This paper presents an evaluation of the variability and its temporal‐scale components of daily air temperature and precipitation simulated by two regional climate models (RCMs) and two driving ...global climate models (GCMs) from the EURO‐CORDEX and CMIP5 projects. The analysis was performed for eight geographical areas of central Europe for the period 1971–2000. After a brief evaluation of total variance, we focus on the ability of the simulations to represent short‐term, seasonal and long‐term variability components. This analysis was carried out for original time series as well as for series with the annual cycle removed. A fast Fourier transformation and a Kolmogorov–Zurbenko filter were used to separate the three temporal components.
The two methods of time series decomposition generally agree, which confirms the robustness of the results. Although the observed total variability of daily air temperature and precipitation is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. Regarding the air temperature, the results for the time series with the annual cycle removed are generally somewhat worse than for original series. The simulations tend to underestimate the short‐term component of temperature variability, while the results for the other two components are inconclusive. Moreover, it was found that temperature variability caused by the annual cycle is overestimated by the simulation of HadGEM2 GCM and CCLM4‐8‐17 RCM driven by HadGEM2, which explains the relatively poor results of these simulations regarding air temperature total variance and short‐term as well as seasonal components in the original temperature series. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components.
Although the observed total variability of daily air temperature and precipitation over selected European areas is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. In the figure, location of the eight studied areas situated in the vicinity of central Europe and the orography of the E‐OBS data set.
Sea surface temperature (SST) variability on decadal timescales has been associated with global and regional climate variability and impacts. The mechanisms that drive decadal SST variability, ...however, remain highly uncertain. Many previous studies have examined the role of atmospheric variability in driving decadal SST variations. Here we assess the strength of oceanic forcing in driving decadal SST variability in observations and state‐of‐the‐art climate models by analyzing the relationship between surface heat flux and SST. We find a largely similar pattern of decadal oceanic forcing across all ocean basins, characterized by oceanic forcing about twice the strength of the atmospheric forcing in the mid‐ and high latitude regions, but comparable or weaker than the atmospheric forcing in the subtropics. The decadal oceanic forcing is hypothesized to be associated with the wind‐driven oceanic circulation, which is common across all ocean basins.
Plain Language Summary
Decadal variabilities in SST create large climate responses, ranging from heat waves to droughts to enhanced hurricanes. However, there has been considerable uncertainty over whether decadal SST variations are driven primarily by atmospheric forcing or ocean forcing related to ocean circulation. Using a newly developed theoretical framework, we provide the first quantitative estimation of decadal oceanic forcing across the global ocean in observations and state‐of‐the‐art climate model. Our estimation shows that decadal ocean forcing is stronger than the atmospheric forcing by about 2–3 times in the mid‐ and high latitude, but comparable or even weaker than atmospheric forcing in the subtropics.
Key Points
In the mid‐ and high latitude, decadal oceanic forcing is stronger than atmospheric forcing by about 2–3 times across world ocean basins
In the subtropics, decadal oceanic forcing is comparable to or even weaker than atmospheric forcing
Decadal oceanic forcing is likely contributed predominantly by the wind‐driven oceanic circulation
The Sahel rainfall has a close teleconnection with North Atlantic sea surface temperature (NASST) variability, which has separately been shown to be affected by aerosols. Therefore, changes in ...regional aerosols emission could potentially drive multidecadal Sahel rainfall variability. Here we combine ensembles of state‐of‐the‐art global climate models (the CESM and CanESM large ensemble simulations and CMIP6 models) with observational data sets to demonstrate that anthropogenic aerosols have significantly impacted 20th‐century detrended Sahel rainfall multidecadal variability through modifying NASST. We show that aerosol‐induced multidecadal variations of downward solar radiative fluxes over the North Atlantic cause NASST variability during the 20th century, altering the ITCZ position and dynamically linking aerosol effects to Sahel rainfall variability. This process chain is caused by aerosol‐induced changes in radiative surface fluxes rather than changes in ocean circulations. CMIP6 models further suggest that aerosol‐cloud interactions modulate the inter‐model uncertainty of simulated NASST and potentially the Sahel rainfall variability.
