to streamline the existing adaptation and mitigation measures to account for climate projections. [ABSTRACT FROM AUTHOR]

Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD). [ABSTRACT FROM AUTHOR]

The complex topography of East Africa poses challenges for accurate modeling of regional climate. The Turkana Channel in northwestern Kenya is an important feature because of a persistent low‐level jet (LLJ) that blows through it, which has complex interactions with local and regional rainfall. We establish the annual cycle and interannual variability of the LLJ in the ERA5, MERRA‐2, and JRA‐55 reanalyses. The jet is strongest during wet seasons in the surrounding region. Results suggest a statistically significant weakening of the LLJ over the last 30–40 years in two out of the three reanalyses. We propose an explanation based on the jet's relationship with regional warming patterns and zonal surface pressure gradients, which link the jet to larger‐scale climate dynamics including the Walker Circulation. If these changes continue in the future, there may be significant implications for rainfall including increases in northwest Kenya and decreases further inland. However, the global models used to produce climate projections vary in their simulations of the LLJ in part because they represented topography. Consequently, it is not possible to assess how future LLJ changes will affect regional climate using CMIP5 models alone. Differences between the reanalyses preclude their direct use for model evaluation. Improving the processes by which topographical observations are mapped onto model grids could lead to improvements in the simulation of the East African climate. A field campaign to measure the LLJ directly could resolve uncertainties in the literature, help constrain reanalyses, and determine which models have the most realistic LLJ representation. Plain Language Summary: The Turkana Low‐Level Jet is an important physical feature of the East African climate. It is present throughout the year and is both a factor in the aridity of the channel through which it blows and a source of moisture for rainfall in the wider region. We investigate how the jet varies throughout the year and across longer timescales, and suggest that it may have been getting weaker over the last 30–40 years. We suggest that this is due to connections between the jet and large‐scale climate dynamics, such as the Walker Circulation. However, because the jet is represented differently in different datasets, and climate models struggle to simulate it, we can't be sure of the impact these changes might have in the future. One potential way forward is to measure the jet directly in a field campaign. Key Points: The Turkana Low‐Level Jet may be slowing down on decadal timescales. However, reanalysis simulations of the trend and annual cycles differThe jet slowdown is a plausible consequence of changes to zonal surface pressure gradients, linking the jet to the Walker CirculationCoupled Model Intercomparison Project 5 representations of the jet are resolution‐dependent [ABSTRACT FROM AUTHOR]

Due to its wide‐ranging impacts, predicting decadal variations of sea surface temperature (SST) in the subpolar North Atlantic remains a key goal of climate science. Here, we compare the representation of observed subpolar SST variations since 1960 in initialized and uninitialized historical simulations from the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5/6). Initialized decadal hindcasts from CMIP6 explain 88% of observed SST variance post‐1980 in the subpolar gyre at lead years 5–7 (77% in uninitialized simulations) compared to 42% (8%) in CMIP5, indicating a more prominent role for forcing in driving observed subpolar SST changes than previously thought. Analysis of single‐forcing experiments suggests much of this correlation is due to natural forcing, explaining ∼55% of the observed variance. The amplitude of observed subpolar SST variations is underestimated in historical simulations and improved by initialization in CMIP6, indicating continued value of initialization for predicting North Atlantic SST. Plain Language Summary: Sea surface temperature (SST) fluctuations in the North Atlantic region are known to influence climate around the globe. Comparing retrospective predictions of North Atlantic SST with observations, we show that the most state‐of‐the‐art climate models have improved in predicting North Atlantic SST for up to 10 years ahead compared to the previous generation of climate models. This recent improvement can be traced back to particularly well‐predicted variations of North Atlantic SST after 1980. During this time, reactions to large volcanic eruptions and changes in solar activity, as well as inherent unforced variations, play an important role for the predictability of North Atlantic SST. Here, not only direct radiative forcing changes play a role, but there is also a dynamical response of the ocean that influences the final climate response. This study inspires hope that current climate models will show improved capability in predicting North Atlantic SST changes up to a decade ahead, particularly following large volcanic eruptions, but also otherwise. Key Points: North Atlantic sea surface temperature (SST) is better predicted in CMIP6 than in CMIP5 in both initialized hindcasts and noninitialized historical simulationsHigh correlation of North Atlantic SST in CMIP6 historical simulations with observations indicates a prominent role for forcing after 1980.Fifty‐five percent of the total observed post‐1980 annual North Atlantic SST variance is explained by simulations with natural forcing only [ABSTRACT FROM AUTHOR]