Plain Language Summary
Sahel rainfall experienced significant multidecadal variability over the 20th century, with large societal implications. However, the drivers of this variability remain debated. Here we show that anthropogenic aerosols largely contributed to the Sahel rainfall variability. We propose a process chain, from changing sulfate emissions from Europe and North America, to changes in North Atlantic surface net radiative fluxes, via North Atlantic sea surface temperature variability to a shift of ITCZ and changes in West African monsoon, and finally Sahel rainfall variability. This process chain is consistently evidenced by ensembles of state‐of‐the‐art global climate models as well as observational data sets. We show that aerosol‐radiation interactions and aerosol‐cloud interactions are both important processes in this chain. These findings highlight the importance of accurate representation of regional aerosol‐cloud‐radiation interactions for the simulation of Sahel rainfall variability.
Key Points
A process chain from anthropogenic aerosol emissions to the Sahel rainfall is proposed
Large ensemble and CMIP6 simulations suggest that this link is mediated by aerosol impacts on North Atlantic SST
Both aerosol‐cloud interactions and aerosol‐radiation interactions are important in this chain process
We provide the first comprehensive analysis of the relationships between large‐scale patterns of Southern Hemisphere climate variability and the detailed structure of Antarctic precipitation. We ...examine linkages between the high spatial resolution precipitation from a regional atmospheric model and four patterns of large‐scale Southern Hemisphere climate variability: the southern baroclinic annular mode, the southern annular mode, and the two Pacific‐South American teleconnection patterns. Variations in all four patterns influence the spatial configuration of precipitation over Antarctica, consistent with their signatures in high‐latitude meridional moisture fluxes. They impact not only the mean but also the incidence of extreme precipitation events. Current coupled‐climate models are able to reproduce all four patterns of atmospheric variability but struggle to correctly replicate their regional impacts on Antarctic climate. Thus, linking these patterns directly to Antarctic precipitation variability may allow a better estimate of future changes in precipitation than using model output alone.
Key Points
The primary Southern Hemisphere extratropical circulation patterns all influence the spatial distribution of Antarctic precipitation
They impact not only the mean but also the incidence of extreme precipitation events
Locally, extreme precipitation may be associated especially with one polarity of a circulation pattern
Previous studies suggested that Atlantic Multidecadal Variability (AMV) modulations on pan‐Pacific sea surface temperature (SST) variability and prediction are model‐dependent. These results were ...mainly based on SST forcing experiments in which AMV‐related Atlantic SST anomalies were prescribed. However, the AMV itself is also model‐dependent, but its influences on the Pacific remain unclear. Here, we use multi‐model fully coupled experiments from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with observations, to study the model‐dependent AMV trans‐basin effects. We found that AMV strength is a key factor: Stronger (Weaker) model AMV than observations overestimates (underestimates) SST response and decadal prediction skills, mainly in the North Pacific. The reason is that stronger positive phased AMV, for example, leads to higher sea level pressure anomalies over the North Pacific, which lifts sea surface height and deepens thermocline to warm SST. Our study highlights the necessity to improve simulations of AMV strength.
Plain Language Summary
Pacific sea surface temperature (SST) decadal variability and prediction are important for economy and environment. The Atlantic Multidecadal Variability (AMV) was thought to significantly influence Pacific SST variability and prediction. However, the results of previous studies were model‐dependent. In this paper, we use fully coupled experiments of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to study what contributes to the model dependency. We found that the strength of the AMV is a key factor influencing AMV trans‐basin modulations on SST variability and decadal prediction, mainly in the North Pacific Ocean. The related mechanisms are discussed. Our paper sheds light on the way to improve North Pacific SST decadal prediction.