The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia. [ABSTRACT FROM AUTHOR]

Climate change is known as one of the key challenges of the 21st century for sustainable agricultural development over Southwest Asia. However, not much is known about the way that climate changes might affect precipitation, temperature, and climate zones as their consequence over Southwest Asia. Here we have analysed probable changes in modified Thornthwaite climate zones by using monthly temperature and precipitation data from 320 meteorological stations during the historical period of 1971–2015, as well as the future projections of 17 GCMs (General Circulation Models) outputs under the RCP4.5 and RCP8.5 scenarios in two future periods, 2041–2070 and 2071–2100. The CMIP5 climate projections were bias corrected using the correction factors derived in the historical period and applied to simulate future climatic conditions over Southwest Asia. Results derived from multi model ensemble projections of precipitation and temperature indicated that the regional mean annual temperature by the future period of 2071–2100 is expected to increase by 2.4 and 3.8°C, under RCP4.5 and RCP8.5, respectively. Also, except for the southern and eastern parts of the Arabian Peninsula, by 2071–2100, the mean annual precipitation would significantly decrease by on average 6.2 and 7.5%, under RCP4.5 and RCP8.5, respectively. Computing climate types based on averages from three climatic periods (1971–2015, 2041–2070, and 2071–2100) showed that Southwest Asia tends to become warmer and dryer. For example, by the end of the 21st century, the percentage of the area occupied by the torrid‐arid climate type (which already covers 13.8% of the total area) would be 29.3 and 36.4% under RCP4.5 and RCP8.5, respectively. These changes are important because they would definitely have profound ecological, hydrologic, and social consequences and may affect the basic components of sustainable agricultural systems like crop and livestock productions. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) plays an important role in modulating the location of tropical cyclone (TC) genesis over the western North Pacific (WNP). This study evaluated the simulation of the ENSO's impact on WNP TC genesis in 10 CMIP5 models based on a TC detection method, and projected its changes under global warming using the historical and the representative concentration pathway 8.5 (RCP8.5) simulations. The observed southeast–northwest shift role of ENSO in the WNP TC genesis location can be well reproduced in most of the models, although the strength of the ENSO's impact is underestimated as the underestimated climatological TC genesis frequency in these models. Moreover, we found the WNP TC during both El Niño and La Niña events shows an apparent eastwards shift under global warming. However, this eastwards shift of WNP TC genesis could be associated with the changes in climatological TC genesis but not the changes in the ENSO's impact. The projected changes in the ENSO's impact on WNP TC genesis is not as certain as we expected based on the previous conclusions of the robust changes in ENSO's impact on large‐scale environment. As a result, we suggest that both the TC genesis climatology and ENSO's impact on TC genesis simulated in the models need improvement in future to project the changes in ENSO's impact under global warming, and more models with 6‐hourly outputs used to detect TCs are needed to increase the confidence of multi‐model ensemble projections. [ABSTRACT FROM AUTHOR]