Key Points
Climate models show a strong inter‐model spread of Atlantic Multidecadal Variability (AMV) strength
Models with stronger (weaker) AMV than observations overestimate (underestimate) North Pacific sea surface temperature (SST) response and decadal prediction
The North Pacific SST response is primarily forced by AMV‐induced wind stress curl and ocean dynamics
All plant species require at least 16 elements for their growth and survival but the relative requirements and the variability at different organizational scales is not well understood.
We use a ...fertiliser experiment with six willow (Salix spp.) genotypes to evaluate a methodology based on Euclidian distances for stoichiometric analysis of the variability in leaf nutrient relations of twelve of those (C, N, P, K, Ca, Mg, Mn, S, Fe, Zn, B, Cu) plus Na and Al.
Differences in availability of the elements in the environment was the major driver of variation. Variability between leaves within a plant or between individuals of the same genotype growing in close proximity was as large as variability between genotypes.
Elements could be grouped by influence on growth: N, P, S and Mn concentrations follow each other and increase with growth rate; K, Ca and Mg uptake follow the increase in biomass; but uptake of Fe, B, Zn and Al seems to be limited. The position of Cu lies between the first two groups. Only for Na is there a difference in element concentrations between genotypes. The three groups of elements can be associated with different biochemical functions.
Abstract
In a recent paper, we argued that ocean dynamics increase the variability of midlatitude sea surface temperatures (SSTs) on monthly to interannual time scales, but act to damp ...lower-frequency SST variability over broad midlatitude regions. Here, we use two configurations of a simple stochastic climate model to provide new insights into this important aspect of climate variability. The simplest configuration includes the forcing and damping of SST variability by observed surface heat fluxes only, and the more complex configuration includes forcing and damping by ocean processes, which are estimated indirectly from monthly observations. It is found that the simple model driven only by the observed surface heat fluxes generally produces midlatitude SST power spectra that are too
red
compared to observations. Including ocean processes in the model reduces this discrepancy by
whitening
the midlatitude SST spectra. In particular, ocean processes generally increase the SST variance on <2-yr time scales and decrease it on >2-yr time scales. This happens because oceanic forcing increases the midlatitude SST variance across many time scales, but oceanic damping outweighs oceanic forcing on >2-yr time scales, particularly away from the western boundary currents. The whitening of midlatitude SST variability by ocean processes also operates in NCAR’s Community Earth System Model (CESM). That is, midlatitude SST spectra are generally redder when the same atmospheric model is coupled to a slab rather than dynamically active ocean model. Overall, the results suggest that forcing and damping by ocean processes play essential roles in driving midlatitude SST variability.
Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) ...variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100‐year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions.
Plain Language Summary
Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data.
Key Points
Several CMIP6 control simulations show more interdecadal global mean temperature variability than previous model generations
Even the most variable CMIP6 models never show century‐length global mean temperature trends that exceed observed warming trends
Unlike in control simulations, observed global mean temperature variability is coherent with variability in tropical convective regions
We compare the ability of coupled global climate models from the phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) in simulating the temperature and ...precipitation climatology and interannual variability over China for the period 1961\2-2005 and the climatological East Asian monsoon for the period 1979–2005. All 92 models are able to simulate the geographical distribution of the above variables reasonably well. Compared with earlier CMIP5 models, current CMIP6 models have nationally weaker cold biases, a similar nationwide overestimation of precipitation and a weaker underestimation of the southeast\3-northwest precipitation gradient, a comparable overestimation of the spatial variability of the interannual variability, and a similar underestimation of the strength of winter monsoon over northern Asia. Pairwise comparison indicates that models have improved from CMIP5 to CMIP6 for climatological temperature and precipitation and winter monsoon but display little improvement for the interannual temperature and precipitation variability and summer monsoon. The ability of models relates to their horizontal resolutions in certain aspects. Both the multi-model arithmetic mean and median display similar skills and outperform most of the individual models in all considered aspects.
In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find ...that an ensemble mean of runs from a single climate model a single model ensemble mean (SMEM) provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations the multimodel ensemble mean (MMEM). The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.
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