As one of three global hot spots of tropical convection, potential future changes to the Congo Basin climate system will have regional and tropics‐wide implications. However, the latest generation of climate models from the Coupled Model Intercomparison Project 5 disagree on the sign and magnitude of future change and diverge in their estimation of the historical rainfall climatology. This study assesses the plausibility of different signals of future rainfall change by examining the processes relating to rainfall projections in samples of historically wet or dry models during the September–November rainy season. In the west Congo Basin, there are no significant differences in rainfall change projections in models that are historically wet or dry. Both composites feature wetting in the north (up to 1.8 mm/day) and drying in the south, associated with enhanced tropical Atlantic sea surface temperatures, increased evaporation, and enhanced low‐level moisture flux into the basin. In the east Congo Basin, there is greater evidence that differences in model historical climatologies has an influence on the magnitude of future rainfall change. Historically wet models project significant wetting in the northeast (1.19 mm/day) associated with a weakened northern component of the African Easterly Jet (AEJ) and enhanced moisture convergence. Dry models do not capture the structure of the AEJs in the historical period, and so changes to the AEJs under warming do not produce the same wetting pattern. The analysis therefore casts doubt on the plausibility of the driest rainfall change signals in the east Congo Basin. Key Points: The magnitude and distribution of Congo Basin future rainfall change varies across coupled modelsProcess‐based model evaluation can help to identify the plausibility of rainfall projections in the absence of observational dataHistorical model biases in the African Easterly Jets affect the magnitude of future rainfall change in the east Congo Basin [ABSTRACT FROM AUTHOR]

There is a lack of high‐quality global observations to evaluate soil drying impacts on surface fluxes in Earth system models (ESMs). Here we use a novel diagnostic based on the observed warming of the land relative to the atmosphere during dry spells (relative warming rate, RWR) to assess ESMs. The ESMs show that RWR is well correlated with changes in the partition of surface energy between sensible and latent heat across dry spells. Therefore, comparisons between observed and simulated RWR reveal where models are unable to capture a realistic soil moisture‐heat flux relationship. The results show that in general, models simulate dry spell ET dynamics well in arid zones while decreases in evaporative fraction appear excessive in some models in continental climate zones. Our approach can help guide land model development in aspects that are key in simulating extreme events like heat waves. Plain Language Summary: We present a methodology to assess how land evapotranspiration (ET) responds to soil moisture (SM) in climate models across the world. Our method is based on looking at the observed versus modeled evolution of land surface temperature and the overlying air temperature during rain free periods, when SM decreases and limits the amount of land ET. We observed a good relationship between modeled ET and the modeled relative evolution of these temperatures during dry periods. Therefore, our method can be used to evaluate how realistic are modeled SM‐ET relationships when soils are drying. Our results show that in general, models capture fairly well the influence of SM on ET in arid climate zones, while they perform less well in continental climate zones. Our method provides a unique new tool to improve aspects of weather and climate models important for simulating phenomena such as heat waves. Key Points: The relative warming rate diagnostic is used to assess soil drying impacts on surface heat fluxes in climate modelsThis diagnostic is shown to be a very good indicator of modeled evaporative fraction evolution during dry downsModels capture dry spell evapotranspiration dynamics well in arid zones but struggle in continental climate zones [ABSTRACT FROM AUTHOR]

The tropical intraseasonal (30–90 days) oscillation (ISO) displays distinctive behaviors in boreal summer and winter. How well each mode is simulated in climate models has been investigated; however, very few studies have examined whether these modes are simulated in appropriate season. Here we developed diagnostics to assess this aspect and applied these diagnostics to numerous atmosphere‐only and atmosphere–ocean‐coupled models. We found out that all models share serious biases and that they sometimes incorrectly simulate the boreal summer ISO mode even in boreal winter and underestimate the appearance frequency of the boreal summer ISO in boreal summer. Nearly all atmosphere‐ocean‐coupled models show some improvements in the ISO seasonality representation compared to their atmosphere‐only counterparts. It is suggested that good models for simulating the ISO seasonality have good life cycles for each ISO mode and that an accurate reproduction of the seasonal mean low‐level zonal wind is crucial. Plain Language Summary: The leading intraseasonal (30–90 days) oscillation (ISO) in the tropical atmosphere has different characteristics in the boreal summer and winter. Accordingly, it has different impacts on extreme weather such as heavy precipitation and tropical cyclone activity. Many studies have examined how well the boreal summer and winter modes are simulated in climate models; however, very few studies have examined whether climate models can simulate the boreal summer and winter ISOs appropriately in the boreal summer and winter, respectively. In this study, we developed a new method to examine this aspect and applied it to numerous present‐day climate simulations using both atmosphere‐only and atmosphere–ocean‐coupled models. We found out that all models share serious biases and that they inadequately simulate the boreal summer mode even in boreal winter and underestimate the appearance frequency of the boreal summer mode in boreal summer. Nearly all atmosphere–ocean‐coupled models show some improvements in the ISO seasonality representation compared to their atmosphere‐only counterparts. It is suggested that good models for simulating the seasonality of the ISO show good life cycles for each ISO mode and a seasonal mean zonal wind in the lower troposphere. These results are helpful for further improving climate models. Key Points: The seasonality of two major modes of ISO (MJO/BSISO) in global climate models was examinedGlobal climate models have larger deficits in their performances when representing the BSISO than the MJOThe presented method helps in examining the model performance in detail and suggesting further improvements [ABSTRACT FROM AUTHOR]

Coupled Model Intercomparison Project phase 5 (CMIP5) climate models simulate a wide range of historical sea ice areas. Even models with areas close to observed values may contain compensating errors, affecting reliability of their projections. This study focuses on the seasonal cycle of sea ice, including analysis of model concentration budgets. Many models have insufficient autumn ice growth, leading to large winter biases. A subset of models accurately represent sea ice evolution year‐round. However, comparing their winter ice concentration budget to observations reveals a range of behaviors. At least one model has an accurate ice budget, which is only possible due to realistic ice drifts. The CMIP5 generation of model physics and resolution is therefore structurally capable of accurately representing processes in Antarctic sea ice. This implies that substantially improved projections of Antarctic dense ocean water formation and ice sheet melting are possible with appropriate subsetting of existing climate models. Plain Language Summary: Antarctic sea ice simulated by numerical climate models generally exhibits large differences compared to satellite‐based estimates. Therefore, attempts to use these models to understand how Antarctic sea ice—and as a result Antarctic climate, sea level rise, and ocean circulation—will change in the future are severely compromised. This study looks beyond standard measures of sea ice such as the area of ocean covered by ice. Instead, we calculate how much of the change in sea ice over the course of a year is caused by local melting and freezing on the one hand and ice transport (for example by winds and ocean currents) on the other. Of an already small number of models which do show a realistic amount of sea ice, we identify that some do so for the wrong reasons. However, there is one model using established model components which shows realistic behavior. This model is able to match observations of sea ice drift. This demonstrates that the existing climate models are capable of representing Antarctic sea ice without further increases in sophistication. Focusing on such models for climate projections may improve the reliability of projections of the future. Key Points: Some CMIP5 models capture Antarctic sea ice area, but quantifying their dynamic and thermodynamic processes reveals serious deficienciesOne model is right for the right reasons; realistic Antarctic sea ice can be simulated using existing model physics and resolutionRealistic representation of sea ice processes in this model is only possible because it has realistic sea ice drift [ABSTRACT FROM AUTHOR]

Retrospective tropical Indo‐Pacific forecasts for 1961–2015 are made using 28 models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) plus four models from the North American Multi‐Model Ensemble (NMME), using a model‐analog technique. Forecast ensembles are extracted from preexisting model simulations, by finding those states that initially best match an observed anomaly and tracking their subsequent evolution, requiring no additional model integrations. Model‐analog forecasts from the 10 "best" CMIP5 models have skill for sea surface temperature and precipitation comparable to that of both the NMME model‐analog forecast ensemble and (since 1982) traditional assimilation‐initialized NMME hindcasts. The El Niño–Southern Oscillation (ENSO) forecast skill has no trend over the 55‐year period, and its decadal variations appear largely random, although the skill does improve during epochs of increased ENSO activity. Including the CMIP5‐projected effects of external radiative forcings improves the tropical sea surface temperature skill of the model‐analog forecasts but not within the ENSO region. Plain Language Summary: Seasonal forecasts are made by starting a climate model from an initial estimate of the latest global three‐dimensional ocean, atmosphere, and land conditions and then using supercomputers to run the model's equations forward in time. These extensive calculations are only feasible at a few national operational centers and large research institutions. However, many similar models are also used for long simulations of the Earth's preindustrial climate, which are made freely available for climate change studies. We investigated whether seasonal forecasts might be drawn from the information already existing within these simulations, instead of by making new model computations. Within each simulation, we determine the best matches, or "model‐analogs," to current observed tropical Indo‐Pacific ocean surface conditions. How these analogs evolve over the next several months within each long simulation is then its seasonal forecast. We find that this much less expensive model‐analog technique is as skillful as the more traditional forecasting method. We then employed it to make forecasts during 1961–2015, using 28 existing climate simulations, significantly expanding previous forecast efforts. This study suggests that with little additional effort, sufficiently realistic and long existing climate model simulations can provide the basis for skillful seasonal forecasts. That is, anyone can be a climate forecaster. Key Points: Seasonal tropical Indo‐Pacific hindcasts for 1961‐2015 are made from 28 different CMIP5 models, with some as skillful as operational CGCMsENSO hindcast skill has no trend, but it is higher in strong‐ENSO epochs (1980s and 1990s) and lower in weak‐ENSO epochs (1970s and 2000s)Including the effects of projected external radiative forcings improves SST hindcast skill but not within the ENSO region [ABSTRACT FROM AUTHOR]

To diagnose the influence of sea surface temperature (SST) biases on temperature and precipitation patterns in the western United States, we analysed atmospheric and coupled global climate model (GCM) simulated output from the Coupled and Atmospheric Model Intercomparison Project versions 3 and 5 (CMIP3, AMIP3, CMIP5, and AMIP5). We further analyse the impact of SST biases in regional climate modelling simulations. CMIP3 and CMIP5 multi‐model ensembles show systematic warm SST biases offshore of California and the Baja California Peninsula (Baja) region, with ensemble mean SST biases of the order of ~3–5 °C. Throughout the western United States, 75% of all models in CMIP3 and CMIP5 exhibit wet precipitation biases and corresponding cold biases in surface temperature. The CMIP5 ensemble shows on average a stronger and more consistent relationship between Baja SST biases and precipitation over the west compared to the CMIP3 ensemble. We attempted to isolate the atmospheric response to regional SST biases using a regional climate model (RCM) based on the Weather Research and Forecasting model with a 36 km grid size. The RCM was driven with the CMIP3‐CCSM3 as boundary conditions with and without corrections of simulated SSTs. Results from RCM simulations further confirm that SST biases impact climate regionally and propagate over the western United States and can explain up to 80% of wet precipitation biases. Our regional GCM comparison and RCM experiment assess the robustness of model estimates of climate mean states and constitute an often neglected prerequisite for characterizing how errors transfer from GCM to regional downscaling modelling frameworks and how they could potentially affect downscaling application and impact studies. This study shows how the coupled global climate models SST biases impact climate regionally and propagate over the western United States and can explain up to 80% of wet precipitation biases. We performed a regional GCM comparison and RCM experiment that assess how errors transfer from GCM to regional downscaling modelling, potentially affecting downscaling application and impact studies. Figure shows RCM wet precipitation differences over SW United States resulting from isolating the effect of using raw (biased) GCM SST minus bias‐corrected SSTs. [ABSTRACT FROM AUTHOR]

Simulations and future projections of year‐to‐year variation in global monsoon precipitation (GMP), defined as the summer precipitation amount per unit area within the global monsoon domain, are investigated using the 33 models that participated in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). The inter‐annual standard deviation of GMP, which presents the amplitude of year‐to‐year variability in monsoon rainfall, is simulated well by the multi‐model ensemble (MME) mean of the 33 models and of the best five (B5) models. The B5 models show superior skills in reproducing the climatological monsoon precipitation and its inter‐annual variability. The inter‐annual standard deviations of 25‐year GMP in the present day (1979–2005) derived from the MME average of the 33 models and of the B5 models are 0.17 and 0.15 mm/day per unit area, respectively, both being close to the observation (0.16 mm/day per unit area). Consistent with the observed El Niño–Southern Oscillation (ENSO)–GMP relationship, the simulated variability of GMP is negatively correlated with the sea surface temperature in the Niño areas at the inter‐annual timescale. Projections by the 33 CMIP5 models under the RCP4.5 scenario indicate that wet and dry monsoon years would occur more intensively in a warmer climate. Around 61% (20 out of the 33) of the models show enhanced inter‐annual variability of GMP by the end of the 21st century (2072–2098) compared to the period 1979–2005. The MME of the B5 models suggests that the amplitude of projected GMP variability would increase by ~20% from present day to the late 21st century. However, the increasing rate of GMP variability detected by the 33 models' MME is relatively small (~6%), which is even smaller than the inter‐model standard deviation, suggesting the amplitude changes in GMP variability feature non‐negligible uncertainty among the 33 models. Under global warming, the inter‐annual variation of GMP would still be tightly connected with ENSO, although the projected amplitude changes in ENSO variability are noticeably diverse across the 33 CMIP5 models. The enhanced amplitude of inter‐annual GMP variability may be attributable to the increase in mean‐state moisture associated with global warming. The dynamic effect related to changes in the variability of monsoon circulation is small and less robust among the 33 CMIP5 models. Taking advantage of the large number of members in phase 5 of the Coupled Model Intercomparison Project (CMIP5), we identified robust changes in monsoon rainfall variability at the inter‐annual timescale under global warming. Around 61% (20 out of 33) of the models show enhanced inter‐annual variability of GMP by the end of the 21st century (2072–2098) compared to the period 1979–2005. ENSO is still the major driving force behind the inter‐annual variability of global monsoon precipitation under global warming, although the amplitude changes in ENSO variability are remarkably diverse in future projections. From the moisture budget perspective, the enhanced amplitude of inter‐annual GMP variability may be attributable to the increase in mean‐state moisture associated with global warming. Figure shows (a) differences in the inter‐annual variability (defined as the inter‐annual standard deviation) of GMP (red), NHMP (blue) and SHMP (green) between the historical run (1979–2005) and RCP4.5 (2072–2098) in the CMIP5 models. The rightmost bars are the results derived from the average of CMAP and GPCP precipitation (unit: mm/day per unit area). (b) TCCs between the GMP and 2‐m air temperature anomalies (shading), and between the GMP and 850‐hPa wind anomalies (vectors), during the period 2072–2098. White dots mark the regions with correlation coefficients at the 95% confidence level. (c) Relative contributions of mean‐state specific humidity and the variability of vertical motion to the enhanced variability of GMP under GW (unit: 10−5 kg m−1 s−3). [ABSTRACT FROM AUTHOR]

ABSTRACT Precipitation, temperature, and evaporative demand are the most dominant factors affecting water availability in a region. This study examines projected changes in these hydro-climatic variables over western Canada under two greenhouse gas emissions scenarios using statistically downscaled, high resolution climate data generated by six Global Climate Models ( GCMs) from the latest Coupled Model Intercomparison Project ( CMIP5). Potential changes in the spatial and seasonal distributions of water availability over nine major western Canadian river basins are examined by computing the 3- and 12-month standardized precipitation and evapotranspiration indices ( SPEI-3 and SPEI-12). While individual GCM projections vary on the rate and seasonality of changes, they all indicate similar spatial and temporal patterns. The highest projected increases in precipitation and temperature are primarily in the northern basins, with some decreases in summer precipitation in the southern basins. The evolution of the SPEI-12 values for the southern basins such as Columbia, Saskatchewan, Fraser and Athabasca indicate a gradual increase in the magnitude and duration of water deficit, while the reverse was found for most of the northern basins such as Peel/Lower Mackenzie, Liard, and Northern Pacific that show a gradual increase in water surplus on an annual basis. The SPEI-3, however, shows that almost all river basins in western Canada, with the exception of Peel/Lower Mackenzie that are located in the extreme north of the study region, are projected to experience decreasing water availability in summer. In general, the study highlights the potential changes in the spatial and seasonal distribution of western Canadian water resources and sets the stage for a more detailed and process based hydro-climate modelling study to be conducted in the region. [ABSTRACT FROM AUTHOR]

Since 1960s, India experiences a series of extreme drought and flood events during the summer months. The Humid Subtropical Climatic Zone ( HSTC), which comprises the Indo-Gangetic Plain region of India, is highly vulnerable to the climatic extremities. This region is the home for approximately 40% of total population and yields approximately 50% of total agricultural production of India. We investigate the historical variation in dry/wet conditions and project the future changes in extreme events under two different scenarios of the Coupled Model Inter-comparison Project 5 models. First, the model parameters, that is, precipitation ( P) and temperature ( T) are bias corrected with respect to observation data and finally six models are selected, which are in right phase with the observation for composite analysis. Next, we calculate the potential evapotranspiration (PET) and the Standardized Potential Evapotranspiration Index to characterize the extreme events. Both P and PET are projected to increase in the HSTC zone; however, both the wet and dry conditions demonstrate a persistent increase in future. In relative terms, P increases faster than PET along the Gangetic Plain region (wet condition) and decreases in the southern and eastern part of the region (dry condition). The mitigating effect (RCP4.5 scenario) of precipitation increase will be overridden by strengthened PET and extreme dry condition project markedly under RCP8.5 scenario. The features are consistent with the increase/ decrease in multimodel mean SPEI, consistent with the spatial pattern of P−PET. The area affected due to wet and dry events will be relatively higher under the RCP4.5 and RCP8.5 scenario, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT The science that humans are the cause of global warming, and that the associated climate change would lead to serious changes in climate extreme events, food production, freshwater resources, biodiversity, human mortality, etc. is unequivocal. After several political negotiations, a 2 °C warming has been considered to be the benchmark for such damaging changes. However, an increasing amount of scientific research indicates that higher levels of warming are increasingly likely. What would the world be like if such higher levels of warming occurred? This study aims to provide information for better politically driven mitigation through an investigation of the changes in temperature- and precipitation-based extreme indices using CMIP5 (coupled model intercomparison project phase 5) simulations of a warming of 1, 2, and 3 °C in China. Warming simulations show more dramatic effects in China compared with the global average. In general, the results show relatively small change signals in climate extreme events in China at 1 °C, larger anomalies at 2 °C, and stronger and more extended anomalies at 3 °C. Changes in the studied temperature indices indicate that warm events would be more frequent and stronger in the future, and that cold events would be reduced and weakened. For changes in the precipitation indices, extreme precipitation generally increases faster than total wet-day precipitation, and China will experience more intensified extreme precipitation events. Furthermore, the risk of flooding is projected to increase, and the dry conditions over northern China are projected to be mitigated. In certain regions, particularly Southwest China, the risks of both drought and flood events would likely increase despite the decreased total precipitation in the future. Uncertainties mainly derived from inter-model and scenario variabilities are attached to these projections, but a high model agreement can be generally observed in the likelihood of these extreme changes. [ABSTRACT FROM AUTHOR]

The climatology of upwelling in the tropical tropopause layer (TTL) in current climate simulations and in future climate projections is examined using models participated in the Coupled Model Intercomparison Project phase 5 (CMIP5). Large intermodel differences in upwelling in the TTL appear in the current climate simulations. Model composite analysis and upwelling diagnosis based on the zonal momentum budget indicate that the intermodel differences in upwelling are controlled by meridional eddy momentum fluxes associated with tropical planetary waves and midlatitude synoptic waves. Future climate simulations indicate that upwelling changes in the TTL are significantly correlated with the upwelling in current climate simulations. Models with strong (weak) TTL upwelling in the current climate simulations tend to project strong (weak) upwelling enhancement in the future climate. The intermodel differences in the upwelling change arise from the same dynamical factors as the current climate cases. The contribution of sea surface temperature (SST) to the intermodel upwelling differences is examined by SST‐prescribed simulations in CMIP5. The contribution of intermodel SST differences to the upwelling is smaller than that of intrinsic atmospheric intermodel differences. The significant correlation of the tropical upwelling between the current climate simulations and the future changes appears to be independent of the target latitude range. Key Points: Upwelling in the tropical tropopause layer in current climate and in future climate is examined using CMIP5 modelsModels with strong upwelling in the current climate tend to project strong upwelling enhancement in the future climateIntermodel differences in upwelling are controlled mainly by atmospheric model uncertainty rather than sea surface temperature uncertainty [ABSTRACT FROM AUTHOR]

The seasonal cycle is fundamental to the Earth's climate system, accounting for the vast majority of temperature variance. Understanding how the seasonal cycle will change in the future, and by when, is a key question with important implications. Here a 40‐member initial condition climate model ensemble is used to investigate the influence of internal variability on the detection of changes in the amplitude and timing of the seasonal cycle of surface temperature over Northern Hemisphere land in response to increasing greenhouse gases. Internal variability renders the detection of these changes challenging; even by the mid‐twenty‐first century, small ensembles will be insufficient to separate the forced signals from internal variability over many continental regions in the Northern Hemisphere. Despite this, projected changes over Europe, North Africa, and Siberia are large and easily detectable, even in a single member. Specifically, amplitude increases over Europe and North Africa while it decreases over Siberia. On the other hand, the timing of the seasonal cycle is delayed over all three regions. It is found that these changes are remarkably robust across model ensembles from the Coupled Model Intercomparison Project phase 5 archive. To understand the mechanisms underlying these robust changes, a simple energy balance model is used to partition changes into contributions arising from changes in the physical parameters that control the seasonal cycle. It is found that future changes in the seasonal cycle over the three regions are most strongly controlled by changes in surface longwave and turbulent heat fluxes. Key Points: Internal variability renders the detection of future changes in the seasonal cycle of surface temperature difficult over many regionsEurope, North Africa, and Siberia, however, exhibit remarkably robust and easily detectable changes across climate model ensemblesThe robust changes over the three regions are primarily driven by changes in surface longwave and turbulent heat fluxes [ABSTRACT FROM AUTHOR]

Climate simulations of future hot extremes exhibit large uncertainties regarding the magnitude of projected warming. We identify two mechanisms that influence how strongly future heat extremes intensify in climate models. First, the magnitude of extreme temperature increases is determined by changes in preceding seasonal precipitation, connected to amplified warming via soil moisture decreases. Second, there are large differences in how models respond to moisture variability; those with a stronger response under current climate simulate larger future increases in hot extremes. We build on this mechanistic understanding of future uncertainty and develop a novel constraint, the observed precipitation‐hot temperature relationship, focused on the conditions on the actual hottest day, to identify climate models with realistic land‐atmosphere feedbacks on hot extremes. Applying this constraint to the Coupled Model Intercomparison Project Phase 5 ensemble reduces the probability of the largest increases in projected heat extremes, particularly over Europe and North America. Plain Language Summary: Climate model simulations of future hot temperature extremes agree on the regions of strongest warming but disagree on how much temperature will increase. We identify that a large part of the uncertainty is explained by intermodel differences in future precipitation changes, and differences in the model‐specific strength of land‐atmosphere feedbacks. We constrain the future climate model ensemble simulations by using only those models that simulate land‐atmosphere feedbacks more similar to real‐world observations over the past 60 years. This reduces the uncertainty of future changes by reducing the probability of the strongest increases in hot extremes particularly over Europe and North America. Key Points: Regional uncertainty of future heat extremes is controlled by changes in seasonal precipitation and strength of land‐atmosphere feedbacksObserved land‐atmosphere feedbacks help constrain future heat extremes in the CMIP5 ensemble over Europe and North AmericaEmploying this process‐based constraint reduces the probability of the highest increases in temperature extremes within the CMIP5 ensemble [ABSTRACT FROM AUTHOR]